From 749ee861ede0eb4500d61924deef5466670b96e8 Mon Sep 17 00:00:00 2001 From: martaferreiro <125505098+martaferreiro@users.noreply.github.com> Date: Sat, 22 Apr 2023 21:33:04 +0200 Subject: [PATCH 1/3] Creado con Colaboratory --- Copia_de_Project2.ipynb | 13909 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 13909 insertions(+) create mode 100644 Copia_de_Project2.ipynb diff --git a/Copia_de_Project2.ipynb b/Copia_de_Project2.ipynb new file mode 100644 index 0000000..54bb7df --- /dev/null +++ b/Copia_de_Project2.ipynb @@ -0,0 +1,13909 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMWrXF4cKlSipGaTpYWYVcv", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 408, + "metadata": { + "id": "k8WL3zjmGtN0" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "source": [ + "fifa = pd.read_csv('fifa21_male2 copia.csv')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HQmSdpDmHC8g", + "outputId": "cf54de61-e473-4d8e-82a7-26cbea95a9bf" + }, + "execution_count": 409, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":1: DtypeWarning: Columns (78) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " fifa = pd.read_csv('fifa21_male2 copia.csv')\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "fifa.head()" + ], + "metadata": { + "id": "dUQzMNTlHT1a", + "outputId": "7ff5c463-09ad-40ee-8b3c-646661be9334", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 479 + } + }, + "execution_count": 410, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ID Name Age OVA Nationality Club BOV BP \\\n", + "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", + "1 16 Luis García 37 71 Spain KAS Eupen 70 CM \n", + "2 27 J. Cole 33 71 England Coventry City 71 CAM \n", + "3 36 D. Yorke 36 68 Trinidad & Tobago Sunderland 70 ST \n", + "4 41 Iniesta 36 81 Spain Vissel Kobe 82 CAM \n", + "\n", + " Position Player Photo \\\n", + "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", + "1 CM CAM CDM https://cdn.sofifa.com/players/000/016/19_120.png \n", + "2 CAM RM RW LM https://cdn.sofifa.com/players/000/027/16_120.png \n", + "3 NaN https://cdn.sofifa.com/players/000/036/09_120.png \n", + "4 CM CAM https://cdn.sofifa.com/players/000/041/20_120.png \n", + "\n", + " Club Logo \\\n", + "0 https://cdn.sofifa.com/teams/55/light_60.png \n", + "1 https://cdn.sofifa.com/teams/2013/light_60.png \n", + "2 https://cdn.sofifa.com/teams/1800/light_60.png \n", + "3 https://cdn.sofifa.com/teams/106/light_60.png \n", + "4 https://cdn.sofifa.com/teams/101146/light_60.png \n", + "\n", + " Flag Photo POT Team & Contract \\\n", + "0 https://cdn.sofifa.com/flags/it.png 69 Udinese 2008 ~ 2016 \n", + "1 https://cdn.sofifa.com/flags/es.png 71 KAS Eupen 2014 ~ 2019 \n", + "2 https://cdn.sofifa.com/flags/gb-eng.png 71 Coventry City 2016 ~ 2020 \n", + "3 https://cdn.sofifa.com/flags/tt.png 82 Sunderland 2009 \n", + "4 https://cdn.sofifa.com/flags/es.png 81 Vissel Kobe 2018 ~ 2021 \n", + "\n", + " Height Weight foot Growth Joined Loan Date End Value Wage \\\n", + "0 6'0\" 181lbs Left 0 Jul 1, 2008 NaN €625K €7K \n", + "1 5'10\" 143lbs Right 0 Jul 19, 2014 NaN €600K €7K \n", + "2 5'9\" 161lbs Right 0 Jan 7, 2016 NaN €1.1M €15K \n", + "3 5'11\" 165lbs Right 14 NaN NaN €0 €0 \n", + "4 5'7\" 150lbs Right 0 Jul 16, 2018 NaN €5.5M €12K \n", + "\n", + " Release Clause Contract Attacking Crossing Finishing \\\n", + "0 €0 2008 ~ 2016 313 75 50 \n", + "1 €1.1M 2014 ~ 2019 337 68 64 \n", + "2 €0 2016 ~ 2020 337 80 64 \n", + "3 €0 2009 264 54 70 \n", + "4 €7.2M 2018 ~ 2021 367 75 69 \n", + "\n", + " Heading Accuracy Short Passing Volleys Skill Dribbling Curve \\\n", + "0 59 71 58.0 338 73 65.0 \n", + "1 61 76 68.0 369 69 79.0 \n", + "2 41 77 75.0 387 79 84.0 \n", + "3 60 80 NaN 255 68 NaN \n", + "4 54 90 79.0 408 85 80.0 \n", + "\n", + " FK Accuracy Long Passing Ball Control Movement Acceleration \\\n", + "0 60 69 71 347 68 \n", + "1 79 71 71 305 56 \n", + "2 77 69 78 295 48 \n", + "3 46 64 77 176 59 \n", + "4 70 83 90 346 61 \n", + "\n", + " Sprint Speed Agility Reactions Balance Power Shot Power Jumping \\\n", + "0 74 68.0 69 68.0 347 74 68.0 \n", + "1 50 62.0 65 72.0 324 75 54.0 \n", + "2 42 71.0 59 75.0 284 72 58.0 \n", + "3 62 NaN 55 NaN 239 63 NaN \n", + "4 56 79.0 75 75.0 297 67 40.0 \n", + "\n", + " Stamina Strength Long Shots Mentality Aggression Interceptions \\\n", + "0 69 68 68 320 72 69.0 \n", + "1 64 60 71 362 71 71.0 \n", + "2 29 56 69 317 69 39.0 \n", + "3 51 66 59 271 59 70.0 \n", + "4 58 62 70 370 58 70.0 \n", + "\n", + " Positioning Vision Penalties Composure Defending Marking \\\n", + "0 63.0 66.0 50 NaN 208 70 \n", + "1 72.0 73.0 75 79.0 153 70 \n", + "2 69.0 74.0 66 NaN 99 35 \n", + "3 72.0 NaN 70 NaN 75 34 \n", + "4 78.0 93.0 71 89.0 181 68 \n", + "\n", + " Standing Tackle Sliding Tackle Goalkeeping GK Diving GK Handling \\\n", + "0 69 69.0 56 14 5 \n", + "1 43 40.0 56 9 12 \n", + "2 34 30.0 51 9 6 \n", + "3 41 NaN 68 5 21 \n", + "4 57 56.0 45 6 13 \n", + "\n", + " GK Kicking GK Positioning GK Reflexes Total Stats Base Stats W/F SM \\\n", + "0 15 10 12 1929 408 3 ★ 2★ \n", + "1 13 11 11 1906 385 4 ★ 3★ \n", + "2 13 16 7 1770 354 4 ★ 4★ \n", + "3 64 21 21 1348 369 3 ★ 1★ \n", + "4 6 13 7 2014 420 4 ★ 4★ \n", + "\n", + " A/W D/W IR PAC SHO PAS DRI DEF PHY Hits LS ST RS \\\n", + "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", + "1 Medium Medium 1 ★ 53 69 73 69 58 63 4 67+1 67+1 67+1 \n", + "2 Medium Low 2 ★ 45 68 76 77 36 52 11 64+0 64+0 64+0 \n", + "3 NaN NaN 1 ★ 61 66 66 69 47 60 3 67+0 67+0 67+0 \n", + "4 High Medium 4 ★ 58 70 85 85 63 59 149 72+3 72+3 72+3 \n", + "\n", + " LW LF CF RF RW LAM CAM RAM LM LCM CM RCM \\\n", + "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", + "1 67+0 68+0 68+0 68+0 67+0 70+1 70+1 70+1 68+1 70+1 70+1 70+1 \n", + "2 70+0 69+0 69+0 69+0 70+0 71+0 71+0 71+0 68+0 66+0 66+0 66+0 \n", + "3 66+0 67+0 67+0 67+0 66+0 70+0 70+0 70+0 66+0 68+0 68+0 68+0 \n", + "4 79+0 79+0 79+0 79+0 79+0 82+-1 82+-1 82+-1 79+2 81+0 81+0 81+0 \n", + "\n", + " RM LWB LDM CDM RDM RWB LB LCB CB RCB RB \\\n", + "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 70+-1 \n", + "1 68+1 62+1 66+1 66+1 66+1 62+1 60+1 60+1 60+1 60+1 60+1 \n", + "2 68+0 52+0 54+0 54+0 54+0 52+0 47+0 46+0 46+0 46+0 47+0 \n", + "3 66+0 56+0 65+0 65+0 65+0 56+0 57+0 51+0 51+0 51+0 57+0 \n", + "4 79+2 70+3 73+3 73+3 73+3 70+3 67+3 64+3 64+3 64+3 67+3 \n", + "\n", + " GK Gender \n", + "0 17+0 Male \n", + "1 17+1 Male \n", + "2 15+0 Male \n", + "3 22+0 Male \n", + "4 17+3 Male " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IDNameAgeOVANationalityClubBOVBPPositionPlayer PhotoClub LogoFlag PhotoPOTTeam & ContractHeightWeightfootGrowthJoinedLoan Date EndValueWageRelease ClauseContractAttackingCrossingFinishingHeading AccuracyShort PassingVolleysSkillDribblingCurveFK AccuracyLong PassingBall ControlMovementAccelerationSprint SpeedAgilityReactionsBalancePowerShot PowerJumpingStaminaStrengthLong ShotsMentalityAggressionInterceptionsPositioningVisionPenaltiesComposureDefendingMarkingStanding TackleSliding TackleGoalkeepingGK DivingGK HandlingGK KickingGK PositioningGK ReflexesTotal StatsBase StatsW/FSMA/WD/WIRPACSHOPASDRIDEFPHYHitsLSSTRSLWLFCFRFRWLAMCAMRAMLMLCMCMRCMRMLWBLDMCDMRDMRWBLBLCBCBRCBRBGKGender
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN€625K€7K€02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male
116Luis García3771SpainKAS Eupen70CMCM CAM CDMhttps://cdn.sofifa.com/players/000/016/19_120.pnghttps://cdn.sofifa.com/teams/2013/light_60.pnghttps://cdn.sofifa.com/flags/es.png71KAS Eupen 2014 ~ 20195'10\"143lbsRight0Jul 19, 2014NaN€600K€7K€1.1M2014 ~ 20193376864617668.03696979.0797171305565062.06572.03247554.06460713627171.072.073.07579.0153704340.05691213111119063854 ★3★MediumMedium1 ★536973695863467+167+167+167+068+068+068+067+070+170+170+168+170+170+170+168+162+166+166+166+162+160+160+160+160+160+117+1Male
227J. Cole3371EnglandCoventry City71CAMCAM RM RW LMhttps://cdn.sofifa.com/players/000/027/16_120.pnghttps://cdn.sofifa.com/teams/1800/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png71Coventry City 2016 ~ 20205'9\"161lbsRight0Jan 7, 2016NaN€1.1M€15K€02016 ~ 20203378064417775.03877984.0776978295484271.05975.02847258.02956693176939.069.074.066NaN99353430.051961316717703544 ★4★MediumLow2 ★4568767736521164+064+064+070+069+069+069+070+071+071+071+068+066+066+066+068+052+054+054+054+052+047+046+046+046+047+015+0Male
336D. Yorke3668Trinidad &amp; TobagoSunderland70STNaNhttps://cdn.sofifa.com/players/000/036/09_120.pnghttps://cdn.sofifa.com/teams/106/light_60.pnghttps://cdn.sofifa.com/flags/tt.png82Sunderland 20095'11\"165lbsRight14NaNNaN€0€0€0200926454706080NaN25568NaN4664771765962NaN55NaN23963NaN5166592715970.072.0NaN70NaN753441NaN6852164212113483693 ★1★NaNNaN1 ★616666694760367+067+067+066+067+067+067+066+070+070+070+066+068+068+068+066+056+065+065+065+056+057+051+051+051+057+022+0Male
441Iniesta3681SpainVissel Kobe82CAMCM CAMhttps://cdn.sofifa.com/players/000/041/20_120.pnghttps://cdn.sofifa.com/teams/101146/light_60.pnghttps://cdn.sofifa.com/flags/es.png81Vissel Kobe 2018 ~ 20215'7\"150lbsRight0Jul 16, 2018NaN€5.5M€12K€7.2M2018 ~ 20213677569549079.04088580.0708390346615679.07575.02976740.05862703705870.078.093.07189.0181685756.045613613720144204 ★4★HighMedium4 ★58708585635914972+372+372+379+079+079+079+079+082+-182+-182+-179+281+081+081+079+270+373+373+373+370+367+364+364+364+367+317+3Male
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 410 + } + ] + }, + { + "cell_type": "code", + "source": [ + "fifa.columns = fifa.columns.str.lower()\n", + "fifa.shape" + ], + "metadata": { + "id": "KRNUoNteHWOZ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3f6be6b7-a28e-4f3e-9ae3-b32164b86887" + }, + "execution_count": 411, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(17125, 107)" + ] + }, + "metadata": {}, + "execution_count": 411 + } + ] + }, + { + "cell_type": "code", + "source": [ + "fifa['value']=[i.replace('€','') for i in fifa['value']]\n", + "fifa['unit'] = fifa['value'].str[-1:]\n", + "fifa['number']=[i.replace('K','') for i in fifa['value']]\n", + "fifa['number']=[i.replace('M','') for i in fifa['number']]\n", + "fifa['number']=fifa['number'].astype(float)" + ], + "metadata": { + "id": "wgvuU_aFKzGd" + }, + "execution_count": 412, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fifa['unit'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hD7VYzcfOSjh", + "outputId": "c1877080-662d-4e90-d48e-bad5e6b59cbc" + }, + "execution_count": 413, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "K 9354\n", + "M 7314\n", + "0 457\n", + "Name: unit, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 413 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#multiply number column by unit\n", + "fifa['unit']=[i.replace('0', '1') for i in fifa['unit']]\n", + "fifa['unit']=[i.replace('K', '1000') for i in fifa['unit']]\n", + "fifa['unit']=[i.replace('M', '1000000') for i in fifa['unit']]\n", + "fifa['unit']=fifa['unit'].astype(int)" + ], + "metadata": { + "id": "gs82HUwbOWGb" + }, + "execution_count": 414, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fifa['market_value'] = (fifa['unit']*fifa['number']).astype(int)" + ], + "metadata": { + "id": "jxltKplFOYIb" + }, + "execution_count": 415, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fifa['release clause']=[i.replace('€','') for i in fifa['release clause']]\n", + "fifa['unit2'] = fifa['release clause'].str[-1:]\n", + "fifa['number2']=[i.replace('K','') for i in fifa['release clause']]\n", + "fifa['number2']=[i.replace('M','') for i in fifa['number2']]\n", + "fifa['number2']=fifa['number2'].astype(float)" + ], + "metadata": { + "id": "qPnXDzcqjLOB" + }, + "execution_count": 416, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fifa['unit2'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mxhZYr0LTZPt", + "outputId": "5ddf4906-7cd2-4f90-eb33-f788b1d249ac" + }, + "execution_count": 417, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "M 9883\n", + "K 5457\n", + "0 1785\n", + "Name: unit2, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 417 + } + ] + }, + { + "cell_type": "code", + "source": [ + "fifa['unit2']=[i.replace('0', '1') for i in fifa['unit2']]\n", + "fifa['unit2']=[i.replace('K', '1000') for i in fifa['unit2']]\n", + "fifa['unit2']=[i.replace('M', '1000000') for i in fifa['unit2']]\n", + "fifa['unit2']=fifa['unit2'].astype(int)\n" + ], + "metadata": { + "id": "87nI_kIVjQ9o" + }, + "execution_count": 418, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fifa['release clause_total'] = (fifa['unit2']*fifa['number2']).astype(int)" + ], + "metadata": { + "id": "ae7_PgwPjq7l" + }, + "execution_count": 419, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "display(fifa.shape)\n", + "fifa.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "5GhGa7YwOZDq", + "outputId": "d1703bfd-2e39-4e7f-96d1-709fd745ba23" + }, + "execution_count": 420, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "(17125, 113)" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id name age ova nationality club bov bp \\\n", + "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", + "1 16 Luis García 37 71 Spain KAS Eupen 70 CM \n", + "2 27 J. Cole 33 71 England Coventry City 71 CAM \n", + "3 36 D. Yorke 36 68 Trinidad & Tobago Sunderland 70 ST \n", + "4 41 Iniesta 36 81 Spain Vissel Kobe 82 CAM \n", + "\n", + " position player photo \\\n", + "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", + "1 CM CAM CDM https://cdn.sofifa.com/players/000/016/19_120.png \n", + "2 CAM RM RW LM https://cdn.sofifa.com/players/000/027/16_120.png \n", + "3 NaN https://cdn.sofifa.com/players/000/036/09_120.png \n", + "4 CM CAM https://cdn.sofifa.com/players/000/041/20_120.png \n", + "\n", + " club logo \\\n", + "0 https://cdn.sofifa.com/teams/55/light_60.png \n", + "1 https://cdn.sofifa.com/teams/2013/light_60.png \n", + "2 https://cdn.sofifa.com/teams/1800/light_60.png \n", + "3 https://cdn.sofifa.com/teams/106/light_60.png \n", + "4 https://cdn.sofifa.com/teams/101146/light_60.png \n", + "\n", + " flag photo pot team & contract \\\n", + "0 https://cdn.sofifa.com/flags/it.png 69 Udinese 2008 ~ 2016 \n", + "1 https://cdn.sofifa.com/flags/es.png 71 KAS Eupen 2014 ~ 2019 \n", + "2 https://cdn.sofifa.com/flags/gb-eng.png 71 Coventry City 2016 ~ 2020 \n", + "3 https://cdn.sofifa.com/flags/tt.png 82 Sunderland 2009 \n", + "4 https://cdn.sofifa.com/flags/es.png 81 Vissel Kobe 2018 ~ 2021 \n", + "\n", + " height weight foot growth joined loan date end value wage \\\n", + "0 6'0\" 181lbs Left 0 Jul 1, 2008 NaN 625K €7K \n", + "1 5'10\" 143lbs Right 0 Jul 19, 2014 NaN 600K €7K \n", + "2 5'9\" 161lbs Right 0 Jan 7, 2016 NaN 1.1M €15K \n", + "3 5'11\" 165lbs Right 14 NaN NaN 0 €0 \n", + "4 5'7\" 150lbs Right 0 Jul 16, 2018 NaN 5.5M €12K \n", + "\n", + " release clause contract attacking crossing finishing \\\n", + "0 0 2008 ~ 2016 313 75 50 \n", + "1 1.1M 2014 ~ 2019 337 68 64 \n", + "2 0 2016 ~ 2020 337 80 64 \n", + "3 0 2009 264 54 70 \n", + "4 7.2M 2018 ~ 2021 367 75 69 \n", + "\n", + " heading accuracy short passing volleys skill dribbling curve \\\n", + "0 59 71 58.0 338 73 65.0 \n", + "1 61 76 68.0 369 69 79.0 \n", + "2 41 77 75.0 387 79 84.0 \n", + "3 60 80 NaN 255 68 NaN \n", + "4 54 90 79.0 408 85 80.0 \n", + "\n", + " fk accuracy long passing ball control movement acceleration \\\n", + "0 60 69 71 347 68 \n", + "1 79 71 71 305 56 \n", + "2 77 69 78 295 48 \n", + "3 46 64 77 176 59 \n", + "4 70 83 90 346 61 \n", + "\n", + " sprint speed agility reactions balance power shot power jumping \\\n", + "0 74 68.0 69 68.0 347 74 68.0 \n", + "1 50 62.0 65 72.0 324 75 54.0 \n", + "2 42 71.0 59 75.0 284 72 58.0 \n", + "3 62 NaN 55 NaN 239 63 NaN \n", + "4 56 79.0 75 75.0 297 67 40.0 \n", + "\n", + " stamina strength long shots mentality aggression interceptions \\\n", + "0 69 68 68 320 72 69.0 \n", + "1 64 60 71 362 71 71.0 \n", + "2 29 56 69 317 69 39.0 \n", + "3 51 66 59 271 59 70.0 \n", + "4 58 62 70 370 58 70.0 \n", + "\n", + " positioning vision penalties composure defending marking \\\n", + "0 63.0 66.0 50 NaN 208 70 \n", + "1 72.0 73.0 75 79.0 153 70 \n", + "2 69.0 74.0 66 NaN 99 35 \n", + "3 72.0 NaN 70 NaN 75 34 \n", + "4 78.0 93.0 71 89.0 181 68 \n", + "\n", + " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", + "0 69 69.0 56 14 5 \n", + "1 43 40.0 56 9 12 \n", + "2 34 30.0 51 9 6 \n", + "3 41 NaN 68 5 21 \n", + "4 57 56.0 45 6 13 \n", + "\n", + " gk kicking gk positioning gk reflexes total stats base stats w/f sm \\\n", + "0 15 10 12 1929 408 3 ★ 2★ \n", + "1 13 11 11 1906 385 4 ★ 3★ \n", + "2 13 16 7 1770 354 4 ★ 4★ \n", + "3 64 21 21 1348 369 3 ★ 1★ \n", + "4 6 13 7 2014 420 4 ★ 4★ \n", + "\n", + " a/w d/w ir pac sho pas dri def phy hits ls st rs \\\n", + "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", + "1 Medium Medium 1 ★ 53 69 73 69 58 63 4 67+1 67+1 67+1 \n", + "2 Medium Low 2 ★ 45 68 76 77 36 52 11 64+0 64+0 64+0 \n", + "3 NaN NaN 1 ★ 61 66 66 69 47 60 3 67+0 67+0 67+0 \n", + "4 High Medium 4 ★ 58 70 85 85 63 59 149 72+3 72+3 72+3 \n", + "\n", + " lw lf cf rf rw lam cam ram lm lcm cm rcm \\\n", + "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", + "1 67+0 68+0 68+0 68+0 67+0 70+1 70+1 70+1 68+1 70+1 70+1 70+1 \n", + "2 70+0 69+0 69+0 69+0 70+0 71+0 71+0 71+0 68+0 66+0 66+0 66+0 \n", + "3 66+0 67+0 67+0 67+0 66+0 70+0 70+0 70+0 66+0 68+0 68+0 68+0 \n", + "4 79+0 79+0 79+0 79+0 79+0 82+-1 82+-1 82+-1 79+2 81+0 81+0 81+0 \n", + "\n", + " rm lwb ldm cdm rdm rwb lb lcb cb rcb rb \\\n", + "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 70+-1 \n", + "1 68+1 62+1 66+1 66+1 66+1 62+1 60+1 60+1 60+1 60+1 60+1 \n", + "2 68+0 52+0 54+0 54+0 54+0 52+0 47+0 46+0 46+0 46+0 47+0 \n", + "3 66+0 56+0 65+0 65+0 65+0 56+0 57+0 51+0 51+0 51+0 57+0 \n", + "4 79+2 70+3 73+3 73+3 73+3 70+3 67+3 64+3 64+3 64+3 67+3 \n", + "\n", + " gk gender unit number market_value unit2 number2 \\\n", + "0 17+0 Male 1000 625.0 625000 1 0.0 \n", + "1 17+1 Male 1000 600.0 600000 1000000 1.1 \n", + "2 15+0 Male 1000000 1.1 1100000 1 0.0 \n", + "3 22+0 Male 1 0.0 0 1 0.0 \n", + "4 17+3 Male 1000000 5.5 5500000 1000000 7.2 \n", + "\n", + " release clause_total \n", + "0 0 \n", + "1 1100000 \n", + "2 0 \n", + "3 0 \n", + "4 7200000 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_total
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00
116Luis García3771SpainKAS Eupen70CMCM CAM CDMhttps://cdn.sofifa.com/players/000/016/19_120.pnghttps://cdn.sofifa.com/teams/2013/light_60.pnghttps://cdn.sofifa.com/flags/es.png71KAS Eupen 2014 ~ 20195'10\"143lbsRight0Jul 19, 2014NaN600K€7K1.1M2014 ~ 20193376864617668.03696979.0797171305565062.06572.03247554.06460713627171.072.073.07579.0153704340.05691213111119063854 ★3★MediumMedium1 ★536973695863467+167+167+167+068+068+068+067+070+170+170+168+170+170+170+168+162+166+166+166+162+160+160+160+160+160+117+1Male1000600.060000010000001.11100000
227J. Cole3371EnglandCoventry City71CAMCAM RM RW LMhttps://cdn.sofifa.com/players/000/027/16_120.pnghttps://cdn.sofifa.com/teams/1800/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png71Coventry City 2016 ~ 20205'9\"161lbsRight0Jan 7, 2016NaN1.1M€15K02016 ~ 20203378064417775.03877984.0776978295484271.05975.02847258.02956693176939.069.074.066NaN99353430.051961316717703544 ★4★MediumLow2 ★4568767736521164+064+064+070+069+069+069+070+071+071+071+068+066+066+066+068+052+054+054+054+052+047+046+046+046+047+015+0Male10000001.1110000010.00
336D. Yorke3668Trinidad &amp; TobagoSunderland70STNaNhttps://cdn.sofifa.com/players/000/036/09_120.pnghttps://cdn.sofifa.com/teams/106/light_60.pnghttps://cdn.sofifa.com/flags/tt.png82Sunderland 20095'11\"165lbsRight14NaNNaN0€00200926454706080NaN25568NaN4664771765962NaN55NaN23963NaN5166592715970.072.0NaN70NaN753441NaN6852164212113483693 ★1★NaNNaN1 ★616666694760367+067+067+066+067+067+067+066+070+070+070+066+068+068+068+066+056+065+065+065+056+057+051+051+051+057+022+0Male10.0010.00
441Iniesta3681SpainVissel Kobe82CAMCM CAMhttps://cdn.sofifa.com/players/000/041/20_120.pnghttps://cdn.sofifa.com/teams/101146/light_60.pnghttps://cdn.sofifa.com/flags/es.png81Vissel Kobe 2018 ~ 20215'7\"150lbsRight0Jul 16, 2018NaN5.5M€12K7.2M2018 ~ 20213677569549079.04088580.0708390346615679.07575.02976740.05862703705870.078.093.07189.0181685756.045613613720144204 ★4★HighMedium4 ★58708585635914972+372+372+379+079+079+079+079+082+-182+-182+-179+281+081+081+079+270+373+373+373+370+367+364+364+364+367+317+3Male10000005.5550000010000007.27200000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 420 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#fifa.drop(columns=['value','unit','number','value2','unit2','number2'])" + ], + "metadata": { + "id": "4XpVXl7ROaiK" + }, + "execution_count": 421, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fifa['bp'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "miofeBLjO4md", + "outputId": "e0b9c6da-980c-4b80-c327-a5bdcca2a0d8" + }, + "execution_count": 422, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "CB 3252\n", + "ST 2660\n", + "CAM 2246\n", + "GK 1576\n", + "RM 1404\n", + "CDM 1246\n", + "CM 990\n", + "LB 921\n", + "RB 894\n", + "LM 805\n", + "RW 329\n", + "LWB 252\n", + "RWB 252\n", + "LW 209\n", + "CF 89\n", + "Name: bp, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 422 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Taking only the defenders\n", + "defenders = fifa[(fifa['bp']==('RB')) | (fifa['bp']==('LB')) | (fifa['bp']==('LWB')) | (fifa['bp']==('RWB')) | (fifa['bp']==('CB'))]\n", + "display(defenders.shape)\n", + "defenders.head()" + ], + "metadata": { + "id": "EKbuMPguQ4d2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 549 + }, + "outputId": "b1ab777b-f716-4123-a8fc-50836f530898" + }, + "execution_count": 423, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "(5571, 113)" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id name age ova nationality club bov bp \\\n", + "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", + "8 244 G. Neville B 35 76 England Manchester United 78 CB \n", + "10 249 P. Neville 35 74 England Everton 75 CB \n", + "13 388 S. Campbell 35 75 England Newcastle United 75 CB \n", + "15 496 D. Hamann 36 61 Germany Milton Keynes Dons 67 CB \n", + "\n", + " position player photo \\\n", + "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", + "8 RB CB RWB https://cdn.sofifa.com/players/000/244/11_120.png \n", + "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", + "13 NaN https://cdn.sofifa.com/players/000/388/11_120.png \n", + "15 NaN https://cdn.sofifa.com/players/000/496/11_120.png \n", + "\n", + " club logo \\\n", + "0 https://cdn.sofifa.com/teams/55/light_60.png \n", + "8 https://cdn.sofifa.com/teams/11/light_60.png \n", + "10 https://cdn.sofifa.com/teams/7/light_60.png \n", + "13 https://cdn.sofifa.com/teams/13/light_60.png \n", + "15 https://cdn.sofifa.com/teams/1798/light_60.png \n", + "\n", + " flag photo pot \\\n", + "0 https://cdn.sofifa.com/flags/it.png 69 \n", + "8 https://cdn.sofifa.com/flags/gb-eng.png 82 \n", + "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", + "13 https://cdn.sofifa.com/flags/gb-eng.png 79 \n", + "15 https://cdn.sofifa.com/flags/de.png 82 \n", + "\n", + " team & contract height weight foot growth joined \\\n", + "0 Udinese 2008 ~ 2016 6'0\" 181lbs Left 0 Jul 1, 2008 \n", + "8 Manchester United 1991 ~ 2011 5'10\" 174lbs Right 6 Jan 1, 1991 \n", + "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 Aug 1, 2005 \n", + "13 Newcastle United 2010 ~ 2011 6'2\" 190lbs Right 4 Sep 2, 2010 \n", + "15 Milton Keynes Dons 2010 ~ 2011 6'2\" 168lbs Right 21 Sep 2, 2010 \n", + "\n", + " loan date end value wage release clause contract attacking crossing \\\n", + "0 NaN 625K €7K 0 2008 ~ 2016 313 75 \n", + "8 NaN 0 €0 0 1991 ~ 2011 304 72 \n", + "10 NaN 120K €7K 0 2005 ~ 2013 315 73 \n", + "13 NaN 0 €0 0 2010 ~ 2011 206 20 \n", + "15 NaN 0 €0 0 2010 ~ 2011 270 51 \n", + "\n", + " finishing heading accuracy short passing volleys skill dribbling \\\n", + "0 50 59 71 58.0 338 73 \n", + "8 31 75 71 55.0 258 44 \n", + "10 36 69 74 63.0 283 53 \n", + "13 28 81 54 23.0 173 40 \n", + "15 35 62 76 46.0 288 37 \n", + "\n", + " curve fk accuracy long passing ball control movement acceleration \\\n", + "0 65.0 60 69 71 347 68 \n", + "8 56.0 33 61 64 324 64 \n", + "10 45.0 41 72 72 321 52 \n", + "13 19.0 15 44 55 321 61 \n", + "15 47.0 76 64 64 212 40 \n", + "\n", + " sprint speed agility reactions balance power shot power jumping \\\n", + "0 74 68.0 69 68.0 347 74 68.0 \n", + "8 70 51.0 72 67.0 284 47 70.0 \n", + "10 51 65.0 83 70.0 349 77 71.0 \n", + "13 68 44.0 68 80.0 319 57 85.0 \n", + "15 44 28.0 30 70.0 330 71 83.0 \n", + "\n", + " stamina strength long shots mentality aggression interceptions \\\n", + "0 69 68 68 320 72 69.0 \n", + "8 65 74 28 319 87 85.0 \n", + "10 61 76 64 335 78 83.0 \n", + "13 64 87 26 296 84 77.0 \n", + "15 28 82 66 344 74 75.0 \n", + "\n", + " positioning vision penalties composure defending marking \\\n", + "0 63.0 66.0 50 NaN 208 70 \n", + "8 45.0 70.0 32 NaN 242 78 \n", + "10 48.0 57.0 69 NaN 224 77 \n", + "13 41.0 57.0 37 NaN 222 72 \n", + "15 51.0 78.0 66 NaN 198 70 \n", + "\n", + " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", + "0 69 69.0 56 14 5 \n", + "8 81 83.0 43 12 9 \n", + "10 75 72.0 41 10 7 \n", + "13 77 73.0 44 11 7 \n", + "15 72 56.0 56 11 12 \n", + "\n", + " gk kicking gk positioning gk reflexes total stats base stats w/f sm \\\n", + "0 15 10 12 1929 408 3 ★ 2★ \n", + "8 5 6 11 1774 378 3 ★ 2★ \n", + "10 12 5 7 1868 381 4 ★ 2★ \n", + "13 12 5 9 1581 347 3 ★ 2★ \n", + "15 10 8 15 1698 343 3 ★ 2★ \n", + "\n", + " a/w d/w ir pac sho pas dri def phy hits ls st rs \\\n", + "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", + "8 NaN NaN 3 ★ 67 36 67 53 81 74 4 54+0 54+0 54+0 \n", + "10 Medium High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 59+0 \n", + "13 NaN NaN 3 ★ 65 34 43 48 76 81 3 51+0 51+0 51+0 \n", + "15 NaN NaN 3 ★ 42 51 68 46 69 67 3 51+0 51+0 51+0 \n", + "\n", + " lw lf cf rf rw lam cam ram lm lcm cm rcm \\\n", + "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", + "8 60+0 56+0 56+0 56+0 60+0 56+0 56+0 56+0 62+0 64+0 64+0 64+0 \n", + "10 63+0 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 68+0 \n", + "13 48+0 48+0 48+0 48+0 48+0 49+0 49+0 49+0 48+0 56+0 56+0 56+0 \n", + "15 50+0 51+0 51+0 51+0 50+0 59+0 59+0 59+0 53+0 61+0 61+0 61+0 \n", + "\n", + " rm lwb ldm cdm rdm rwb lb lcb cb rcb \\\n", + "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 \n", + "8 62+0 73+0 74+0 74+0 74+0 73+0 76+0 78+0 78+0 78+0 \n", + "10 65+0 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 \n", + "13 48+0 63+0 68+0 68+0 68+0 63+0 70+0 75+0 75+0 75+0 \n", + "15 53+0 58+0 65+0 65+0 65+0 58+0 62+0 67+0 67+0 67+0 \n", + "\n", + " rb gk gender unit number market_value unit2 number2 \\\n", + "0 70+-1 17+0 Male 1000 625.0 625000 1 0.0 \n", + "8 76+0 13+0 Male 1 0.0 0 1 0.0 \n", + "10 73+0 12+0 Male 1000 120.0 120000 1 0.0 \n", + "13 70+0 12+0 Male 1 0.0 0 1 0.0 \n", + "15 62+0 14+0 Male 1 0.0 0 1 0.0 \n", + "\n", + " release clause_total \n", + "0 0 \n", + "8 0 \n", + "10 0 \n", + "13 0 \n", + "15 0 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_total
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00
8244G. Neville B3576EnglandManchester United78CBRB CB RWBhttps://cdn.sofifa.com/players/000/244/11_120.pnghttps://cdn.sofifa.com/teams/11/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png82Manchester United 1991 ~ 20115'10\"174lbsRight6Jan 1, 1991NaN0€001991 ~ 20113047231757155.02584456.0336164324647051.07267.02844770.06574283198785.045.070.032NaN242788183.043129561117743783 ★2★NaNNaN3 ★673667538174454+054+054+060+056+056+056+060+056+056+056+062+064+064+064+062+073+074+074+074+073+076+078+078+078+076+013+0Male10.0010.00
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00
13388S. Campbell3575EnglandNewcastle United75CBNaNhttps://cdn.sofifa.com/players/000/388/11_120.pnghttps://cdn.sofifa.com/teams/13/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png79Newcastle United 2010 ~ 20116'2\"190lbsRight4Sep 2, 2010NaN0€002010 ~ 20112062028815423.01734019.0154455321616844.06880.03195785.06487262968477.041.057.037NaN222727773.044117125915813473 ★2★NaNNaN3 ★653443487681351+051+051+048+048+048+048+048+049+049+049+048+056+056+056+048+063+068+068+068+063+070+075+075+075+070+012+0Male10.0010.00
15496D. Hamann3661GermanyMilton Keynes Dons67CBNaNhttps://cdn.sofifa.com/players/000/496/11_120.pnghttps://cdn.sofifa.com/teams/1798/light_60.pnghttps://cdn.sofifa.com/flags/de.png82Milton Keynes Dons 2010 ~ 20116'2\"168lbsRight21Sep 2, 2010NaN0€002010 ~ 20112705135627646.02883747.0766464212404428.03070.03307183.02882663447475.051.078.066NaN198707256.05611121081516983433 ★2★NaNNaN3 ★425168466967351+051+051+050+051+051+051+050+059+059+059+053+061+061+061+053+058+065+065+065+058+062+067+067+067+062+014+0Male10.0010.00
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 423 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Create the column for the total value\n", + "defenders['total_price'] = defenders['market_value']+defenders['release clause_total']\n", + "display(defenders.shape)\n", + "defenders.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 658 + }, + "id": "Z5-wtZatgMcq", + "outputId": "15e3b8bc-7daf-480e-e5ad-840ad0f8cb13" + }, + "execution_count": 424, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders['total_price'] = defenders['market_value']+defenders['release clause_total']\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "(5571, 114)" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id name age ova nationality club bov bp \\\n", + "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", + "8 244 G. Neville B 35 76 England Manchester United 78 CB \n", + "10 249 P. Neville 35 74 England Everton 75 CB \n", + "13 388 S. Campbell 35 75 England Newcastle United 75 CB \n", + "15 496 D. Hamann 36 61 Germany Milton Keynes Dons 67 CB \n", + "\n", + " position player photo \\\n", + "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", + "8 RB CB RWB https://cdn.sofifa.com/players/000/244/11_120.png \n", + "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", + "13 NaN https://cdn.sofifa.com/players/000/388/11_120.png \n", + "15 NaN https://cdn.sofifa.com/players/000/496/11_120.png \n", + "\n", + " club logo \\\n", + "0 https://cdn.sofifa.com/teams/55/light_60.png \n", + "8 https://cdn.sofifa.com/teams/11/light_60.png \n", + "10 https://cdn.sofifa.com/teams/7/light_60.png \n", + "13 https://cdn.sofifa.com/teams/13/light_60.png \n", + "15 https://cdn.sofifa.com/teams/1798/light_60.png \n", + "\n", + " flag photo pot \\\n", + "0 https://cdn.sofifa.com/flags/it.png 69 \n", + "8 https://cdn.sofifa.com/flags/gb-eng.png 82 \n", + "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", + "13 https://cdn.sofifa.com/flags/gb-eng.png 79 \n", + "15 https://cdn.sofifa.com/flags/de.png 82 \n", + "\n", + " team & contract height weight foot growth joined \\\n", + "0 Udinese 2008 ~ 2016 6'0\" 181lbs Left 0 Jul 1, 2008 \n", + "8 Manchester United 1991 ~ 2011 5'10\" 174lbs Right 6 Jan 1, 1991 \n", + "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 Aug 1, 2005 \n", + "13 Newcastle United 2010 ~ 2011 6'2\" 190lbs Right 4 Sep 2, 2010 \n", + "15 Milton Keynes Dons 2010 ~ 2011 6'2\" 168lbs Right 21 Sep 2, 2010 \n", + "\n", + " loan date end value wage release clause contract attacking crossing \\\n", + "0 NaN 625K €7K 0 2008 ~ 2016 313 75 \n", + "8 NaN 0 €0 0 1991 ~ 2011 304 72 \n", + "10 NaN 120K €7K 0 2005 ~ 2013 315 73 \n", + "13 NaN 0 €0 0 2010 ~ 2011 206 20 \n", + "15 NaN 0 €0 0 2010 ~ 2011 270 51 \n", + "\n", + " finishing heading accuracy short passing volleys skill dribbling \\\n", + "0 50 59 71 58.0 338 73 \n", + "8 31 75 71 55.0 258 44 \n", + "10 36 69 74 63.0 283 53 \n", + "13 28 81 54 23.0 173 40 \n", + "15 35 62 76 46.0 288 37 \n", + "\n", + " curve fk accuracy long passing ball control movement acceleration \\\n", + "0 65.0 60 69 71 347 68 \n", + "8 56.0 33 61 64 324 64 \n", + "10 45.0 41 72 72 321 52 \n", + "13 19.0 15 44 55 321 61 \n", + "15 47.0 76 64 64 212 40 \n", + "\n", + " sprint speed agility reactions balance power shot power jumping \\\n", + "0 74 68.0 69 68.0 347 74 68.0 \n", + "8 70 51.0 72 67.0 284 47 70.0 \n", + "10 51 65.0 83 70.0 349 77 71.0 \n", + "13 68 44.0 68 80.0 319 57 85.0 \n", + "15 44 28.0 30 70.0 330 71 83.0 \n", + "\n", + " stamina strength long shots mentality aggression interceptions \\\n", + "0 69 68 68 320 72 69.0 \n", + "8 65 74 28 319 87 85.0 \n", + "10 61 76 64 335 78 83.0 \n", + "13 64 87 26 296 84 77.0 \n", + "15 28 82 66 344 74 75.0 \n", + "\n", + " positioning vision penalties composure defending marking \\\n", + "0 63.0 66.0 50 NaN 208 70 \n", + "8 45.0 70.0 32 NaN 242 78 \n", + "10 48.0 57.0 69 NaN 224 77 \n", + "13 41.0 57.0 37 NaN 222 72 \n", + "15 51.0 78.0 66 NaN 198 70 \n", + "\n", + " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", + "0 69 69.0 56 14 5 \n", + "8 81 83.0 43 12 9 \n", + "10 75 72.0 41 10 7 \n", + "13 77 73.0 44 11 7 \n", + "15 72 56.0 56 11 12 \n", + "\n", + " gk kicking gk positioning gk reflexes total stats base stats w/f sm \\\n", + "0 15 10 12 1929 408 3 ★ 2★ \n", + "8 5 6 11 1774 378 3 ★ 2★ \n", + "10 12 5 7 1868 381 4 ★ 2★ \n", + "13 12 5 9 1581 347 3 ★ 2★ \n", + "15 10 8 15 1698 343 3 ★ 2★ \n", + "\n", + " a/w d/w ir pac sho pas dri def phy hits ls st rs \\\n", + "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", + "8 NaN NaN 3 ★ 67 36 67 53 81 74 4 54+0 54+0 54+0 \n", + "10 Medium High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 59+0 \n", + "13 NaN NaN 3 ★ 65 34 43 48 76 81 3 51+0 51+0 51+0 \n", + "15 NaN NaN 3 ★ 42 51 68 46 69 67 3 51+0 51+0 51+0 \n", + "\n", + " lw lf cf rf rw lam cam ram lm lcm cm rcm \\\n", + "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", + "8 60+0 56+0 56+0 56+0 60+0 56+0 56+0 56+0 62+0 64+0 64+0 64+0 \n", + "10 63+0 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 68+0 \n", + "13 48+0 48+0 48+0 48+0 48+0 49+0 49+0 49+0 48+0 56+0 56+0 56+0 \n", + "15 50+0 51+0 51+0 51+0 50+0 59+0 59+0 59+0 53+0 61+0 61+0 61+0 \n", + "\n", + " rm lwb ldm cdm rdm rwb lb lcb cb rcb \\\n", + "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 \n", + "8 62+0 73+0 74+0 74+0 74+0 73+0 76+0 78+0 78+0 78+0 \n", + "10 65+0 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 \n", + "13 48+0 63+0 68+0 68+0 68+0 63+0 70+0 75+0 75+0 75+0 \n", + "15 53+0 58+0 65+0 65+0 65+0 58+0 62+0 67+0 67+0 67+0 \n", + "\n", + " rb gk gender unit number market_value unit2 number2 \\\n", + "0 70+-1 17+0 Male 1000 625.0 625000 1 0.0 \n", + "8 76+0 13+0 Male 1 0.0 0 1 0.0 \n", + "10 73+0 12+0 Male 1000 120.0 120000 1 0.0 \n", + "13 70+0 12+0 Male 1 0.0 0 1 0.0 \n", + "15 62+0 14+0 Male 1 0.0 0 1 0.0 \n", + "\n", + " release clause_total total_price \n", + "0 0 625000 \n", + "8 0 0 \n", + "10 0 120000 \n", + "13 0 0 \n", + "15 0 0 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_price
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00625000
8244G. Neville B3576EnglandManchester United78CBRB CB RWBhttps://cdn.sofifa.com/players/000/244/11_120.pnghttps://cdn.sofifa.com/teams/11/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png82Manchester United 1991 ~ 20115'10\"174lbsRight6Jan 1, 1991NaN0€001991 ~ 20113047231757155.02584456.0336164324647051.07267.02844770.06574283198785.045.070.032NaN242788183.043129561117743783 ★2★NaNNaN3 ★673667538174454+054+054+060+056+056+056+060+056+056+056+062+064+064+064+062+073+074+074+074+073+076+078+078+078+076+013+0Male10.0010.000
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000
13388S. Campbell3575EnglandNewcastle United75CBNaNhttps://cdn.sofifa.com/players/000/388/11_120.pnghttps://cdn.sofifa.com/teams/13/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png79Newcastle United 2010 ~ 20116'2\"190lbsRight4Sep 2, 2010NaN0€002010 ~ 20112062028815423.01734019.0154455321616844.06880.03195785.06487262968477.041.057.037NaN222727773.044117125915813473 ★2★NaNNaN3 ★653443487681351+051+051+048+048+048+048+048+049+049+049+048+056+056+056+048+063+068+068+068+063+070+075+075+075+070+012+0Male10.0010.000
15496D. Hamann3661GermanyMilton Keynes Dons67CBNaNhttps://cdn.sofifa.com/players/000/496/11_120.pnghttps://cdn.sofifa.com/teams/1798/light_60.pnghttps://cdn.sofifa.com/flags/de.png82Milton Keynes Dons 2010 ~ 20116'2\"168lbsRight21Sep 2, 2010NaN0€002010 ~ 20112705135627646.02883747.0766464212404428.03070.03307183.02882663447475.051.078.066NaN198707256.05611121081516983433 ★2★NaNNaN3 ★425168466967351+051+051+050+051+051+051+050+059+059+059+053+061+061+061+053+058+065+065+065+058+062+067+067+067+062+014+0Male10.0010.000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 424 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Filter the old players between 30 and 37, so they are not too old\n", + "defenders_filtered = defenders[(defenders['age'] >= 30) & (defenders['age'] <= 37)]\n", + "display(defenders_filtered.shape)\n", + "defenders_filtered.head()" + ], + "metadata": { + "id": "rKgELZ9zi8uT", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 549 + }, + "outputId": "37376b89-ebe2-44ef-99e5-41df2a0e1d36" + }, + "execution_count": 425, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "(1182, 114)" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id name age ova nationality club bov bp \\\n", + "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", + "8 244 G. Neville B 35 76 England Manchester United 78 CB \n", + "10 249 P. Neville 35 74 England Everton 75 CB \n", + "13 388 S. Campbell 35 75 England Newcastle United 75 CB \n", + "15 496 D. Hamann 36 61 Germany Milton Keynes Dons 67 CB \n", + "\n", + " position player photo \\\n", + "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", + "8 RB CB RWB https://cdn.sofifa.com/players/000/244/11_120.png \n", + "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", + "13 NaN https://cdn.sofifa.com/players/000/388/11_120.png \n", + "15 NaN https://cdn.sofifa.com/players/000/496/11_120.png \n", + "\n", + " club logo \\\n", + "0 https://cdn.sofifa.com/teams/55/light_60.png \n", + "8 https://cdn.sofifa.com/teams/11/light_60.png \n", + "10 https://cdn.sofifa.com/teams/7/light_60.png \n", + "13 https://cdn.sofifa.com/teams/13/light_60.png \n", + "15 https://cdn.sofifa.com/teams/1798/light_60.png \n", + "\n", + " flag photo pot \\\n", + "0 https://cdn.sofifa.com/flags/it.png 69 \n", + "8 https://cdn.sofifa.com/flags/gb-eng.png 82 \n", + "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", + "13 https://cdn.sofifa.com/flags/gb-eng.png 79 \n", + "15 https://cdn.sofifa.com/flags/de.png 82 \n", + "\n", + " team & contract height weight foot growth joined \\\n", + "0 Udinese 2008 ~ 2016 6'0\" 181lbs Left 0 Jul 1, 2008 \n", + "8 Manchester United 1991 ~ 2011 5'10\" 174lbs Right 6 Jan 1, 1991 \n", + "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 Aug 1, 2005 \n", + "13 Newcastle United 2010 ~ 2011 6'2\" 190lbs Right 4 Sep 2, 2010 \n", + "15 Milton Keynes Dons 2010 ~ 2011 6'2\" 168lbs Right 21 Sep 2, 2010 \n", + "\n", + " loan date end value wage release clause contract attacking crossing \\\n", + "0 NaN 625K €7K 0 2008 ~ 2016 313 75 \n", + "8 NaN 0 €0 0 1991 ~ 2011 304 72 \n", + "10 NaN 120K €7K 0 2005 ~ 2013 315 73 \n", + "13 NaN 0 €0 0 2010 ~ 2011 206 20 \n", + "15 NaN 0 €0 0 2010 ~ 2011 270 51 \n", + "\n", + " finishing heading accuracy short passing volleys skill dribbling \\\n", + "0 50 59 71 58.0 338 73 \n", + "8 31 75 71 55.0 258 44 \n", + "10 36 69 74 63.0 283 53 \n", + "13 28 81 54 23.0 173 40 \n", + "15 35 62 76 46.0 288 37 \n", + "\n", + " curve fk accuracy long passing ball control movement acceleration \\\n", + "0 65.0 60 69 71 347 68 \n", + "8 56.0 33 61 64 324 64 \n", + "10 45.0 41 72 72 321 52 \n", + "13 19.0 15 44 55 321 61 \n", + "15 47.0 76 64 64 212 40 \n", + "\n", + " sprint speed agility reactions balance power shot power jumping \\\n", + "0 74 68.0 69 68.0 347 74 68.0 \n", + "8 70 51.0 72 67.0 284 47 70.0 \n", + "10 51 65.0 83 70.0 349 77 71.0 \n", + "13 68 44.0 68 80.0 319 57 85.0 \n", + "15 44 28.0 30 70.0 330 71 83.0 \n", + "\n", + " stamina strength long shots mentality aggression interceptions \\\n", + "0 69 68 68 320 72 69.0 \n", + "8 65 74 28 319 87 85.0 \n", + "10 61 76 64 335 78 83.0 \n", + "13 64 87 26 296 84 77.0 \n", + "15 28 82 66 344 74 75.0 \n", + "\n", + " positioning vision penalties composure defending marking \\\n", + "0 63.0 66.0 50 NaN 208 70 \n", + "8 45.0 70.0 32 NaN 242 78 \n", + "10 48.0 57.0 69 NaN 224 77 \n", + "13 41.0 57.0 37 NaN 222 72 \n", + "15 51.0 78.0 66 NaN 198 70 \n", + "\n", + " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", + "0 69 69.0 56 14 5 \n", + "8 81 83.0 43 12 9 \n", + "10 75 72.0 41 10 7 \n", + "13 77 73.0 44 11 7 \n", + "15 72 56.0 56 11 12 \n", + "\n", + " gk kicking gk positioning gk reflexes total stats base stats w/f sm \\\n", + "0 15 10 12 1929 408 3 ★ 2★ \n", + "8 5 6 11 1774 378 3 ★ 2★ \n", + "10 12 5 7 1868 381 4 ★ 2★ \n", + "13 12 5 9 1581 347 3 ★ 2★ \n", + "15 10 8 15 1698 343 3 ★ 2★ \n", + "\n", + " a/w d/w ir pac sho pas dri def phy hits ls st rs \\\n", + "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", + "8 NaN NaN 3 ★ 67 36 67 53 81 74 4 54+0 54+0 54+0 \n", + "10 Medium High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 59+0 \n", + "13 NaN NaN 3 ★ 65 34 43 48 76 81 3 51+0 51+0 51+0 \n", + "15 NaN NaN 3 ★ 42 51 68 46 69 67 3 51+0 51+0 51+0 \n", + "\n", + " lw lf cf rf rw lam cam ram lm lcm cm rcm \\\n", + "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", + "8 60+0 56+0 56+0 56+0 60+0 56+0 56+0 56+0 62+0 64+0 64+0 64+0 \n", + "10 63+0 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 68+0 \n", + "13 48+0 48+0 48+0 48+0 48+0 49+0 49+0 49+0 48+0 56+0 56+0 56+0 \n", + "15 50+0 51+0 51+0 51+0 50+0 59+0 59+0 59+0 53+0 61+0 61+0 61+0 \n", + "\n", + " rm lwb ldm cdm rdm rwb lb lcb cb rcb \\\n", + "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 \n", + "8 62+0 73+0 74+0 74+0 74+0 73+0 76+0 78+0 78+0 78+0 \n", + "10 65+0 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 \n", + "13 48+0 63+0 68+0 68+0 68+0 63+0 70+0 75+0 75+0 75+0 \n", + "15 53+0 58+0 65+0 65+0 65+0 58+0 62+0 67+0 67+0 67+0 \n", + "\n", + " rb gk gender unit number market_value unit2 number2 \\\n", + "0 70+-1 17+0 Male 1000 625.0 625000 1 0.0 \n", + "8 76+0 13+0 Male 1 0.0 0 1 0.0 \n", + "10 73+0 12+0 Male 1000 120.0 120000 1 0.0 \n", + "13 70+0 12+0 Male 1 0.0 0 1 0.0 \n", + "15 62+0 14+0 Male 1 0.0 0 1 0.0 \n", + "\n", + " release clause_total total_price \n", + "0 0 625000 \n", + "8 0 0 \n", + "10 0 120000 \n", + "13 0 0 \n", + "15 0 0 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_price
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00625000
8244G. Neville B3576EnglandManchester United78CBRB CB RWBhttps://cdn.sofifa.com/players/000/244/11_120.pnghttps://cdn.sofifa.com/teams/11/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png82Manchester United 1991 ~ 20115'10\"174lbsRight6Jan 1, 1991NaN0€001991 ~ 20113047231757155.02584456.0336164324647051.07267.02844770.06574283198785.045.070.032NaN242788183.043129561117743783 ★2★NaNNaN3 ★673667538174454+054+054+060+056+056+056+060+056+056+056+062+064+064+064+062+073+074+074+074+073+076+078+078+078+076+013+0Male10.0010.000
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000
13388S. Campbell3575EnglandNewcastle United75CBNaNhttps://cdn.sofifa.com/players/000/388/11_120.pnghttps://cdn.sofifa.com/teams/13/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png79Newcastle United 2010 ~ 20116'2\"190lbsRight4Sep 2, 2010NaN0€002010 ~ 20112062028815423.01734019.0154455321616844.06880.03195785.06487262968477.041.057.037NaN222727773.044117125915813473 ★2★NaNNaN3 ★653443487681351+051+051+048+048+048+048+048+049+049+049+048+056+056+056+048+063+068+068+068+063+070+075+075+075+070+012+0Male10.0010.000
15496D. Hamann3661GermanyMilton Keynes Dons67CBNaNhttps://cdn.sofifa.com/players/000/496/11_120.pnghttps://cdn.sofifa.com/teams/1798/light_60.pnghttps://cdn.sofifa.com/flags/de.png82Milton Keynes Dons 2010 ~ 20116'2\"168lbsRight21Sep 2, 2010NaN0€002010 ~ 20112705135627646.02883747.0766464212404428.03070.03307183.02882663447475.051.078.066NaN198707256.05611121081516983433 ★2★NaNNaN3 ★425168466967351+051+051+050+051+051+051+050+059+059+059+053+061+061+061+053+058+065+065+065+058+062+067+067+067+062+014+0Male10.0010.000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 425 + } + ] + }, + { + "cell_type": "code", + "source": [ + "def ft_in_to_cm(height):\n", + " feet, inches = height.split(\"'\")\n", + " cm = int(feet) * 30.48 + int(inches[:-1]) * 2.54\n", + " return cm\n", + "\n", + "# apply the function to the 'Height' column and create a new 'Height_cm' column\n", + "defenders_filtered['height_cm'] = defenders_filtered['height'].apply(ft_in_to_cm)\n", + "defenders_filtered.drop(columns=['height'])\n", + "defenders_filtered.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "id": "-WHNF347hwOd", + "outputId": "26168b02-303d-4649-9180-87fa3b0c488a" + }, + "execution_count": 426, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered['height_cm'] = defenders_filtered['height'].apply(ft_in_to_cm)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id name age ova nationality club bov bp \\\n", + "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", + "8 244 G. Neville B 35 76 England Manchester United 78 CB \n", + "10 249 P. Neville 35 74 England Everton 75 CB \n", + "13 388 S. Campbell 35 75 England Newcastle United 75 CB \n", + "15 496 D. Hamann 36 61 Germany Milton Keynes Dons 67 CB \n", + "\n", + " position player photo \\\n", + "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", + "8 RB CB RWB https://cdn.sofifa.com/players/000/244/11_120.png \n", + "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", + "13 NaN https://cdn.sofifa.com/players/000/388/11_120.png \n", + "15 NaN https://cdn.sofifa.com/players/000/496/11_120.png \n", + "\n", + " club logo \\\n", + "0 https://cdn.sofifa.com/teams/55/light_60.png \n", + "8 https://cdn.sofifa.com/teams/11/light_60.png \n", + "10 https://cdn.sofifa.com/teams/7/light_60.png \n", + "13 https://cdn.sofifa.com/teams/13/light_60.png \n", + "15 https://cdn.sofifa.com/teams/1798/light_60.png \n", + "\n", + " flag photo pot \\\n", + "0 https://cdn.sofifa.com/flags/it.png 69 \n", + "8 https://cdn.sofifa.com/flags/gb-eng.png 82 \n", + "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", + "13 https://cdn.sofifa.com/flags/gb-eng.png 79 \n", + "15 https://cdn.sofifa.com/flags/de.png 82 \n", + "\n", + " team & contract height weight foot growth joined \\\n", + "0 Udinese 2008 ~ 2016 6'0\" 181lbs Left 0 Jul 1, 2008 \n", + "8 Manchester United 1991 ~ 2011 5'10\" 174lbs Right 6 Jan 1, 1991 \n", + "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 Aug 1, 2005 \n", + "13 Newcastle United 2010 ~ 2011 6'2\" 190lbs Right 4 Sep 2, 2010 \n", + "15 Milton Keynes Dons 2010 ~ 2011 6'2\" 168lbs Right 21 Sep 2, 2010 \n", + "\n", + " loan date end value wage release clause contract attacking crossing \\\n", + "0 NaN 625K €7K 0 2008 ~ 2016 313 75 \n", + "8 NaN 0 €0 0 1991 ~ 2011 304 72 \n", + "10 NaN 120K €7K 0 2005 ~ 2013 315 73 \n", + "13 NaN 0 €0 0 2010 ~ 2011 206 20 \n", + "15 NaN 0 €0 0 2010 ~ 2011 270 51 \n", + "\n", + " finishing heading accuracy short passing volleys skill dribbling \\\n", + "0 50 59 71 58.0 338 73 \n", + "8 31 75 71 55.0 258 44 \n", + "10 36 69 74 63.0 283 53 \n", + "13 28 81 54 23.0 173 40 \n", + "15 35 62 76 46.0 288 37 \n", + "\n", + " curve fk accuracy long passing ball control movement acceleration \\\n", + "0 65.0 60 69 71 347 68 \n", + "8 56.0 33 61 64 324 64 \n", + "10 45.0 41 72 72 321 52 \n", + "13 19.0 15 44 55 321 61 \n", + "15 47.0 76 64 64 212 40 \n", + "\n", + " sprint speed agility reactions balance power shot power jumping \\\n", + "0 74 68.0 69 68.0 347 74 68.0 \n", + "8 70 51.0 72 67.0 284 47 70.0 \n", + "10 51 65.0 83 70.0 349 77 71.0 \n", + "13 68 44.0 68 80.0 319 57 85.0 \n", + "15 44 28.0 30 70.0 330 71 83.0 \n", + "\n", + " stamina strength long shots mentality aggression interceptions \\\n", + "0 69 68 68 320 72 69.0 \n", + "8 65 74 28 319 87 85.0 \n", + "10 61 76 64 335 78 83.0 \n", + "13 64 87 26 296 84 77.0 \n", + "15 28 82 66 344 74 75.0 \n", + "\n", + " positioning vision penalties composure defending marking ... \\\n", + "0 63.0 66.0 50 NaN 208 70 ... \n", + "8 45.0 70.0 32 NaN 242 78 ... \n", + "10 48.0 57.0 69 NaN 224 77 ... \n", + "13 41.0 57.0 37 NaN 222 72 ... \n", + "15 51.0 78.0 66 NaN 198 70 ... \n", + "\n", + " sliding tackle goalkeeping gk diving gk handling gk kicking \\\n", + "0 69.0 56 14 5 15 \n", + "8 83.0 43 12 9 5 \n", + "10 72.0 41 10 7 12 \n", + "13 73.0 44 11 7 12 \n", + "15 56.0 56 11 12 10 \n", + "\n", + " gk positioning gk reflexes total stats base stats w/f sm a/w \\\n", + "0 10 12 1929 408 3 ★ 2★ Medium \n", + "8 6 11 1774 378 3 ★ 2★ NaN \n", + "10 5 7 1868 381 4 ★ 2★ Medium \n", + "13 5 9 1581 347 3 ★ 2★ NaN \n", + "15 8 15 1698 343 3 ★ 2★ NaN \n", + "\n", + " d/w ir pac sho pas dri def phy hits ls st rs lw \\\n", + "0 High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 68+0 \n", + "8 NaN 3 ★ 67 36 67 53 81 74 4 54+0 54+0 54+0 60+0 \n", + "10 High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 59+0 63+0 \n", + "13 NaN 3 ★ 65 34 43 48 76 81 3 51+0 51+0 51+0 48+0 \n", + "15 NaN 3 ★ 42 51 68 46 69 67 3 51+0 51+0 51+0 50+0 \n", + "\n", + " lf cf rf rw lam cam ram lm lcm cm rcm rm \\\n", + "0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 69+0 \n", + "8 56+0 56+0 56+0 60+0 56+0 56+0 56+0 62+0 64+0 64+0 64+0 62+0 \n", + "10 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 68+0 65+0 \n", + "13 48+0 48+0 48+0 48+0 49+0 49+0 49+0 48+0 56+0 56+0 56+0 48+0 \n", + "15 51+0 51+0 51+0 50+0 59+0 59+0 59+0 53+0 61+0 61+0 61+0 53+0 \n", + "\n", + " lwb ldm cdm rdm rwb lb lcb cb rcb rb \\\n", + "0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 70+-1 \n", + "8 73+0 74+0 74+0 74+0 73+0 76+0 78+0 78+0 78+0 76+0 \n", + "10 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 73+0 \n", + "13 63+0 68+0 68+0 68+0 63+0 70+0 75+0 75+0 75+0 70+0 \n", + "15 58+0 65+0 65+0 65+0 58+0 62+0 67+0 67+0 67+0 62+0 \n", + "\n", + " gk gender unit number market_value unit2 number2 \\\n", + "0 17+0 Male 1000 625.0 625000 1 0.0 \n", + "8 13+0 Male 1 0.0 0 1 0.0 \n", + "10 12+0 Male 1000 120.0 120000 1 0.0 \n", + "13 12+0 Male 1 0.0 0 1 0.0 \n", + "15 14+0 Male 1 0.0 0 1 0.0 \n", + "\n", + " release clause_total total_price height_cm \n", + "0 0 625000 182.88 \n", + "8 0 0 177.80 \n", + "10 0 120000 180.34 \n", + "13 0 0 187.96 \n", + "15 0 0 187.96 \n", + "\n", + "[5 rows x 115 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarking...sliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_priceheight_cm
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN20870...69.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00625000182.88
8244G. Neville B3576EnglandManchester United78CBRB CB RWBhttps://cdn.sofifa.com/players/000/244/11_120.pnghttps://cdn.sofifa.com/teams/11/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png82Manchester United 1991 ~ 20115'10\"174lbsRight6Jan 1, 1991NaN0€001991 ~ 20113047231757155.02584456.0336164324647051.07267.02844770.06574283198785.045.070.032NaN24278...83.043129561117743783 ★2★NaNNaN3 ★673667538174454+054+054+060+056+056+056+060+056+056+056+062+064+064+064+062+073+074+074+074+073+076+078+078+078+076+013+0Male10.0010.000177.80
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN22477...72.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000180.34
13388S. Campbell3575EnglandNewcastle United75CBNaNhttps://cdn.sofifa.com/players/000/388/11_120.pnghttps://cdn.sofifa.com/teams/13/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png79Newcastle United 2010 ~ 20116'2\"190lbsRight4Sep 2, 2010NaN0€002010 ~ 20112062028815423.01734019.0154455321616844.06880.03195785.06487262968477.041.057.037NaN22272...73.044117125915813473 ★2★NaNNaN3 ★653443487681351+051+051+048+048+048+048+048+049+049+049+048+056+056+056+048+063+068+068+068+063+070+075+075+075+070+012+0Male10.0010.000187.96
15496D. Hamann3661GermanyMilton Keynes Dons67CBNaNhttps://cdn.sofifa.com/players/000/496/11_120.pnghttps://cdn.sofifa.com/teams/1798/light_60.pnghttps://cdn.sofifa.com/flags/de.png82Milton Keynes Dons 2010 ~ 20116'2\"168lbsRight21Sep 2, 2010NaN0€002010 ~ 20112705135627646.02883747.0766464212404428.03070.03307183.02882663447475.051.078.066NaN19870...56.05611121081516983433 ★2★NaNNaN3 ★425168466967351+051+051+050+051+051+051+050+059+059+059+053+061+061+061+053+058+065+065+065+058+062+067+067+067+062+014+0Male10.0010.000187.96
\n", + "

5 rows × 115 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 426 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#list with the name of every column\n", + "pd.options.display.max_columns = len(defenders.columns)\n", + "list(defenders.columns)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LtKNlIfCfcJj", + "outputId": "82027135-9834-4d01-e3ff-e6eb22c46a5d" + }, + "execution_count": 427, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['id',\n", + " 'name',\n", + " 'age',\n", + " 'ova',\n", + " 'nationality',\n", + " 'club',\n", + " 'bov',\n", + " 'bp',\n", + " 'position',\n", + " 'player photo',\n", + " 'club logo',\n", + " 'flag photo',\n", + " 'pot',\n", + " 'team & contract',\n", + " 'height',\n", + " 'weight',\n", + " 'foot',\n", + " 'growth',\n", + " 'joined',\n", + " 'loan date end',\n", + " 'value',\n", + " 'wage',\n", + " 'release clause',\n", + " 'contract',\n", + " 'attacking',\n", + " 'crossing',\n", + " 'finishing',\n", + " 'heading accuracy',\n", + " 'short passing',\n", + " 'volleys',\n", + " 'skill',\n", + " 'dribbling',\n", + " 'curve',\n", + " 'fk accuracy',\n", + " 'long passing',\n", + " 'ball control',\n", + " 'movement',\n", + " 'acceleration',\n", + " 'sprint speed',\n", + " 'agility',\n", + " 'reactions',\n", + " 'balance',\n", + " 'power',\n", + " 'shot power',\n", + " 'jumping',\n", + " 'stamina',\n", + " 'strength',\n", + " 'long shots',\n", + " 'mentality',\n", + " 'aggression',\n", + " 'interceptions',\n", + " 'positioning',\n", + " 'vision',\n", + " 'penalties',\n", + " 'composure',\n", + " 'defending',\n", + " 'marking',\n", + " 'standing tackle',\n", + " 'sliding tackle',\n", + " 'goalkeeping',\n", + " 'gk diving',\n", + " 'gk handling',\n", + " 'gk kicking',\n", + " 'gk positioning',\n", + " 'gk reflexes',\n", + " 'total stats',\n", + " 'base stats',\n", + " 'w/f',\n", + " 'sm',\n", + " 'a/w',\n", + " 'd/w',\n", + " 'ir',\n", + " 'pac',\n", + " 'sho',\n", + " 'pas',\n", + " 'dri',\n", + " 'def',\n", + " 'phy',\n", + " 'hits',\n", + " 'ls',\n", + " 'st',\n", + " 'rs',\n", + " 'lw',\n", + " 'lf',\n", + " 'cf',\n", + " 'rf',\n", + " 'rw',\n", + " 'lam',\n", + " 'cam',\n", + " 'ram',\n", + " 'lm',\n", + " 'lcm',\n", + " 'cm',\n", + " 'rcm',\n", + " 'rm',\n", + " 'lwb',\n", + " 'ldm',\n", + " 'cdm',\n", + " 'rdm',\n", + " 'rwb',\n", + " 'lb',\n", + " 'lcb',\n", + " 'cb',\n", + " 'rcb',\n", + " 'rb',\n", + " 'gk',\n", + " 'gender',\n", + " 'unit',\n", + " 'number',\n", + " 'market_value',\n", + " 'unit2',\n", + " 'number2',\n", + " 'release clause_total',\n", + " 'total_price']" + ] + }, + "metadata": {}, + "execution_count": 427 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#defenders.drop(columns=['unit','number'])" + ], + "metadata": { + "id": "ia0UPlXph6S_" + }, + "execution_count": 428, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#The budget is 800.000€, so we have to sum the value of the player and the release clause.\n", + "defenders_filtered = defenders_filtered[(defenders_filtered['total_price'] < 800000)]\n", + "defenders_filtered = defenders_filtered[(defenders['wage'] != '€0')]\n", + "\n", + "defenders_filtered.shape" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pQiX1diUkX_M", + "outputId": "e6a33380-c5b1-4d72-aa3d-8f6af112e0ea" + }, + "execution_count": 429, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " defenders_filtered = defenders_filtered[(defenders['wage'] != '€0')]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(261, 115)" + ] + }, + "metadata": {}, + "execution_count": 429 + } + ] + }, + { + "cell_type": "code", + "source": [ + "defenders_filtered_stats = defenders_filtered[['name','age','nationality','height_cm','club','short passing','long passing','jumping','mentality','interceptions','defending', 'marking', 'standing tackle','sliding tackle','total stats','pas','def','phy','total_price']]\n", + "defenders_filtered_stats" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "K0ln1NzNmUuR", + "outputId": "abb0c6fc-114b-4772-a042-6770a3b537cb" + }, + "execution_count": 430, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age nationality height_cm club \\\n", + "0 G. Pasquale 33 Italy 182.88 Udinese \n", + "10 P. Neville 35 England 180.34 Everton \n", + "24 A. Nesta 37 Italy 187.96 Montreal Impact \n", + "53 J. O'Shea 37 Republic of Ireland 190.50 Reading \n", + "59 A. Radomski 34 Poland 177.80 Cracovia \n", + "... ... ... ... ... ... \n", + "15930 I. González 36 Uruguay 182.88 Zamora FC \n", + "15938 A. Șeroni 33 Romania 195.58 FC Botoşani \n", + "16221 J. Stöckner 31 Germany 187.96 SC Verl \n", + "16682 W. Burrell 30 England 177.80 Harrogate Town \n", + "16943 M. Sus 30 Czech Republic 180.34 Stal Mielec \n", + "\n", + " short passing long passing jumping mentality interceptions \\\n", + "0 71 69 68.0 320 69.0 \n", + "10 74 72 71.0 335 83.0 \n", + "24 65 62 74.0 277 76.0 \n", + "53 59 56 54.0 264 68.0 \n", + "59 61 70 59.0 335 78.0 \n", + "... ... ... ... ... ... \n", + "15930 53 33 67.0 233 59.0 \n", + "15938 45 35 76.0 253 57.0 \n", + "16221 42 35 71.0 206 53.0 \n", + "16682 50 46 75.0 245 55.0 \n", + "16943 58 52 61.0 255 58.0 \n", + "\n", + " defending marking standing tackle sliding tackle total stats pas \\\n", + "0 208 70 69 69.0 1929 70 \n", + "10 224 77 75 72.0 1868 67 \n", + "24 221 74 77 70.0 1548 54 \n", + "53 200 70 66 64.0 1463 55 \n", + "59 176 47 64 65.0 1698 59 \n", + "... ... ... ... ... ... ... \n", + "15930 180 56 66 58.0 1322 40 \n", + "15938 192 65 65 62.0 1444 39 \n", + "16221 182 60 64 58.0 1315 32 \n", + "16682 169 56 57 56.0 1481 47 \n", + "16943 171 56 57 58.0 1620 58 \n", + "\n", + " def phy total_price \n", + "0 68 69 625000 \n", + "10 76 72 120000 \n", + "24 75 62 0 \n", + "53 68 60 216000 \n", + "59 62 66 50000 \n", + "... ... ... ... \n", + "15930 60 60 95000 \n", + "15938 63 79 588000 \n", + "16221 60 74 466000 \n", + "16682 56 66 330000 \n", + "16943 56 56 564000 \n", + "\n", + "[261 rows x 19 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_price
0G. Pasquale33Italy182.88Udinese716968.032069.0208706969.01929706869625000
10P. Neville35England180.34Everton747271.033583.0224777572.01868677672120000
24A. Nesta37Italy187.96Montreal Impact656274.027776.0221747770.015485475620
53J. O'Shea37Republic of Ireland190.50Reading595654.026468.0200706664.01463556860216000
59A. Radomski34Poland177.80Cracovia617059.033578.0176476465.0169859626650000
............................................................
15930I. González36Uruguay182.88Zamora FC533367.023359.0180566658.0132240606095000
15938A. Șeroni33Romania195.58FC Botoşani453576.025357.0192656562.01444396379588000
16221J. Stöckner31Germany187.96SC Verl423571.020653.0182606458.01315326074466000
16682W. Burrell30England177.80Harrogate Town504675.024555.0169565756.01481475666330000
16943M. Sus30Czech Republic180.34Stal Mielec585261.025558.0171565758.01620585656564000
\n", + "

261 rows × 19 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 430 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#HACER AQUI LA NORMALIZACIÓN DE TODOS LOS DATOS NUMÉRICOS\n" + ], + "metadata": { + "id": "vVYVMjqwnAQY" + }, + "execution_count": 431, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "defenders_filtered_stats.dtypes\n", + "num_cols = defenders_filtered_stats.select_dtypes(include=['int64', 'float64']).columns.tolist()" + ], + "metadata": { + "id": "hCp4TefQnYH-" + }, + "execution_count": 432, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import sklearn\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "#from sklearn.preprocessing import StandardScaler" + ], + "metadata": { + "id": "fetDa8tYDAvo" + }, + "execution_count": 433, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for col in num_cols:\n", + " col_max = defenders_filtered_stats[col].max()\n", + " col_min = defenders_filtered_stats[col].min()\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + "defenders_filtered_stats.tail()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "Z6XuaqN7E0I6", + "outputId": "bd5ce47e-2590-4833-967f-153ed3d7dcad" + }, + "execution_count": 434, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", + ":4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age nationality height_cm club \\\n", + "15930 I. González 36 Uruguay 182.88 Zamora FC \n", + "15938 A. Șeroni 33 Romania 195.58 FC Botoşani \n", + "16221 J. Stöckner 31 Germany 187.96 SC Verl \n", + "16682 W. Burrell 30 England 177.80 Harrogate Town \n", + "16943 M. Sus 30 Czech Republic 180.34 Stal Mielec \n", + "\n", + " short passing long passing jumping mentality interceptions \\\n", + "15930 53 33 67.0 233 59.0 \n", + "15938 45 35 76.0 253 57.0 \n", + "16221 42 35 71.0 206 53.0 \n", + "16682 50 46 75.0 245 55.0 \n", + "16943 58 52 61.0 255 58.0 \n", + "\n", + " defending marking standing tackle sliding tackle total stats pas \\\n", + "15930 180 56 66 58.0 1322 40 \n", + "15938 192 65 65 62.0 1444 39 \n", + "16221 182 60 64 58.0 1315 32 \n", + "16682 169 56 57 56.0 1481 47 \n", + "16943 171 56 57 58.0 1620 58 \n", + "\n", + " def phy total_price age_n height_cm_n short passing_n \\\n", + "15930 60 60 95000 0.857143 0.538462 0.428571 \n", + "15938 63 79 588000 0.428571 0.923077 0.238095 \n", + "16221 60 74 466000 0.142857 0.692308 0.166667 \n", + "16682 56 66 330000 0.000000 0.384615 0.357143 \n", + "16943 56 56 564000 0.000000 0.461538 0.547619 \n", + "\n", + " long passing_n jumping_n mentality_n interceptions_n defending_n \\\n", + "15930 0.218182 0.600000 0.247368 0.325 0.329412 \n", + "15938 0.254545 0.738462 0.352632 0.275 0.470588 \n", + "16221 0.254545 0.661538 0.105263 0.175 0.352941 \n", + "16682 0.454545 0.723077 0.310526 0.225 0.200000 \n", + "16943 0.563636 0.507692 0.363158 0.300 0.223529 \n", + "\n", + " marking_n standing tackle_n sliding tackle_n total stats_n \\\n", + "15930 0.333333 0.46875 0.34375 0.157431 \n", + "15938 0.583333 0.43750 0.46875 0.311083 \n", + "16221 0.444444 0.40625 0.34375 0.148615 \n", + "16682 0.333333 0.18750 0.28125 0.357683 \n", + "16943 0.333333 0.18750 0.34375 0.532746 \n", + "\n", + " pas_n def_n phy_n total_price_n \n", + "15930 0.227273 0.344828 0.384615 0.119048 \n", + "15938 0.204545 0.448276 0.871795 0.736842 \n", + "16221 0.045455 0.344828 0.743590 0.583960 \n", + "16682 0.386364 0.206897 0.538462 0.413534 \n", + "16943 0.636364 0.206897 0.282051 0.706767 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_n
15930I. González36Uruguay182.88Zamora FC533367.023359.0180566658.01322406060950000.8571430.5384620.4285710.2181820.6000000.2473680.3250.3294120.3333330.468750.343750.1574310.2272730.3448280.3846150.119048
15938A. Șeroni33Romania195.58FC Botoşani453576.025357.0192656562.014443963795880000.4285710.9230770.2380950.2545450.7384620.3526320.2750.4705880.5833330.437500.468750.3110830.2045450.4482760.8717950.736842
16221J. Stöckner31Germany187.96SC Verl423571.020653.0182606458.013153260744660000.1428570.6923080.1666670.2545450.6615380.1052630.1750.3529410.4444440.406250.343750.1486150.0454550.3448280.7435900.583960
16682W. Burrell30England177.80Harrogate Town504675.024555.0169565756.014814756663300000.0000000.3846150.3571430.4545450.7230770.3105260.2250.2000000.3333330.187500.281250.3576830.3863640.2068970.5384620.413534
16943M. Sus30Czech Republic180.34Stal Mielec585261.025558.0171565758.016205856565640000.0000000.4615380.5476190.5636360.5076920.3631580.3000.2235290.3333330.187500.343750.5327460.6363640.2068970.2820510.706767
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 434 + } + ] + }, + { + "cell_type": "code", + "source": [ + "'''scaler = MinMaxScaler()\n", + "defenders_stats_norm = pd.DataFrame(scaler.fit_transform(defenders_filtered_stats[num_cols]),columns=num_cols)\n", + "defenders_stats_norm\n", + "'''" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "_0Wq1ZHwDSbx", + "outputId": "351266eb-ef29-40bd-aa0e-076b8adedbed" + }, + "execution_count": 435, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'scaler = MinMaxScaler()\\ndefenders_stats_norm = pd.DataFrame(scaler.fit_transform(defenders_filtered_stats[num_cols]),columns=num_cols)\\ndefenders_stats_norm\\n'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 435 + } + ] + }, + { + "cell_type": "code", + "source": [ + "defenders_filtered_stats['quality_value'] = (defenders_filtered_stats['short passing_n']+defenders_filtered_stats['long passing_n']+defenders_filtered_stats['jumping_n']+defenders_filtered_stats['interceptions_n']+defenders_filtered_stats['phy_n']).astype('float64')\n", + "display(defenders_filtered_stats.shape)\n", + "defenders_filtered_stats.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 484 + }, + "id": "_zN2TNwRiZjn", + "outputId": "0bf5cca6-54cc-4b66-e928-94704c13a670" + }, + "execution_count": 436, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats['quality_value'] = (defenders_filtered_stats['short passing_n']+defenders_filtered_stats['long passing_n']+defenders_filtered_stats['jumping_n']+defenders_filtered_stats['interceptions_n']+defenders_filtered_stats['phy_n']).astype('float64')\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "(261, 36)" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age nationality height_cm club \\\n", + "0 G. Pasquale 33 Italy 182.88 Udinese \n", + "10 P. Neville 35 England 180.34 Everton \n", + "24 A. Nesta 37 Italy 187.96 Montreal Impact \n", + "53 J. O'Shea 37 Republic of Ireland 190.50 Reading \n", + "59 A. Radomski 34 Poland 177.80 Cracovia \n", + "\n", + " short passing long passing jumping mentality interceptions defending \\\n", + "0 71 69 68.0 320 69.0 208 \n", + "10 74 72 71.0 335 83.0 224 \n", + "24 65 62 74.0 277 76.0 221 \n", + "53 59 56 54.0 264 68.0 200 \n", + "59 61 70 59.0 335 78.0 176 \n", + "\n", + " marking standing tackle sliding tackle total stats pas def phy \\\n", + "0 70 69 69.0 1929 70 68 69 \n", + "10 77 75 72.0 1868 67 76 72 \n", + "24 74 77 70.0 1548 54 75 62 \n", + "53 70 66 64.0 1463 55 68 60 \n", + "59 47 64 65.0 1698 59 62 66 \n", + "\n", + " total_price age_n height_cm_n short passing_n long passing_n \\\n", + "0 625000 0.428571 0.538462 0.857143 0.872727 \n", + "10 120000 0.714286 0.461538 0.928571 0.927273 \n", + "24 0 1.000000 0.692308 0.714286 0.745455 \n", + "53 216000 1.000000 0.769231 0.571429 0.636364 \n", + "59 50000 0.571429 0.384615 0.619048 0.890909 \n", + "\n", + " jumping_n mentality_n interceptions_n defending_n marking_n \\\n", + "0 0.615385 0.705263 0.575 0.658824 0.722222 \n", + "10 0.661538 0.784211 0.925 0.847059 0.916667 \n", + "24 0.707692 0.478947 0.750 0.811765 0.833333 \n", + "53 0.400000 0.410526 0.550 0.564706 0.722222 \n", + "59 0.476923 0.784211 0.800 0.282353 0.083333 \n", + "\n", + " standing tackle_n sliding tackle_n total stats_n pas_n def_n \\\n", + "0 0.56250 0.68750 0.921914 0.909091 0.620690 \n", + "10 0.75000 0.78125 0.845088 0.840909 0.896552 \n", + "24 0.81250 0.71875 0.442065 0.545455 0.862069 \n", + "53 0.46875 0.53125 0.335013 0.568182 0.620690 \n", + "59 0.40625 0.56250 0.630982 0.659091 0.413793 \n", + "\n", + " phy_n total_price_n quality_value \n", + "0 0.615385 0.783208 3.535639 \n", + "10 0.692308 0.150376 4.134690 \n", + "24 0.435897 0.000000 3.353330 \n", + "53 0.384615 0.270677 2.542408 \n", + "59 0.538462 0.062657 3.325341 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_nquality_value
0G. Pasquale33Italy182.88Udinese716968.032069.0208706969.019297068696250000.4285710.5384620.8571430.8727270.6153850.7052630.5750.6588240.7222220.562500.687500.9219140.9090910.6206900.6153850.7832083.535639
10P. Neville35England180.34Everton747271.033583.0224777572.018686776721200000.7142860.4615380.9285710.9272730.6615380.7842110.9250.8470590.9166670.750000.781250.8450880.8409090.8965520.6923080.1503764.134690
24A. Nesta37Italy187.96Montreal Impact656274.027776.0221747770.0154854756201.0000000.6923080.7142860.7454550.7076920.4789470.7500.8117650.8333330.812500.718750.4420650.5454550.8620690.4358970.0000003.353330
53J. O'Shea37Republic of Ireland190.50Reading595654.026468.0200706664.014635568602160001.0000000.7692310.5714290.6363640.4000000.4105260.5500.5647060.7222220.468750.531250.3350130.5681820.6206900.3846150.2706772.542408
59A. Radomski34Poland177.80Cracovia617059.033578.0176476465.01698596266500000.5714290.3846150.6190480.8909090.4769230.7842110.8000.2823530.0833330.406250.562500.6309820.6590910.4137930.5384620.0626573.325341
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 436 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Select only the players above the 70 percentil of the quality value\n", + "bins = pd.qcut(defenders_filtered_stats['quality_value'],(0,0.8,1),labels=['lower','higher'])\n", + "defenders_filtered_stats['rank'] = bins\n", + "defenders_filtered_stats = defenders_filtered_stats[defenders_filtered_stats['rank']=='higher']\n", + "display(defenders_filtered_stats.shape)\n", + "defenders_filtered_stats" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "0OX9dZ5kYG0F", + "outputId": "dc5a5a27-df28-490c-e5af-344a380f9f81" + }, + "execution_count": 437, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats['rank'] = bins\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "(52, 37)" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age nationality height_cm \\\n", + "0 G. Pasquale 33 Italy 182.88 \n", + "10 P. Neville 35 England 180.34 \n", + "24 A. Nesta 37 Italy 187.96 \n", + "92 G. Heinze 35 Argentina 177.80 \n", + "151 W. Samuel 37 Argentina 182.88 \n", + "156 O. Mellberg 35 Sweden 187.96 \n", + "161 S. Distin 37 France 193.04 \n", + "182 S. Diawara 35 Senegal 187.96 \n", + "210 Gilberto Silva 36 Brazil 182.88 \n", + "235 S. Cherundolo 34 United States 172.72 \n", + "257 M. Ambrosini 36 Italy 182.88 \n", + "294 L. Cufré 36 Argentina 175.26 \n", + "309 Lúcio 35 Brazil 187.96 \n", + "321 Borja Fernández 37 Spain 187.96 \n", + "327 S. Riether 35 Germany 175.26 \n", + "354 K. Touré 35 Ivory Coast 177.80 \n", + "362 C. Schulz 34 Germany 185.42 \n", + "363 A. Tymoshchuk 35 Ukraine 180.34 \n", + "393 Junior Cesar 31 Brazil 165.10 \n", + "399 Cris 36 Brazil 182.88 \n", + "404 P. García 36 Uruguay 185.42 \n", + "424 M. Gobbi 37 Italy 182.88 \n", + "425 M. Cassani 33 Italy 182.88 \n", + "463 C. Rodríguez 36 Argentina 167.64 \n", + "496 D. Verón 36 Paraguay 180.34 \n", + "516 J. Thomsen 37 Denmark 180.34 \n", + "669 Àngel Rangel 36 Spain 177.80 \n", + "680 L. Perea 35 Colombia 180.34 \n", + "724 D. Pratley 35 England 185.42 \n", + "726 G. Sardo 37 Italy 190.50 \n", + "803 D. Pérez 34 Uruguay 177.80 \n", + "861 Evaldo 33 Brazil 182.88 \n", + "925 D. Moor 36 United States 182.88 \n", + "1004 J. Polák 34 Czech Republic 180.34 \n", + "1170 Bolaño 34 Spain 182.88 \n", + "1414 V. Elm 34 Sweden 190.50 \n", + "1465 A. Raineau 34 France 177.80 \n", + "1653 N. Topor-Stanley 35 Australia 190.50 \n", + "1664 L. Broxham 32 Australia 170.18 \n", + "1782 B. Sigmund 34 New Zealand 187.96 \n", + "1984 A. Collin 34 France 187.96 \n", + "2298 A. Meijers 32 Netherlands 175.26 \n", + "2467 Lombán 33 Spain 185.42 \n", + "2531 R. Austin 34 Jamaica 182.88 \n", + "2711 M. Inoha 34 Japan 177.80 \n", + "2881 H. Mulder 33 Netherlands 180.34 \n", + "3084 T. Makino 33 Japan 182.88 \n", + "3374 Carlos Ruiz 36 Spain 182.88 \n", + "4267 D. Kempe 34 Germany 187.96 \n", + "7770 M. Čovilo 34 Bosnia Herzegovina 193.04 \n", + "15408 C. Caraza 34 Peru 175.26 \n", + "15426 V. Balta 34 Peru 180.34 \n", + "\n", + " club short passing long passing jumping \\\n", + "0 Udinese 71 69 68.0 \n", + "10 Everton 74 72 71.0 \n", + "24 Montreal Impact 65 62 74.0 \n", + "92 Newell's Old Boys 60 66 76.0 \n", + "151 FC Basel 1893 66 67 75.0 \n", + "156 FC København 63 67 66.0 \n", + "161 Bournemouth 66 64 63.0 \n", + "182 OGC Nice 63 59 72.0 \n", + "210 Atlético Mineiro 68 63 51.0 \n", + "235 Hannover 96 75 71 81.0 \n", + "257 Fiorentina 76 76 92.0 \n", + "294 Leones Negros de la UdeG 63 61 82.0 \n", + "309 Palmeiras 65 65 80.0 \n", + "321 Real Valladolid CF 66 64 70.0 \n", + "327 FC Schalke 04 74 65 71.0 \n", + "354 Celtic 70 65 72.0 \n", + "362 SK Sturm Graz 67 65 72.0 \n", + "363 Zenit St. Petersburg 72 70 76.0 \n", + "393 Botafogo 71 66 82.0 \n", + "399 Vasco da Gama 72 70 73.0 \n", + "404 PAOK 77 76 65.0 \n", + "424 Parma 71 71 67.0 \n", + "425 Bari 70 67 70.0 \n", + "463 Club Atlético Colón 67 62 87.0 \n", + "496 Paraguay 63 64 80.0 \n", + "516 Randers FC 65 62 81.0 \n", + "669 Queens Park Rangers 68 67 75.0 \n", + "680 Cruz Azul 59 52 90.0 \n", + "724 Charlton Athletic 64 61 87.0 \n", + "726 Chievo Verona 60 60 78.0 \n", + "803 Bologna 65 66 74.0 \n", + "861 Moreirense FC 67 64 76.0 \n", + "925 Colorado Rapids 68 68 71.0 \n", + "1004 1. FC Nürnberg 68 63 68.0 \n", + "1170 Real Oviedo 62 61 75.0 \n", + "1414 Kalmar FF 66 63 81.0 \n", + "1465 La Berrichonne de Châteauroux 66 62 80.0 \n", + "1653 Newcastle Jets 58 60 82.0 \n", + "1664 Melbourne Victory 61 59 93.0 \n", + "1782 Wellington Phoenix 58 55 92.0 \n", + "1984 Philadelphia Union 59 63 82.0 \n", + "2298 Sparta Rotterdam 65 67 79.0 \n", + "2467 Málaga CF 63 69 71.0 \n", + "2531 Esbjerg fB 64 62 80.0 \n", + "2711 Yokohama FC 61 65 92.0 \n", + "2881 RKC Waalwijk 66 63 73.0 \n", + "3084 Urawa Red Diamonds 66 62 78.0 \n", + "3374 CD Tenerife 67 61 83.0 \n", + "4267 SV Wehen Wiesbaden 62 58 76.0 \n", + "7770 FC Lugano 63 62 86.0 \n", + "15408 Sport Huancayo 68 72 72.0 \n", + "15426 Sport Huancayo 60 57 85.0 \n", + "\n", + " mentality interceptions defending marking standing tackle \\\n", + "0 320 69.0 208 70 69 \n", + "10 335 83.0 224 77 75 \n", + "24 277 76.0 221 74 77 \n", + "92 305 76.0 227 76 76 \n", + "151 293 86.0 220 74 74 \n", + "156 302 78.0 215 75 77 \n", + "161 304 75.0 237 80 79 \n", + "182 269 71.0 214 71 71 \n", + "210 330 79.0 229 76 79 \n", + "235 324 73.0 224 74 73 \n", + "257 376 82.0 226 70 78 \n", + "294 284 65.0 206 69 69 \n", + "309 340 75.0 227 75 79 \n", + "321 337 70.0 206 70 74 \n", + "327 310 72.0 220 78 73 \n", + "354 315 72.0 223 72 75 \n", + "362 287 66.0 196 65 67 \n", + "363 359 80.0 237 77 81 \n", + "393 338 72.0 210 68 66 \n", + "399 320 69.0 201 68 68 \n", + "404 345 75.0 214 70 73 \n", + "424 313 75.0 222 65 78 \n", + "425 305 71.0 203 63 68 \n", + "463 311 67.0 199 60 70 \n", + "496 306 72.0 215 72 74 \n", + "516 289 68.0 196 64 65 \n", + "669 334 72.0 215 69 74 \n", + "680 284 74.0 227 75 76 \n", + "724 338 71.0 197 71 62 \n", + "726 303 71.0 226 75 78 \n", + "803 346 72.0 208 65 72 \n", + "861 307 66.0 215 68 75 \n", + "925 273 66.0 199 68 66 \n", + "1004 304 73.0 197 65 68 \n", + "1170 305 67.0 199 66 67 \n", + "1414 311 66.0 191 68 64 \n", + "1465 303 64.0 200 68 65 \n", + "1653 245 60.0 178 61 60 \n", + "1664 303 61.0 194 61 66 \n", + "1782 234 60.0 183 62 61 \n", + "1984 287 64.0 181 61 60 \n", + "2298 325 64.0 193 63 65 \n", + "2467 290 68.0 197 68 65 \n", + "2531 300 60.0 186 65 64 \n", + "2711 271 66.0 184 60 63 \n", + "2881 313 68.0 187 60 64 \n", + "3084 289 61.0 193 61 65 \n", + "3374 286 63.0 187 64 63 \n", + "4267 296 65.0 196 61 66 \n", + "7770 294 69.0 192 58 69 \n", + "15408 315 64.0 192 65 65 \n", + "15426 280 67.0 184 61 63 \n", + "\n", + " sliding tackle total stats pas def phy total_price age_n \\\n", + "0 69.0 1929 70 68 69 625000 0.428571 \n", + "10 72.0 1868 67 76 72 120000 0.714286 \n", + "24 70.0 1548 54 75 62 0 1.000000 \n", + "92 75.0 1784 63 76 68 300000 0.714286 \n", + "151 72.0 1560 54 76 66 0 1.000000 \n", + "156 63.0 1634 55 76 75 300000 0.714286 \n", + "161 78.0 1679 56 79 78 0 1.000000 \n", + "182 72.0 1606 55 71 75 400000 0.714286 \n", + "210 74.0 1695 61 77 69 0 0.857143 \n", + "235 77.0 1918 69 73 68 400000 0.571429 \n", + "257 78.0 1957 71 77 73 0 0.857143 \n", + "294 68.0 1612 56 69 73 0 0.857143 \n", + "309 73.0 1807 58 77 75 450000 0.714286 \n", + "321 62.0 1758 63 70 75 378000 1.000000 \n", + "327 69.0 1815 66 73 61 621000 0.714286 \n", + "354 76.0 1816 60 74 79 700000 0.714286 \n", + "362 64.0 1659 60 66 70 667000 0.571429 \n", + "363 79.0 1965 68 78 72 375000 0.714286 \n", + "393 76.0 1991 66 68 62 575000 0.142857 \n", + "399 65.0 1664 63 69 71 0 0.857143 \n", + "404 71.0 1900 74 72 78 0 0.857143 \n", + "424 79.0 1867 68 73 65 596000 1.000000 \n", + "425 72.0 1806 64 67 69 710000 0.428571 \n", + "463 69.0 1864 65 64 71 250000 0.857143 \n", + "496 69.0 1771 56 73 73 0 0.857143 \n", + "516 67.0 1729 63 64 68 225000 1.000000 \n", + "669 72.0 1853 69 72 67 464000 0.857143 \n", + "680 76.0 1724 51 75 80 550000 0.714286 \n", + "724 64.0 1880 62 67 78 633000 0.714286 \n", + "726 73.0 1807 60 75 75 0 1.000000 \n", + "803 71.0 1845 62 70 81 400000 0.571429 \n", + "861 72.0 1839 64 70 70 375000 0.428571 \n", + "925 65.0 1614 59 67 69 368000 0.857143 \n", + "1004 64.0 1735 61 67 69 220000 0.571429 \n", + "1170 66.0 1806 59 67 73 715000 0.571429 \n", + "1414 59.0 1743 61 66 77 731000 0.571429 \n", + "1465 67.0 1765 63 65 71 523000 0.571429 \n", + "1653 57.0 1507 53 60 84 315000 0.714286 \n", + "1664 67.0 1748 55 63 84 788000 0.285714 \n", + "1782 60.0 1416 44 61 82 130000 0.571429 \n", + "1984 60.0 1560 52 62 75 600000 0.571429 \n", + "2298 65.0 1915 67 64 72 550000 0.285714 \n", + "2467 64.0 1696 59 67 69 780000 0.428571 \n", + "2531 57.0 1685 59 63 80 731000 0.571429 \n", + "2711 61.0 1633 55 62 73 383000 0.571429 \n", + "2881 63.0 1742 62 63 75 735000 0.428571 \n", + "3084 67.0 1727 60 64 76 788000 0.428571 \n", + "3374 60.0 1752 57 64 77 390000 0.857143 \n", + "4267 69.0 1730 58 64 77 674000 0.571429 \n", + "7770 65.0 1636 58 65 76 515000 0.571429 \n", + "15408 62.0 1852 69 62 70 725000 0.571429 \n", + "15426 60.0 1652 52 63 75 630000 0.571429 \n", + "\n", + " height_cm_n short passing_n long passing_n jumping_n mentality_n \\\n", + "0 0.538462 0.857143 0.872727 0.615385 0.705263 \n", + "10 0.461538 0.928571 0.927273 0.661538 0.784211 \n", + "24 0.692308 0.714286 0.745455 0.707692 0.478947 \n", + "92 0.384615 0.595238 0.818182 0.738462 0.626316 \n", + "151 0.538462 0.738095 0.836364 0.723077 0.563158 \n", + "156 0.692308 0.666667 0.836364 0.584615 0.610526 \n", + "161 0.846154 0.738095 0.781818 0.538462 0.621053 \n", + "182 0.692308 0.666667 0.690909 0.676923 0.436842 \n", + "210 0.538462 0.785714 0.763636 0.353846 0.757895 \n", + "235 0.230769 0.952381 0.909091 0.815385 0.726316 \n", + "257 0.538462 0.976190 1.000000 0.984615 1.000000 \n", + "294 0.307692 0.666667 0.727273 0.830769 0.515789 \n", + "309 0.692308 0.714286 0.800000 0.800000 0.810526 \n", + "321 0.692308 0.738095 0.781818 0.646154 0.794737 \n", + "327 0.307692 0.928571 0.800000 0.661538 0.652632 \n", + "354 0.384615 0.833333 0.800000 0.676923 0.678947 \n", + "362 0.615385 0.761905 0.800000 0.676923 0.531579 \n", + "363 0.461538 0.880952 0.890909 0.738462 0.910526 \n", + "393 0.000000 0.857143 0.818182 0.830769 0.800000 \n", + "399 0.538462 0.880952 0.890909 0.692308 0.705263 \n", + "404 0.615385 1.000000 1.000000 0.569231 0.836842 \n", + "424 0.538462 0.857143 0.909091 0.600000 0.668421 \n", + "425 0.538462 0.833333 0.836364 0.646154 0.626316 \n", + "463 0.076923 0.761905 0.745455 0.907692 0.657895 \n", + "496 0.461538 0.666667 0.781818 0.800000 0.631579 \n", + "516 0.461538 0.714286 0.745455 0.815385 0.542105 \n", + "669 0.384615 0.785714 0.836364 0.723077 0.778947 \n", + "680 0.461538 0.571429 0.563636 0.953846 0.515789 \n", + "724 0.615385 0.690476 0.727273 0.907692 0.800000 \n", + "726 0.769231 0.595238 0.709091 0.769231 0.615789 \n", + "803 0.384615 0.714286 0.818182 0.707692 0.842105 \n", + "861 0.538462 0.761905 0.781818 0.738462 0.636842 \n", + "925 0.538462 0.785714 0.854545 0.661538 0.457895 \n", + "1004 0.461538 0.785714 0.763636 0.615385 0.621053 \n", + "1170 0.538462 0.642857 0.727273 0.723077 0.626316 \n", + "1414 0.769231 0.738095 0.763636 0.815385 0.657895 \n", + "1465 0.384615 0.738095 0.745455 0.800000 0.615789 \n", + "1653 0.769231 0.547619 0.709091 0.830769 0.310526 \n", + "1664 0.153846 0.619048 0.690909 1.000000 0.615789 \n", + "1782 0.692308 0.547619 0.618182 0.984615 0.252632 \n", + "1984 0.692308 0.571429 0.763636 0.830769 0.531579 \n", + "2298 0.307692 0.714286 0.836364 0.784615 0.731579 \n", + "2467 0.615385 0.666667 0.872727 0.661538 0.547368 \n", + "2531 0.538462 0.690476 0.745455 0.800000 0.600000 \n", + "2711 0.384615 0.619048 0.800000 0.984615 0.447368 \n", + "2881 0.461538 0.738095 0.763636 0.692308 0.668421 \n", + "3084 0.538462 0.738095 0.745455 0.769231 0.542105 \n", + "3374 0.538462 0.761905 0.727273 0.846154 0.526316 \n", + "4267 0.692308 0.642857 0.672727 0.738462 0.578947 \n", + "7770 0.846154 0.666667 0.745455 0.892308 0.568421 \n", + "15408 0.307692 0.785714 0.927273 0.676923 0.678947 \n", + "15426 0.461538 0.595238 0.654545 0.876923 0.494737 \n", + "\n", + " interceptions_n defending_n marking_n standing tackle_n \\\n", + "0 0.575 0.658824 0.722222 0.56250 \n", + "10 0.925 0.847059 0.916667 0.75000 \n", + "24 0.750 0.811765 0.833333 0.81250 \n", + "92 0.750 0.882353 0.888889 0.78125 \n", + "151 1.000 0.800000 0.833333 0.71875 \n", + "156 0.800 0.741176 0.861111 0.81250 \n", + "161 0.725 1.000000 1.000000 0.87500 \n", + "182 0.625 0.729412 0.750000 0.62500 \n", + "210 0.825 0.905882 0.888889 0.87500 \n", + "235 0.675 0.847059 0.833333 0.68750 \n", + "257 0.900 0.870588 0.722222 0.84375 \n", + "294 0.475 0.635294 0.694444 0.56250 \n", + "309 0.725 0.882353 0.861111 0.87500 \n", + "321 0.600 0.635294 0.722222 0.71875 \n", + "327 0.650 0.800000 0.944444 0.68750 \n", + "354 0.650 0.835294 0.777778 0.75000 \n", + "362 0.500 0.517647 0.583333 0.50000 \n", + "363 0.850 1.000000 0.916667 0.93750 \n", + "393 0.650 0.682353 0.666667 0.46875 \n", + "399 0.575 0.576471 0.666667 0.53125 \n", + "404 0.725 0.729412 0.722222 0.68750 \n", + "424 0.725 0.823529 0.583333 0.84375 \n", + "425 0.625 0.600000 0.527778 0.53125 \n", + "463 0.525 0.552941 0.444444 0.59375 \n", + "496 0.650 0.741176 0.777778 0.71875 \n", + "516 0.550 0.517647 0.555556 0.43750 \n", + "669 0.650 0.741176 0.694444 0.71875 \n", + "680 0.700 0.882353 0.861111 0.78125 \n", + "724 0.625 0.529412 0.750000 0.34375 \n", + "726 0.625 0.870588 0.861111 0.84375 \n", + "803 0.650 0.658824 0.583333 0.65625 \n", + "861 0.500 0.741176 0.666667 0.75000 \n", + "925 0.500 0.552941 0.666667 0.46875 \n", + "1004 0.675 0.529412 0.583333 0.53125 \n", + "1170 0.525 0.552941 0.611111 0.50000 \n", + "1414 0.500 0.458824 0.666667 0.40625 \n", + "1465 0.450 0.564706 0.666667 0.43750 \n", + "1653 0.350 0.305882 0.472222 0.28125 \n", + "1664 0.375 0.494118 0.472222 0.46875 \n", + "1782 0.350 0.364706 0.500000 0.31250 \n", + "1984 0.450 0.341176 0.472222 0.28125 \n", + "2298 0.450 0.482353 0.527778 0.43750 \n", + "2467 0.550 0.529412 0.666667 0.43750 \n", + "2531 0.350 0.400000 0.583333 0.40625 \n", + "2711 0.500 0.376471 0.444444 0.37500 \n", + "2881 0.550 0.411765 0.444444 0.40625 \n", + "3084 0.375 0.482353 0.472222 0.43750 \n", + "3374 0.425 0.411765 0.555556 0.37500 \n", + "4267 0.475 0.517647 0.472222 0.46875 \n", + "7770 0.575 0.470588 0.388889 0.56250 \n", + "15408 0.450 0.470588 0.583333 0.43750 \n", + "15426 0.525 0.376471 0.472222 0.37500 \n", + "\n", + " sliding tackle_n total stats_n pas_n def_n phy_n \\\n", + "0 0.68750 0.921914 0.909091 0.620690 0.615385 \n", + "10 0.78125 0.845088 0.840909 0.896552 0.692308 \n", + "24 0.71875 0.442065 0.545455 0.862069 0.435897 \n", + "92 0.87500 0.739295 0.750000 0.896552 0.589744 \n", + "151 0.78125 0.457179 0.545455 0.896552 0.538462 \n", + "156 0.50000 0.550378 0.568182 0.896552 0.769231 \n", + "161 0.96875 0.607053 0.590909 1.000000 0.846154 \n", + "182 0.78125 0.515113 0.568182 0.724138 0.769231 \n", + "210 0.84375 0.627204 0.704545 0.931034 0.615385 \n", + "235 0.93750 0.908060 0.886364 0.793103 0.589744 \n", + "257 0.96875 0.957179 0.931818 0.931034 0.717949 \n", + "294 0.65625 0.522670 0.590909 0.655172 0.717949 \n", + "309 0.81250 0.768262 0.636364 0.931034 0.769231 \n", + "321 0.46875 0.706549 0.750000 0.689655 0.769231 \n", + "327 0.68750 0.778338 0.818182 0.793103 0.410256 \n", + "354 0.90625 0.779597 0.681818 0.827586 0.871795 \n", + "362 0.53125 0.581864 0.681818 0.551724 0.641026 \n", + "363 1.00000 0.967254 0.863636 0.965517 0.692308 \n", + "393 0.90625 1.000000 0.818182 0.620690 0.435897 \n", + "399 0.56250 0.588161 0.750000 0.655172 0.666667 \n", + "404 0.75000 0.885390 1.000000 0.758621 0.846154 \n", + "424 1.00000 0.843829 0.863636 0.793103 0.512821 \n", + "425 0.78125 0.767003 0.772727 0.586207 0.615385 \n", + "463 0.68750 0.840050 0.795455 0.482759 0.666667 \n", + "496 0.68750 0.722922 0.590909 0.793103 0.717949 \n", + "516 0.62500 0.670025 0.750000 0.482759 0.589744 \n", + "669 0.78125 0.826196 0.886364 0.758621 0.564103 \n", + "680 0.90625 0.663728 0.477273 0.862069 0.897436 \n", + "724 0.53125 0.860202 0.727273 0.586207 0.846154 \n", + "726 0.81250 0.768262 0.681818 0.862069 0.769231 \n", + "803 0.75000 0.816121 0.727273 0.689655 0.923077 \n", + "861 0.78125 0.808564 0.772727 0.689655 0.641026 \n", + "925 0.56250 0.525189 0.659091 0.586207 0.615385 \n", + "1004 0.53125 0.677582 0.704545 0.586207 0.615385 \n", + "1170 0.59375 0.767003 0.659091 0.586207 0.717949 \n", + "1414 0.37500 0.687657 0.704545 0.551724 0.820513 \n", + "1465 0.62500 0.715365 0.750000 0.517241 0.666667 \n", + "1653 0.31250 0.390428 0.522727 0.344828 1.000000 \n", + "1664 0.62500 0.693955 0.568182 0.448276 1.000000 \n", + "1782 0.40625 0.275819 0.318182 0.379310 0.948718 \n", + "1984 0.40625 0.457179 0.500000 0.413793 0.769231 \n", + "2298 0.56250 0.904282 0.840909 0.482759 0.692308 \n", + "2467 0.53125 0.628463 0.659091 0.586207 0.615385 \n", + "2531 0.31250 0.614610 0.659091 0.448276 0.897436 \n", + "2711 0.43750 0.549118 0.568182 0.413793 0.717949 \n", + "2881 0.50000 0.686398 0.727273 0.448276 0.769231 \n", + "3084 0.62500 0.667506 0.681818 0.482759 0.794872 \n", + "3374 0.40625 0.698992 0.613636 0.482759 0.820513 \n", + "4267 0.68750 0.671285 0.636364 0.482759 0.820513 \n", + "7770 0.56250 0.552897 0.636364 0.517241 0.794872 \n", + "15408 0.46875 0.824937 0.886364 0.413793 0.641026 \n", + "15426 0.40625 0.573048 0.500000 0.448276 0.769231 \n", + "\n", + " total_price_n quality_value rank \n", + "0 0.783208 3.535639 higher \n", + "10 0.150376 4.134690 higher \n", + "24 0.000000 3.353330 higher \n", + "92 0.375940 3.491625 higher \n", + "151 0.000000 3.835997 higher \n", + "156 0.375940 3.656876 higher \n", + "161 0.000000 3.629529 higher \n", + "182 0.501253 3.428730 higher \n", + "210 0.000000 3.343581 higher \n", + "235 0.501253 3.941600 higher \n", + "257 0.000000 4.578755 higher \n", + "294 0.000000 3.417657 higher \n", + "309 0.563910 3.808516 higher \n", + "321 0.473684 3.535298 higher \n", + "327 0.778195 3.450366 higher \n", + "354 0.877193 3.832051 higher \n", + "362 0.835840 3.379853 higher \n", + "363 0.469925 4.052631 higher \n", + "393 0.720551 3.591991 higher \n", + "399 0.000000 3.705836 higher \n", + "404 0.000000 4.140385 higher \n", + "424 0.746867 3.604054 higher \n", + "425 0.889724 3.556235 higher \n", + "463 0.313283 3.606718 higher \n", + "496 0.000000 3.616434 higher \n", + "516 0.281955 3.414868 higher \n", + "669 0.581454 3.559257 higher \n", + "680 0.689223 3.686347 higher \n", + "724 0.793233 3.796595 higher \n", + "726 0.000000 3.467791 higher \n", + "803 0.501253 3.813237 higher \n", + "861 0.469925 3.423210 higher \n", + "925 0.461153 3.417183 higher \n", + "1004 0.275689 3.455120 higher \n", + "1170 0.895990 3.336156 higher \n", + "1414 0.916040 3.637629 higher \n", + "1465 0.655388 3.400216 higher \n", + "1653 0.394737 3.437479 higher \n", + "1664 0.987469 3.684957 higher \n", + "1782 0.162907 3.449134 higher \n", + "1984 0.751880 3.385065 higher \n", + "2298 0.689223 3.477572 higher \n", + "2467 0.977444 3.366317 higher \n", + "2531 0.916040 3.483367 higher \n", + "2711 0.479950 3.621612 higher \n", + "2881 0.921053 3.513270 higher \n", + "3084 0.987469 3.422652 higher \n", + "3374 0.488722 3.580844 higher \n", + "4267 0.844612 3.349559 higher \n", + "7770 0.645363 3.674301 higher \n", + "15408 0.908521 3.480936 higher \n", + "15426 0.789474 3.420937 higher " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_nquality_valuerank
0G. Pasquale33Italy182.88Udinese716968.032069.0208706969.019297068696250000.4285710.5384620.8571430.8727270.6153850.7052630.5750.6588240.7222220.562500.687500.9219140.9090910.6206900.6153850.7832083.535639higher
10P. Neville35England180.34Everton747271.033583.0224777572.018686776721200000.7142860.4615380.9285710.9272730.6615380.7842110.9250.8470590.9166670.750000.781250.8450880.8409090.8965520.6923080.1503764.134690higher
24A. Nesta37Italy187.96Montreal Impact656274.027776.0221747770.0154854756201.0000000.6923080.7142860.7454550.7076920.4789470.7500.8117650.8333330.812500.718750.4420650.5454550.8620690.4358970.0000003.353330higher
92G. Heinze35Argentina177.80Newell's Old Boys606676.030576.0227767675.017846376683000000.7142860.3846150.5952380.8181820.7384620.6263160.7500.8823530.8888890.781250.875000.7392950.7500000.8965520.5897440.3759403.491625higher
151W. Samuel37Argentina182.88FC Basel 1893666775.029386.0220747472.0156054766601.0000000.5384620.7380950.8363640.7230770.5631581.0000.8000000.8333330.718750.781250.4571790.5454550.8965520.5384620.0000003.835997higher
156O. Mellberg35Sweden187.96FC København636766.030278.0215757763.016345576753000000.7142860.6923080.6666670.8363640.5846150.6105260.8000.7411760.8611110.812500.500000.5503780.5681820.8965520.7692310.3759403.656876higher
161S. Distin37France193.04Bournemouth666463.030475.0237807978.0167956797801.0000000.8461540.7380950.7818180.5384620.6210530.7251.0000001.0000000.875000.968750.6070530.5909091.0000000.8461540.0000003.629529higher
182S. Diawara35Senegal187.96OGC Nice635972.026971.0214717172.016065571754000000.7142860.6923080.6666670.6909090.6769230.4368420.6250.7294120.7500000.625000.781250.5151130.5681820.7241380.7692310.5012533.428730higher
210Gilberto Silva36Brazil182.88Atlético Mineiro686351.033079.0229767974.0169561776900.8571430.5384620.7857140.7636360.3538460.7578950.8250.9058820.8888890.875000.843750.6272040.7045450.9310340.6153850.0000003.343581higher
235S. Cherundolo34United States172.72Hannover 96757181.032473.0224747377.019186973684000000.5714290.2307690.9523810.9090910.8153850.7263160.6750.8470590.8333330.687500.937500.9080600.8863640.7931030.5897440.5012533.941600higher
257M. Ambrosini36Italy182.88Fiorentina767692.037682.0226707878.0195771777300.8571430.5384620.9761901.0000000.9846151.0000000.9000.8705880.7222220.843750.968750.9571790.9318180.9310340.7179490.0000004.578755higher
294L. Cufré36Argentina175.26Leones Negros de la UdeG636182.028465.0206696968.0161256697300.8571430.3076920.6666670.7272730.8307690.5157890.4750.6352940.6944440.562500.656250.5226700.5909090.6551720.7179490.0000003.417657higher
309Lúcio35Brazil187.96Palmeiras656580.034075.0227757973.018075877754500000.7142860.6923080.7142860.8000000.8000000.8105260.7250.8823530.8611110.875000.812500.7682620.6363640.9310340.7692310.5639103.808516higher
321Borja Fernández37Spain187.96Real Valladolid CF666470.033770.0206707462.017586370753780001.0000000.6923080.7380950.7818180.6461540.7947370.6000.6352940.7222220.718750.468750.7065490.7500000.6896550.7692310.4736843.535298higher
327S. Riether35Germany175.26FC Schalke 04746571.031072.0220787369.018156673616210000.7142860.3076920.9285710.8000000.6615380.6526320.6500.8000000.9444440.687500.687500.7783380.8181820.7931030.4102560.7781953.450366higher
354K. Touré35Ivory Coast177.80Celtic706572.031572.0223727576.018166074797000000.7142860.3846150.8333330.8000000.6769230.6789470.6500.8352940.7777780.750000.906250.7795970.6818180.8275860.8717950.8771933.832051higher
362C. Schulz34Germany185.42SK Sturm Graz676572.028766.0196656764.016596066706670000.5714290.6153850.7619050.8000000.6769230.5315790.5000.5176470.5833330.500000.531250.5818640.6818180.5517240.6410260.8358403.379853higher
363A. Tymoshchuk35Ukraine180.34Zenit St. Petersburg727076.035980.0237778179.019656878723750000.7142860.4615380.8809520.8909090.7384620.9105260.8501.0000000.9166670.937501.000000.9672540.8636360.9655170.6923080.4699254.052631higher
393Junior Cesar31Brazil165.10Botafogo716682.033872.0210686676.019916668625750000.1428570.0000000.8571430.8181820.8307690.8000000.6500.6823530.6666670.468750.906251.0000000.8181820.6206900.4358970.7205513.591991higher
399Cris36Brazil182.88Vasco da Gama727073.032069.0201686865.0166463697100.8571430.5384620.8809520.8909090.6923080.7052630.5750.5764710.6666670.531250.562500.5881610.7500000.6551720.6666670.0000003.705836higher
404P. García36Uruguay185.42PAOK777665.034575.0214707371.0190074727800.8571430.6153851.0000001.0000000.5692310.8368420.7250.7294120.7222220.687500.750000.8853901.0000000.7586210.8461540.0000004.140385higher
424M. Gobbi37Italy182.88Parma717167.031375.0222657879.018676873655960001.0000000.5384620.8571430.9090910.6000000.6684210.7250.8235290.5833330.843751.000000.8438290.8636360.7931030.5128210.7468673.604054higher
425M. Cassani33Italy182.88Bari706770.030571.0203636872.018066467697100000.4285710.5384620.8333330.8363640.6461540.6263160.6250.6000000.5277780.531250.781250.7670030.7727270.5862070.6153850.8897243.556235higher
463C. Rodríguez36Argentina167.64Club Atlético Colón676287.031167.0199607069.018646564712500000.8571430.0769230.7619050.7454550.9076920.6578950.5250.5529410.4444440.593750.687500.8400500.7954550.4827590.6666670.3132833.606718higher
496D. Verón36Paraguay180.34Paraguay636480.030672.0215727469.0177156737300.8571430.4615380.6666670.7818180.8000000.6315790.6500.7411760.7777780.718750.687500.7229220.5909090.7931030.7179490.0000003.616434higher
516J. Thomsen37Denmark180.34Randers FC656281.028968.0196646567.017296364682250001.0000000.4615380.7142860.7454550.8153850.5421050.5500.5176470.5555560.437500.625000.6700250.7500000.4827590.5897440.2819553.414868higher
669Àngel Rangel36Spain177.80Queens Park Rangers686775.033472.0215697472.018536972674640000.8571430.3846150.7857140.8363640.7230770.7789470.6500.7411760.6944440.718750.781250.8261960.8863640.7586210.5641030.5814543.559257higher
680L. Perea35Colombia180.34Cruz Azul595290.028474.0227757676.017245175805500000.7142860.4615380.5714290.5636360.9538460.5157890.7000.8823530.8611110.781250.906250.6637280.4772730.8620690.8974360.6892233.686347higher
724D. Pratley35England185.42Charlton Athletic646187.033871.0197716264.018806267786330000.7142860.6153850.6904760.7272730.9076920.8000000.6250.5294120.7500000.343750.531250.8602020.7272730.5862070.8461540.7932333.796595higher
726G. Sardo37Italy190.50Chievo Verona606078.030371.0226757873.0180760757501.0000000.7692310.5952380.7090910.7692310.6157890.6250.8705880.8611110.843750.812500.7682620.6818180.8620690.7692310.0000003.467791higher
803D. Pérez34Uruguay177.80Bologna656674.034672.0208657271.018456270814000000.5714290.3846150.7142860.8181820.7076920.8421050.6500.6588240.5833330.656250.750000.8161210.7272730.6896550.9230770.5012533.813237higher
861Evaldo33Brazil182.88Moreirense FC676476.030766.0215687572.018396470703750000.4285710.5384620.7619050.7818180.7384620.6368420.5000.7411760.6666670.750000.781250.8085640.7727270.6896550.6410260.4699253.423210higher
925D. Moor36United States182.88Colorado Rapids686871.027366.0199686665.016145967693680000.8571430.5384620.7857140.8545450.6615380.4578950.5000.5529410.6666670.468750.562500.5251890.6590910.5862070.6153850.4611533.417183higher
1004J. Polák34Czech Republic180.341. FC Nürnberg686368.030473.0197656864.017356167692200000.5714290.4615380.7857140.7636360.6153850.6210530.6750.5294120.5833330.531250.531250.6775820.7045450.5862070.6153850.2756893.455120higher
1170Bolaño34Spain182.88Real Oviedo626175.030567.0199666766.018065967737150000.5714290.5384620.6428570.7272730.7230770.6263160.5250.5529410.6111110.500000.593750.7670030.6590910.5862070.7179490.8959903.336156higher
1414V. Elm34Sweden190.50Kalmar FF666381.031166.0191686459.017436166777310000.5714290.7692310.7380950.7636360.8153850.6578950.5000.4588240.6666670.406250.375000.6876570.7045450.5517240.8205130.9160403.637629higher
1465A. Raineau34France177.80La Berrichonne de Châteauroux666280.030364.0200686567.017656365715230000.5714290.3846150.7380950.7454550.8000000.6157890.4500.5647060.6666670.437500.625000.7153650.7500000.5172410.6666670.6553883.400216higher
1653N. Topor-Stanley35Australia190.50Newcastle Jets586082.024560.0178616057.015075360843150000.7142860.7692310.5476190.7090910.8307690.3105260.3500.3058820.4722220.281250.312500.3904280.5227270.3448281.0000000.3947373.437479higher
1664L. Broxham32Australia170.18Melbourne Victory615993.030361.0194616667.017485563847880000.2857140.1538460.6190480.6909091.0000000.6157890.3750.4941180.4722220.468750.625000.6939550.5681820.4482761.0000000.9874693.684957higher
1782B. Sigmund34New Zealand187.96Wellington Phoenix585592.023460.0183626160.014164461821300000.5714290.6923080.5476190.6181820.9846150.2526320.3500.3647060.5000000.312500.406250.2758190.3181820.3793100.9487180.1629073.449134higher
1984A. Collin34France187.96Philadelphia Union596382.028764.0181616060.015605262756000000.5714290.6923080.5714290.7636360.8307690.5315790.4500.3411760.4722220.281250.406250.4571790.5000000.4137930.7692310.7518803.385065higher
2298A. Meijers32Netherlands175.26Sparta Rotterdam656779.032564.0193636565.019156764725500000.2857140.3076920.7142860.8363640.7846150.7315790.4500.4823530.5277780.437500.562500.9042820.8409090.4827590.6923080.6892233.477572higher
2467Lombán33Spain185.42Málaga CF636971.029068.0197686564.016965967697800000.4285710.6153850.6666670.8727270.6615380.5473680.5500.5294120.6666670.437500.531250.6284630.6590910.5862070.6153850.9774443.366317higher
2531R. Austin34Jamaica182.88Esbjerg fB646280.030060.0186656457.016855963807310000.5714290.5384620.6904760.7454550.8000000.6000000.3500.4000000.5833330.406250.312500.6146100.6590910.4482760.8974360.9160403.483367higher
2711M. Inoha34Japan177.80Yokohama FC616592.027166.0184606361.016335562733830000.5714290.3846150.6190480.8000000.9846150.4473680.5000.3764710.4444440.375000.437500.5491180.5681820.4137930.7179490.4799503.621612higher
2881H. Mulder33Netherlands180.34RKC Waalwijk666373.031368.0187606463.017426263757350000.4285710.4615380.7380950.7636360.6923080.6684210.5500.4117650.4444440.406250.500000.6863980.7272730.4482760.7692310.9210533.513270higher
3084T. Makino33Japan182.88Urawa Red Diamonds666278.028961.0193616567.017276064767880000.4285710.5384620.7380950.7454550.7692310.5421050.3750.4823530.4722220.437500.625000.6675060.6818180.4827590.7948720.9874693.422652higher
3374Carlos Ruiz36Spain182.88CD Tenerife676183.028663.0187646360.017525764773900000.8571430.5384620.7619050.7272730.8461540.5263160.4250.4117650.5555560.375000.406250.6989920.6136360.4827590.8205130.4887223.580844higher
4267D. Kempe34Germany187.96SV Wehen Wiesbaden625876.029665.0196616669.017305864776740000.5714290.6923080.6428570.6727270.7384620.5789470.4750.5176470.4722220.468750.687500.6712850.6363640.4827590.8205130.8446123.349559higher
7770M. Čovilo34Bosnia Herzegovina193.04FC Lugano636286.029469.0192586965.016365865765150000.5714290.8461540.6666670.7454550.8923080.5684210.5750.4705880.3888890.562500.562500.5528970.6363640.5172410.7948720.6453633.674301higher
15408C. Caraza34Peru175.26Sport Huancayo687272.031564.0192656562.018526962707250000.5714290.3076920.7857140.9272730.6769230.6789470.4500.4705880.5833330.437500.468750.8249370.8863640.4137930.6410260.9085213.480936higher
15426V. Balta34Peru180.34Sport Huancayo605785.028067.0184616360.016525263756300000.5714290.4615380.5952380.6545450.8769230.4947370.5250.3764710.4722220.375000.406250.5730480.5000000.4482760.7692310.7894743.420937higher
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 437 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import sklearn\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.preprocessing import StandardScaler" + ], + "metadata": { + "id": "xfuDQZ1xofGy" + }, + "execution_count": 438, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#scaler = StandardScaler()\n", + "#scaler.fit_transform(defenders_filtered_stats2)" + ], + "metadata": { + "id": "LqoL-m8GpC4e" + }, + "execution_count": 439, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "#sns.heatmap(defenders_filtered_stats2.corr(),annot=True,xticklabel=defenders_filtered_stats2.corr().columns,yticklabel=defenders_filtered_stats2.corr().columns)\n", + "#plt.plot(defenders_filtered_stats['total stats'],defenders_filtered_stats['total_price'],'go')\n", + "#plt.show()" + ], + "metadata": { + "id": "n01-rxomqUZe" + }, + "execution_count": 440, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "'''fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(10, 10))\n", + "\n", + "# set the x-axis to a common column for all subplots\n", + "x_column = 'total_price'\n", + "\n", + "# iterate over the axes and plot each column against the common column\n", + "for i, ax in enumerate(axes.flatten()):\n", + " if i < len(defenders_filtered_stats2.columns) - 1:\n", + " y_column = defenders_filtered_stats2.columns[i+1]\n", + " ax.scatter(defenders_filtered_stats2[x_column], defenders_filtered_stats2[y_column])\n", + " ax.set_xlabel(x_column)\n", + " ax.set_ylabel(y_column)\n", + "\n", + "# adjust the layout of the subplots and show the figure\n", + "fig.tight_layout()\n", + "plt.show()\n", + "'''" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 71 + }, + "id": "m-GevxVHtF3a", + "outputId": "c01647a7-c5b9-494a-a92c-3e9939caa4d5" + }, + "execution_count": 441, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(10, 10))\\n\\n# set the x-axis to a common column for all subplots\\nx_column = 'total_price'\\n\\n# iterate over the axes and plot each column against the common column\\nfor i, ax in enumerate(axes.flatten()):\\n if i < len(defenders_filtered_stats2.columns) - 1:\\n y_column = defenders_filtered_stats2.columns[i+1]\\n ax.scatter(defenders_filtered_stats2[x_column], defenders_filtered_stats2[y_column])\\n ax.set_xlabel(x_column)\\n ax.set_ylabel(y_column)\\n\\n# adjust the layout of the subplots and show the figure\\nfig.tight_layout()\\nplt.show()\\n\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 441 + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(defenders_filtered_stats['quality_value'],defenders_filtered_stats['def'],c=defenders_filtered_stats['total_price'])\n", + "plt.colorbar()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430 + }, + "id": "0wTUgX50uWT0", + "outputId": "5aff5b61-3116-486b-b459-e3ff4632f017" + }, + "execution_count": 442, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "defenders_filtered_stats[defenders_filtered_stats['def']>72]" + ], + "metadata": { + "id": "LZeNZYomw3T3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 838 + }, + "outputId": "a34d2fb3-8f5e-49e9-c30c-5e1c17ae9432" + }, + "execution_count": 443, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age nationality height_cm club \\\n", + "10 P. Neville 35 England 180.34 Everton \n", + "24 A. Nesta 37 Italy 187.96 Montreal Impact \n", + "92 G. Heinze 35 Argentina 177.80 Newell's Old Boys \n", + "151 W. Samuel 37 Argentina 182.88 FC Basel 1893 \n", + "156 O. Mellberg 35 Sweden 187.96 FC København \n", + "161 S. Distin 37 France 193.04 Bournemouth \n", + "210 Gilberto Silva 36 Brazil 182.88 Atlético Mineiro \n", + "235 S. Cherundolo 34 United States 172.72 Hannover 96 \n", + "257 M. Ambrosini 36 Italy 182.88 Fiorentina \n", + "309 Lúcio 35 Brazil 187.96 Palmeiras \n", + "327 S. Riether 35 Germany 175.26 FC Schalke 04 \n", + "354 K. Touré 35 Ivory Coast 177.80 Celtic \n", + "363 A. Tymoshchuk 35 Ukraine 180.34 Zenit St. Petersburg \n", + "424 M. Gobbi 37 Italy 182.88 Parma \n", + "496 D. Verón 36 Paraguay 180.34 Paraguay \n", + "680 L. Perea 35 Colombia 180.34 Cruz Azul \n", + "726 G. Sardo 37 Italy 190.50 Chievo Verona \n", + "\n", + " short passing long passing jumping mentality interceptions \\\n", + "10 74 72 71.0 335 83.0 \n", + "24 65 62 74.0 277 76.0 \n", + "92 60 66 76.0 305 76.0 \n", + "151 66 67 75.0 293 86.0 \n", + "156 63 67 66.0 302 78.0 \n", + "161 66 64 63.0 304 75.0 \n", + "210 68 63 51.0 330 79.0 \n", + "235 75 71 81.0 324 73.0 \n", + "257 76 76 92.0 376 82.0 \n", + "309 65 65 80.0 340 75.0 \n", + "327 74 65 71.0 310 72.0 \n", + "354 70 65 72.0 315 72.0 \n", + "363 72 70 76.0 359 80.0 \n", + "424 71 71 67.0 313 75.0 \n", + "496 63 64 80.0 306 72.0 \n", + "680 59 52 90.0 284 74.0 \n", + "726 60 60 78.0 303 71.0 \n", + "\n", + " defending marking standing tackle sliding tackle total stats pas \\\n", + "10 224 77 75 72.0 1868 67 \n", + "24 221 74 77 70.0 1548 54 \n", + "92 227 76 76 75.0 1784 63 \n", + "151 220 74 74 72.0 1560 54 \n", + "156 215 75 77 63.0 1634 55 \n", + "161 237 80 79 78.0 1679 56 \n", + "210 229 76 79 74.0 1695 61 \n", + "235 224 74 73 77.0 1918 69 \n", + "257 226 70 78 78.0 1957 71 \n", + "309 227 75 79 73.0 1807 58 \n", + "327 220 78 73 69.0 1815 66 \n", + "354 223 72 75 76.0 1816 60 \n", + "363 237 77 81 79.0 1965 68 \n", + "424 222 65 78 79.0 1867 68 \n", + "496 215 72 74 69.0 1771 56 \n", + "680 227 75 76 76.0 1724 51 \n", + "726 226 75 78 73.0 1807 60 \n", + "\n", + " def phy total_price age_n height_cm_n short passing_n \\\n", + "10 76 72 120000 0.714286 0.461538 0.928571 \n", + "24 75 62 0 1.000000 0.692308 0.714286 \n", + "92 76 68 300000 0.714286 0.384615 0.595238 \n", + "151 76 66 0 1.000000 0.538462 0.738095 \n", + "156 76 75 300000 0.714286 0.692308 0.666667 \n", + "161 79 78 0 1.000000 0.846154 0.738095 \n", + "210 77 69 0 0.857143 0.538462 0.785714 \n", + "235 73 68 400000 0.571429 0.230769 0.952381 \n", + "257 77 73 0 0.857143 0.538462 0.976190 \n", + "309 77 75 450000 0.714286 0.692308 0.714286 \n", + "327 73 61 621000 0.714286 0.307692 0.928571 \n", + "354 74 79 700000 0.714286 0.384615 0.833333 \n", + "363 78 72 375000 0.714286 0.461538 0.880952 \n", + "424 73 65 596000 1.000000 0.538462 0.857143 \n", + "496 73 73 0 0.857143 0.461538 0.666667 \n", + "680 75 80 550000 0.714286 0.461538 0.571429 \n", + "726 75 75 0 1.000000 0.769231 0.595238 \n", + "\n", + " long passing_n jumping_n mentality_n interceptions_n defending_n \\\n", + "10 0.927273 0.661538 0.784211 0.925 0.847059 \n", + "24 0.745455 0.707692 0.478947 0.750 0.811765 \n", + "92 0.818182 0.738462 0.626316 0.750 0.882353 \n", + "151 0.836364 0.723077 0.563158 1.000 0.800000 \n", + "156 0.836364 0.584615 0.610526 0.800 0.741176 \n", + "161 0.781818 0.538462 0.621053 0.725 1.000000 \n", + "210 0.763636 0.353846 0.757895 0.825 0.905882 \n", + "235 0.909091 0.815385 0.726316 0.675 0.847059 \n", + "257 1.000000 0.984615 1.000000 0.900 0.870588 \n", + "309 0.800000 0.800000 0.810526 0.725 0.882353 \n", + "327 0.800000 0.661538 0.652632 0.650 0.800000 \n", + "354 0.800000 0.676923 0.678947 0.650 0.835294 \n", + "363 0.890909 0.738462 0.910526 0.850 1.000000 \n", + "424 0.909091 0.600000 0.668421 0.725 0.823529 \n", + "496 0.781818 0.800000 0.631579 0.650 0.741176 \n", + "680 0.563636 0.953846 0.515789 0.700 0.882353 \n", + "726 0.709091 0.769231 0.615789 0.625 0.870588 \n", + "\n", + " marking_n standing tackle_n sliding tackle_n total stats_n pas_n \\\n", + "10 0.916667 0.75000 0.78125 0.845088 0.840909 \n", + "24 0.833333 0.81250 0.71875 0.442065 0.545455 \n", + "92 0.888889 0.78125 0.87500 0.739295 0.750000 \n", + "151 0.833333 0.71875 0.78125 0.457179 0.545455 \n", + "156 0.861111 0.81250 0.50000 0.550378 0.568182 \n", + "161 1.000000 0.87500 0.96875 0.607053 0.590909 \n", + "210 0.888889 0.87500 0.84375 0.627204 0.704545 \n", + "235 0.833333 0.68750 0.93750 0.908060 0.886364 \n", + "257 0.722222 0.84375 0.96875 0.957179 0.931818 \n", + "309 0.861111 0.87500 0.81250 0.768262 0.636364 \n", + "327 0.944444 0.68750 0.68750 0.778338 0.818182 \n", + "354 0.777778 0.75000 0.90625 0.779597 0.681818 \n", + "363 0.916667 0.93750 1.00000 0.967254 0.863636 \n", + "424 0.583333 0.84375 1.00000 0.843829 0.863636 \n", + "496 0.777778 0.71875 0.68750 0.722922 0.590909 \n", + "680 0.861111 0.78125 0.90625 0.663728 0.477273 \n", + "726 0.861111 0.84375 0.81250 0.768262 0.681818 \n", + "\n", + " def_n phy_n total_price_n quality_value rank \n", + "10 0.896552 0.692308 0.150376 4.134690 higher \n", + "24 0.862069 0.435897 0.000000 3.353330 higher \n", + "92 0.896552 0.589744 0.375940 3.491625 higher \n", + "151 0.896552 0.538462 0.000000 3.835997 higher \n", + "156 0.896552 0.769231 0.375940 3.656876 higher \n", + "161 1.000000 0.846154 0.000000 3.629529 higher \n", + "210 0.931034 0.615385 0.000000 3.343581 higher \n", + "235 0.793103 0.589744 0.501253 3.941600 higher \n", + "257 0.931034 0.717949 0.000000 4.578755 higher \n", + "309 0.931034 0.769231 0.563910 3.808516 higher \n", + "327 0.793103 0.410256 0.778195 3.450366 higher \n", + "354 0.827586 0.871795 0.877193 3.832051 higher \n", + "363 0.965517 0.692308 0.469925 4.052631 higher \n", + "424 0.793103 0.512821 0.746867 3.604054 higher \n", + "496 0.793103 0.717949 0.000000 3.616434 higher \n", + "680 0.862069 0.897436 0.689223 3.686347 higher \n", + "726 0.862069 0.769231 0.000000 3.467791 higher " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_nquality_valuerank
10P. Neville35England180.34Everton747271.033583.0224777572.018686776721200000.7142860.4615380.9285710.9272730.6615380.7842110.9250.8470590.9166670.750000.781250.8450880.8409090.8965520.6923080.1503764.134690higher
24A. Nesta37Italy187.96Montreal Impact656274.027776.0221747770.0154854756201.0000000.6923080.7142860.7454550.7076920.4789470.7500.8117650.8333330.812500.718750.4420650.5454550.8620690.4358970.0000003.353330higher
92G. Heinze35Argentina177.80Newell's Old Boys606676.030576.0227767675.017846376683000000.7142860.3846150.5952380.8181820.7384620.6263160.7500.8823530.8888890.781250.875000.7392950.7500000.8965520.5897440.3759403.491625higher
151W. Samuel37Argentina182.88FC Basel 1893666775.029386.0220747472.0156054766601.0000000.5384620.7380950.8363640.7230770.5631581.0000.8000000.8333330.718750.781250.4571790.5454550.8965520.5384620.0000003.835997higher
156O. Mellberg35Sweden187.96FC København636766.030278.0215757763.016345576753000000.7142860.6923080.6666670.8363640.5846150.6105260.8000.7411760.8611110.812500.500000.5503780.5681820.8965520.7692310.3759403.656876higher
161S. Distin37France193.04Bournemouth666463.030475.0237807978.0167956797801.0000000.8461540.7380950.7818180.5384620.6210530.7251.0000001.0000000.875000.968750.6070530.5909091.0000000.8461540.0000003.629529higher
210Gilberto Silva36Brazil182.88Atlético Mineiro686351.033079.0229767974.0169561776900.8571430.5384620.7857140.7636360.3538460.7578950.8250.9058820.8888890.875000.843750.6272040.7045450.9310340.6153850.0000003.343581higher
235S. Cherundolo34United States172.72Hannover 96757181.032473.0224747377.019186973684000000.5714290.2307690.9523810.9090910.8153850.7263160.6750.8470590.8333330.687500.937500.9080600.8863640.7931030.5897440.5012533.941600higher
257M. Ambrosini36Italy182.88Fiorentina767692.037682.0226707878.0195771777300.8571430.5384620.9761901.0000000.9846151.0000000.9000.8705880.7222220.843750.968750.9571790.9318180.9310340.7179490.0000004.578755higher
309Lúcio35Brazil187.96Palmeiras656580.034075.0227757973.018075877754500000.7142860.6923080.7142860.8000000.8000000.8105260.7250.8823530.8611110.875000.812500.7682620.6363640.9310340.7692310.5639103.808516higher
327S. Riether35Germany175.26FC Schalke 04746571.031072.0220787369.018156673616210000.7142860.3076920.9285710.8000000.6615380.6526320.6500.8000000.9444440.687500.687500.7783380.8181820.7931030.4102560.7781953.450366higher
354K. Touré35Ivory Coast177.80Celtic706572.031572.0223727576.018166074797000000.7142860.3846150.8333330.8000000.6769230.6789470.6500.8352940.7777780.750000.906250.7795970.6818180.8275860.8717950.8771933.832051higher
363A. Tymoshchuk35Ukraine180.34Zenit St. Petersburg727076.035980.0237778179.019656878723750000.7142860.4615380.8809520.8909090.7384620.9105260.8501.0000000.9166670.937501.000000.9672540.8636360.9655170.6923080.4699254.052631higher
424M. Gobbi37Italy182.88Parma717167.031375.0222657879.018676873655960001.0000000.5384620.8571430.9090910.6000000.6684210.7250.8235290.5833330.843751.000000.8438290.8636360.7931030.5128210.7468673.604054higher
496D. Verón36Paraguay180.34Paraguay636480.030672.0215727469.0177156737300.8571430.4615380.6666670.7818180.8000000.6315790.6500.7411760.7777780.718750.687500.7229220.5909090.7931030.7179490.0000003.616434higher
680L. Perea35Colombia180.34Cruz Azul595290.028474.0227757676.017245175805500000.7142860.4615380.5714290.5636360.9538460.5157890.7000.8823530.8611110.781250.906250.6637280.4772730.8620690.8974360.6892233.686347higher
726G. Sardo37Italy190.50Chievo Verona606078.030371.0226757873.0180760757501.0000000.7692310.5952380.7090910.7692310.6157890.6250.8705880.8611110.843750.812500.7682620.6818180.8620690.7692310.0000003.467791higher
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 443 + } + ] + }, + { + "cell_type": "code", + "source": [ + "defenders_filtered_stats['ratio_n'] = defenders_filtered_stats['quality_value']/defenders_filtered_stats['total_price_n']\n", + "defenders_filtered_stats.sort_values(by='quality_value',ascending=False).shape\n", + "#list(defenders_filtered_stats['name'][:10])" + ], + "metadata": { + "id": "E_pP_xJjb9S2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d0ca0658-bff9-4321-ea10-5e893c495815" + }, + "execution_count": 444, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " defenders_filtered_stats['ratio_n'] = defenders_filtered_stats['quality_value']/defenders_filtered_stats['total_price_n']\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(52, 38)" + ] + }, + "metadata": {}, + "execution_count": 444 + } + ] + }, + { + "cell_type": "code", + "source": [ + "defenders_filtered_stats = defenders_filtered_stats[defenders_filtered_stats['defending_n']>0.6]\n", + "best_defenders = defenders_filtered_stats.sort_values(by='quality_value',ascending=False)\n", + "best_defenders.head(10)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "id": "UuWlAT_lL03x", + "outputId": "2cfe23fc-d6d8-4099-b016-050e6249d486" + }, + "execution_count": 451, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age nationality height_cm club \\\n", + "257 M. Ambrosini 36 Italy 182.88 Fiorentina \n", + "404 P. García 36 Uruguay 185.42 PAOK \n", + "10 P. Neville 35 England 180.34 Everton \n", + "363 A. Tymoshchuk 35 Ukraine 180.34 Zenit St. Petersburg \n", + "235 S. Cherundolo 34 United States 172.72 Hannover 96 \n", + "151 W. Samuel 37 Argentina 182.88 FC Basel 1893 \n", + "354 K. Touré 35 Ivory Coast 177.80 Celtic \n", + "803 D. Pérez 34 Uruguay 177.80 Bologna \n", + "309 Lúcio 35 Brazil 187.96 Palmeiras \n", + "680 L. Perea 35 Colombia 180.34 Cruz Azul \n", + "\n", + " short passing long passing jumping mentality interceptions \\\n", + "257 76 76 92.0 376 82.0 \n", + "404 77 76 65.0 345 75.0 \n", + "10 74 72 71.0 335 83.0 \n", + "363 72 70 76.0 359 80.0 \n", + "235 75 71 81.0 324 73.0 \n", + "151 66 67 75.0 293 86.0 \n", + "354 70 65 72.0 315 72.0 \n", + "803 65 66 74.0 346 72.0 \n", + "309 65 65 80.0 340 75.0 \n", + "680 59 52 90.0 284 74.0 \n", + "\n", + " defending marking standing tackle sliding tackle total stats pas \\\n", + "257 226 70 78 78.0 1957 71 \n", + "404 214 70 73 71.0 1900 74 \n", + "10 224 77 75 72.0 1868 67 \n", + "363 237 77 81 79.0 1965 68 \n", + "235 224 74 73 77.0 1918 69 \n", + "151 220 74 74 72.0 1560 54 \n", + "354 223 72 75 76.0 1816 60 \n", + "803 208 65 72 71.0 1845 62 \n", + "309 227 75 79 73.0 1807 58 \n", + "680 227 75 76 76.0 1724 51 \n", + "\n", + " def phy total_price age_n height_cm_n short passing_n \\\n", + "257 77 73 0 0.857143 0.538462 0.976190 \n", + "404 72 78 0 0.857143 0.615385 1.000000 \n", + "10 76 72 120000 0.714286 0.461538 0.928571 \n", + "363 78 72 375000 0.714286 0.461538 0.880952 \n", + "235 73 68 400000 0.571429 0.230769 0.952381 \n", + "151 76 66 0 1.000000 0.538462 0.738095 \n", + "354 74 79 700000 0.714286 0.384615 0.833333 \n", + "803 70 81 400000 0.571429 0.384615 0.714286 \n", + "309 77 75 450000 0.714286 0.692308 0.714286 \n", + "680 75 80 550000 0.714286 0.461538 0.571429 \n", + "\n", + " long passing_n jumping_n mentality_n interceptions_n defending_n \\\n", + "257 1.000000 0.984615 1.000000 0.900 0.870588 \n", + "404 1.000000 0.569231 0.836842 0.725 0.729412 \n", + "10 0.927273 0.661538 0.784211 0.925 0.847059 \n", + "363 0.890909 0.738462 0.910526 0.850 1.000000 \n", + "235 0.909091 0.815385 0.726316 0.675 0.847059 \n", + "151 0.836364 0.723077 0.563158 1.000 0.800000 \n", + "354 0.800000 0.676923 0.678947 0.650 0.835294 \n", + "803 0.818182 0.707692 0.842105 0.650 0.658824 \n", + "309 0.800000 0.800000 0.810526 0.725 0.882353 \n", + "680 0.563636 0.953846 0.515789 0.700 0.882353 \n", + "\n", + " marking_n standing tackle_n sliding tackle_n total stats_n pas_n \\\n", + "257 0.722222 0.84375 0.96875 0.957179 0.931818 \n", + "404 0.722222 0.68750 0.75000 0.885390 1.000000 \n", + "10 0.916667 0.75000 0.78125 0.845088 0.840909 \n", + "363 0.916667 0.93750 1.00000 0.967254 0.863636 \n", + "235 0.833333 0.68750 0.93750 0.908060 0.886364 \n", + "151 0.833333 0.71875 0.78125 0.457179 0.545455 \n", + "354 0.777778 0.75000 0.90625 0.779597 0.681818 \n", + "803 0.583333 0.65625 0.75000 0.816121 0.727273 \n", + "309 0.861111 0.87500 0.81250 0.768262 0.636364 \n", + "680 0.861111 0.78125 0.90625 0.663728 0.477273 \n", + "\n", + " def_n phy_n total_price_n quality_value rank ratio_n \n", + "257 0.931034 0.717949 0.000000 4.578755 higher inf \n", + "404 0.758621 0.846154 0.000000 4.140385 higher inf \n", + "10 0.896552 0.692308 0.150376 4.134690 higher 27.495691 \n", + "363 0.965517 0.692308 0.469925 4.052631 higher 8.623998 \n", + "235 0.793103 0.589744 0.501253 3.941600 higher 7.863492 \n", + "151 0.896552 0.538462 0.000000 3.835997 higher inf \n", + "354 0.827586 0.871795 0.877193 3.832051 higher 4.368538 \n", + "803 0.689655 0.923077 0.501253 3.813237 higher 7.607407 \n", + "309 0.931034 0.769231 0.563910 3.808516 higher 6.753769 \n", + "680 0.862069 0.897436 0.689223 3.686347 higher 5.348554 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_nquality_valuerankratio_n
257M. Ambrosini36Italy182.88Fiorentina767692.037682.0226707878.0195771777300.8571430.5384620.9761901.0000000.9846151.0000000.9000.8705880.7222220.843750.968750.9571790.9318180.9310340.7179490.0000004.578755higherinf
404P. García36Uruguay185.42PAOK777665.034575.0214707371.0190074727800.8571430.6153851.0000001.0000000.5692310.8368420.7250.7294120.7222220.687500.750000.8853901.0000000.7586210.8461540.0000004.140385higherinf
10P. Neville35England180.34Everton747271.033583.0224777572.018686776721200000.7142860.4615380.9285710.9272730.6615380.7842110.9250.8470590.9166670.750000.781250.8450880.8409090.8965520.6923080.1503764.134690higher27.495691
363A. Tymoshchuk35Ukraine180.34Zenit St. Petersburg727076.035980.0237778179.019656878723750000.7142860.4615380.8809520.8909090.7384620.9105260.8501.0000000.9166670.937501.000000.9672540.8636360.9655170.6923080.4699254.052631higher8.623998
235S. Cherundolo34United States172.72Hannover 96757181.032473.0224747377.019186973684000000.5714290.2307690.9523810.9090910.8153850.7263160.6750.8470590.8333330.687500.937500.9080600.8863640.7931030.5897440.5012533.941600higher7.863492
151W. Samuel37Argentina182.88FC Basel 1893666775.029386.0220747472.0156054766601.0000000.5384620.7380950.8363640.7230770.5631581.0000.8000000.8333330.718750.781250.4571790.5454550.8965520.5384620.0000003.835997higherinf
354K. Touré35Ivory Coast177.80Celtic706572.031572.0223727576.018166074797000000.7142860.3846150.8333330.8000000.6769230.6789470.6500.8352940.7777780.750000.906250.7795970.6818180.8275860.8717950.8771933.832051higher4.368538
803D. Pérez34Uruguay177.80Bologna656674.034672.0208657271.018456270814000000.5714290.3846150.7142860.8181820.7076920.8421050.6500.6588240.5833330.656250.750000.8161210.7272730.6896550.9230770.5012533.813237higher7.607407
309Lúcio35Brazil187.96Palmeiras656580.034075.0227757973.018075877754500000.7142860.6923080.7142860.8000000.8000000.8105260.7250.8823530.8611110.875000.812500.7682620.6363640.9310340.7692310.5639103.808516higher6.753769
680L. Perea35Colombia180.34Cruz Azul595290.028474.0227757676.017245175805500000.7142860.4615380.5714290.5636360.9538460.5157890.7000.8823530.8611110.781250.906250.6637280.4772730.8620690.8974360.6892233.686347higher5.348554
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 451 + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(defenders_filtered_stats['defending'],defenders_filtered_stats['quality_value'],c=defenders_filtered_stats['total_price'])\n", + "plt.colorbar()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + }, + "id": "fvdbwrE7HIqL", + "outputId": "53672dee-9908-4961-bea9-618bf36d04b9" + }, + "execution_count": 452, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGiCAYAAADJO+2bAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABgJUlEQVR4nO3de1xUdf4/8NeZgRlAGG4KiOClNJUUTVSczNqKpJbc3LRVY5M1q9XQVCov5XqrxLWb9jM1rdTvbq5mu7bmNdPQSlREKTTRvILCgDcYRGFgzuf3h8vkBAzDzDAzwOv5eJzHI855z+e8jxMzbz6fz/kcSQghQEREROSGFK5OgIiIiKguLFSIiIjIbbFQISIiIrfFQoWIiIjcFgsVIiIiclssVIiIiMhtsVAhIiIit8VChYiIiNwWCxUiIiJyWyxUiIiIyG2xUCEiImoiOnbsCEmSamzJyckAgPLyciQnJyM4OBi+vr4YNmwYCgsLzdrIzc1FQkICfHx8EBISgldffRVVVVVmMWlpaejTpw/UajU6d+6M1atX18jlww8/RMeOHeHl5YXY2FgcPHjQ7Lg1uViDhQoREVETkZGRgYKCAtO2c+dOAMBTTz0FAJgyZQq++uorbNiwAXv27EF+fj6efPJJ0+uNRiMSEhJgMBiwb98+rFmzBqtXr8asWbNMMWfPnkVCQgIefPBBZGVlYfLkyXjuueewY8cOU8z69euRkpKC2bNn4/Dhw+jVqxfi4+NRVFRkiqkvF6sJIiIiapImTZok7rzzTiHLsiguLhaenp5iw4YNpuPHjx8XAER6eroQQoitW7cKhUIhdDqdKWbZsmVCo9GIiooKIYQQU6dOFXfffbfZeUaMGCHi4+NNP/fv318kJyebfjYajSI8PFykpqYKIYRVuVjLo+GljfPJsoz8/Hz4+flBkiRXp0NERG5MCIHS0lKEh4dDoWicgYPy8nIYDAaHtCWEqPHdplaroVarLb7OYDDgn//8J1JSUiBJEjIzM1FZWYm4uDhTTLdu3dC+fXukp6djwIABSE9PR8+ePREaGmqKiY+Px/jx43Hs2DHcc889SE9PN2ujOmby5Mmm82ZmZmLGjBmm4wqFAnFxcUhPTwcAq3KxVpMoVPLz8xEZGenqNIiIqAnJy8tDRESEw9stLy9Hpw6+0BUZHdKer68vrl+/brZv9uzZmDNnjsXXffnllyguLsZf/vIXAIBOp4NKpUJAQIBZXGhoKHQ6nSnm9iKl+nj1MUsxer0eN2/exLVr12A0GmuNycnJsToXazWJQsXPzw/Arf/pNBqNi7MhIiJ3ptfrERkZafrucDSDwQBdkRFnMztA42dfj42+VEanmPM1vt/q600BgE8++QSPPfYYwsPD7crB3TWJQqW6S0yj0bBQISIiqzT2VAGNn8LuQsXUVgO/386fP49vvvkG//nPf0z7wsLCYDAYUFxcbNaTUVhYiLCwMFPMb+/Oqb4T5/aY396dU1hYCI1GA29vbyiVSiiVylpjbm+jvlysxbt+iIiIbGAUskM2W6xatQohISFISEgw7YuJiYGnpyd27dpl2nfixAnk5uZCq9UCALRaLbKzs83uztm5cyc0Gg2ioqJMMbe3UR1T3YZKpUJMTIxZjCzL2LVrlynGmlys1SR6VIiIiNyNDAEZwu42GvwaWcaqVauQlJQED49fv8b9/f0xduxYpKSkICgoCBqNBhMnToRWqzVNXh08eDCioqLwzDPPYOHChdDpdJg5cyaSk5NNw03jxo3DkiVLMHXqVDz77LPYvXs3Pv/8c2zZssV0rpSUFCQlJaFv377o378/Fi1ahLKyMowZM8bqXKzFQoWIiMgGMmTY1h9i3kZDffPNN8jNzcWzzz5b49j7778PhUKBYcOGoaKiAvHx8Vi6dKnpuFKpxObNmzF+/HhotVq0atUKSUlJmDdvnimmU6dO2LJlC6ZMmYLFixcjIiICH3/8MeLj400xI0aMwKVLlzBr1izodDr07t0b27dvN5tgW18u1pKEEPaVg06g1+vh7++PkpISzlEhIiKLGvs7o7r9/BMRDplMG971Ar/fLLDrX3jBggWQJMl0b3VdiouLkZycjLZt20KtVuOuu+7C1q1b7Tk1ERGRSxmFcMhGltk89JORkYGPPvoI0dHRFuMMBgMeeeQRhISE4IsvvkC7du1w/vz5GvdWExERNSWumqPS0thUqFy/fh2JiYlYuXIl3nzzTYuxn376Ka5evYp9+/bB09MTwK2HKhERERHVx6ahn+TkZCQkJNRYYrc2mzZtglarRXJyMkJDQ9GjRw/Mnz8fRmPdK/pVVFRAr9ebbURERO5EhoDRzo09KvVrcI/KunXrcPjwYWRkZFgVf+bMGezevRuJiYnYunUrTp06hRdffBGVlZWYPXt2ra9JTU3F3LlzG5oaETVRxiojDu/KxjVdMYLDA9H7wR5QeihdnRaRRRz6cY4GFSp5eXmYNGkSdu7cCS8vL6teI8syQkJCsGLFCiiVSsTExODixYt4++236yxUZsyYgZSUFNPP1cshE1Hzs3vtd1j+8hpcKywx7QtqG4gXF43BA081bGEoImp+GlSoZGZmoqioCH369DHtMxqN2Lt3L5YsWYKKigooleZ/BbVt2xaenp5m+7t37w6dTgeDwQCVSlXjPNY8NZKImr7d//oeqX/+oMb+qwXX8OaI96BQvIxBwxq2OBSRszjirh3e9VO/Bs1Refjhh5GdnY2srCzT1rdvXyQmJiIrK6tGkQIAAwcOxKlTpyDLvy5qc/LkSbRt27bWIoWIWgZjlRHLX15jMWb5y2vMPjuI3InsoI0sa1Ch4ufnhx49ephtrVq1QnBwMHr06AEAGD16NGbMmGF6zfjx43H16lVMmjQJJ0+exJYtWzB//nwkJyc79kqIqEnJ+vYorumKLcYU5V7GsR9OOCchInJLDl9CPzc3FwrFr/VPZGQkduzYgSlTpiA6Ohrt2rXDpEmTMG3aNEefmoiakKv1FCkNjSNytuo7d+xtgyyzu1BJS0uz+DNw60mM+/fvt/dURNSMtG4X5NA4ImczilubvW2QZfY9pICIyEbRD0TdKkKk2o9LEtD2jhB0H9DFuYkRWYlzVJyDhQoRuYRSqcSLi//39NffFCuSdGtn8uJnzYaSiajl4ScAEbnMoCdjMfc/UxHaoY3Z/rA7QvHGpmmITYhxUWZE9ZMhwWjnJtfVpUgmDp9MS0TUEPc+0Q8DhsTg+P5fcLXgGoLDA9F9wF2QJH6Ak3uTxa3N3jbIMhYqRORyCoUCd9/b1dVpEJEbYqFCRERkg+rhG3vbIMtYqBAREdmAhYpzcDItERERuS32qBAREdlAFhJkYV+PiL2vbwlYqBAREdmAQz/OwaEfIiIiclvsUSEiIrKBEQoY7fx73+igXJozFipEREQ2EA6YoyI4R6VeLFSIiIhswDkqzsE5KkREROS22KNCRERkA6NQwCjsnKPCZ/3Ui4UKERGRDWRIkO0cmJDBSqU+HPohIiIit8UeFSIiIhtwMq1zsFAhIiKygWPmqHDopz4c+iEiIiK3xR4VIiIiG9yaTGvnQwk59FMvFipEREQ2kB2whD7v+qkfh36IiIjIbbFHhYiIyAacTOscLFSIiIhsIEPBBd+cgIUKERGRDYxCgtHOpx/b+/qWgHNUiIiIyG2xR4WIiMgGRgfc9WPk0E+9WKgQERHZQBYKyHZOppU5mbZeHPohIiIit8VChYiIyAbVQz/2bg118eJF/PnPf0ZwcDC8vb3Rs2dPHDp0yHRcCIFZs2ahbdu28Pb2RlxcHH755RezNq5evYrExERoNBoEBARg7NixuH79ulnMTz/9hEGDBsHLywuRkZFYuHBhjVw2bNiAbt26wcvLCz179sTWrVvNjluTS31YqBAREdlAxq93/ti6yQ0857Vr1zBw4EB4enpi27Zt+Pnnn/Huu+8iMDDQFLNw4UJ88MEHWL58OQ4cOIBWrVohPj4e5eXlppjExEQcO3YMO3fuxObNm7F371688MILpuN6vR6DBw9Ghw4dkJmZibfffhtz5szBihUrTDH79u3DqFGjMHbsWBw5cgRDhw7F0KFDcfTo0QblUh9JCPcfINPr9fD390dJSQk0Go2r0yEiIjfW2N8Z1e1/dDgG3r72TfW8eb0Kf+2Tiby8PLNc1Wo11Gp1jfjp06fjhx9+wHfffVdre0IIhIeH4+WXX8Yrr7wCACgpKUFoaChWr16NkSNH4vjx44iKikJGRgb69u0LANi+fTt+//vf48KFCwgPD8eyZcvw+uuvQ6fTQaVSmc795ZdfIicnBwAwYsQIlJWVYfPmzabzDxgwAL1798by5cutysUa7FEhIiKyQfWCb/ZuABAZGQl/f3/TlpqaWus5N23ahL59++Kpp55CSEgI7rnnHqxcudJ0/OzZs9DpdIiLizPt8/f3R2xsLNLT0wEA6enpCAgIMBUpABAXFweFQoEDBw6YYu6//35TkQIA8fHxOHHiBK5du2aKuf081THV57EmF2vwrh8iIiIbOGYJ/Vuvr61HpTZnzpzBsmXLkJKSgtdeew0ZGRl46aWXoFKpkJSUBJ1OBwAIDQ01e11oaKjpmE6nQ0hIiNlxDw8PBAUFmcV06tSpRhvVxwIDA6HT6eo9T325WIOFChERkYtpNBqrhqlkWUbfvn0xf/58AMA999yDo0ePYvny5UhKSmrsNF2CQz9EREQ2kCE5ZGuItm3bIioqymxf9+7dkZubCwAICwsDABQWFprFFBYWmo6FhYWhqKjI7HhVVRWuXr1qFlNbG7efo66Y24/Xl4s1WKgQERHZoHrox96tIQYOHIgTJ06Y7Tt58iQ6dOgAAOjUqRPCwsKwa9cu03G9Xo8DBw5Aq9UCALRaLYqLi5GZmWmK2b17N2RZRmxsrClm7969qKysNMXs3LkTXbt2Nd1hpNVqzc5THVN9HmtysQYLFSIiIhu4Yh2VKVOmYP/+/Zg/fz5OnTqFtWvXYsWKFUhOTgYASJKEyZMn480338SmTZuQnZ2N0aNHIzw8HEOHDgVwqwfm0UcfxfPPP4+DBw/ihx9+wIQJEzBy5EiEh4cDAJ5++mmoVCqMHTsWx44dw/r167F48WKkpKSYcpk0aRK2b9+Od999Fzk5OZgzZw4OHTqECRMmWJ2LNThHhYiIqIno168fNm7ciBkzZmDevHno1KkTFi1ahMTERFPM1KlTUVZWhhdeeAHFxcW47777sH37dnh5eZliPvvsM0yYMAEPP/wwFAoFhg0bhg8++MB03N/fH19//TWSk5MRExOD1q1bY9asWWZrrdx7771Yu3YtZs6ciddeew1dunTBl19+iR49ejQol/pwHRUiImpWnLWOysKMQQ5ZR2Vqv+/4/WYBe1SIiIhsIDvg6ckyZ2DUy65/oQULFpjGoKyxbt06SJLUoLEpIiIiarls7lHJyMjARx99hOjoaKviz507h1deeQWDBg2y9ZRERERuQxYKyHYu+Gbv61sCm/6Frl+/jsTERKxcudLsQUh1MRqNSExMxNy5c3HHHXfYckoiIiK3YoTkkI0ss6lQSU5ORkJCQo01/usyb948hISEYOzYsVbFV1RUQK/Xm21ERETU8jR46GfdunU4fPgwMjIyrIr//vvv8cknnyArK8vqc6SmpmLu3LkNTY2IiMhpOPTjHA36F8rLy8OkSZPw2WefWXUPdGlpKZ555hmsXLkSrVu3tvo8M2bMQElJiWnLy8trSJpERESNzghHDP9QfRrUo5KZmYmioiL06dPHtM9oNGLv3r1YsmQJKioqoFQqTcdOnz6Nc+fOYciQIaZ9sizfOrGHB06cOIE777yzxnnUanWdT44kIiKilqNBhcrDDz+M7Oxss31jxoxBt27dMG3aNLMiBQC6detWI37mzJkoLS3F4sWLERkZaWPaRERErsWhH+doUKHi5+dntjQuALRq1QrBwcGm/aNHj0a7du2QmpoKLy+vGvEBAQEAUGM/ERFRU2LLQwVra4Msc/jKtLm5uVAo+A9PRETNm4AE2c7biwVvT66X3YVKWlqaxZ9/a/Xq1faekoiIiFoIPuuHiIjIBhz6cQ4WKkRERDaQhQRZ2Dd0Y+/rWwKWckREROS22KNCRERkAyMUMNr59769r28JWKgQERHZgEM/zsFSjoiIiNwWe1SIiIhsIEMB2c6/9+19fUvAQoWIiMgGRiHBaOfQjb2vbwlYyhEREZHbYo8KERGRDTiZ1jlYqBAREdlAOODpyYIr09aLhQoREZENjJBgtPOhgva+viVgKUdERERuiz0qRERENpCF/XNMZOGgZJoxFipEREQ2kB0wR8Xe17cE/BciIiIit8UeFSIiIhvIkCDbORnW3te3BCxUiIiIbMCVaZ2DQz9ERETkttijQkREZANOpnUOFipEREQ2kOGAJfQ5R6VeLOWIiIjIbbFHhYiIyAbCAXf9CPao1IuFChERkQ349GTnYKFCRERkA06mdQ7+CxEREZHbYo8KERGRDTj04xwsVIiIiGzAJfSdg0M/RERE5LbYo0JERGQDDv04B3tUiIiIbFBdqNi7NcScOXMgSZLZ1q1bN9Px8vJyJCcnIzg4GL6+vhg2bBgKCwvN2sjNzUVCQgJ8fHwQEhKCV199FVVVVWYxaWlp6NOnD9RqNTp37ozVq1fXyOXDDz9Ex44d4eXlhdjYWBw8eNDsuDW5WIOFChERURNy9913o6CgwLR9//33pmNTpkzBV199hQ0bNmDPnj3Iz8/Hk08+aTpuNBqRkJAAg8GAffv2Yc2aNVi9ejVmzZplijl79iwSEhLw4IMPIisrC5MnT8Zzzz2HHTt2mGLWr1+PlJQUzJ49G4cPH0avXr0QHx+PoqIiq3OxliSEEA1+lZPp9Xr4+/ujpKQEGo3G1ekQEZEba+zvjOr247e9AM9WKrvaqiwzYMdjK5CXl2eWq1qthlqtrhE/Z84cfPnll8jKyqpxrKSkBG3atMHatWsxfPhwAEBOTg66d++O9PR0DBgwANu2bcPjjz+O/Px8hIaGAgCWL1+OadOm4dKlS1CpVJg2bRq2bNmCo0ePmtoeOXIkiouLsX37dgBAbGws+vXrhyVLlgAAZFlGZGQkJk6ciOnTp1uVi7XYo0JERGQDRw79REZGwt/f37SlpqbWed5ffvkF4eHhuOOOO5CYmIjc3FwAQGZmJiorKxEXF2eK7datG9q3b4/09HQAQHp6Onr27GkqUgAgPj4eer0ex44dM8Xc3kZ1THUbBoMBmZmZZjEKhQJxcXGmGGtysRYn0xIREblYbT0qtYmNjcXq1avRtWtXFBQUYO7cuRg0aBCOHj0KnU4HlUqFgIAAs9eEhoZCp9MBAHQ6nVmRUn28+pilGL1ej5s3b+LatWswGo21xuTk5JjaqC8Xa7FQISIisoGA/eugVM+90Gg0Vg1TPfbYY6b/jo6ORmxsLDp06IDPP/8c3t7eduXirjj0Q0REZANX3PXzWwEBAbjrrrtw6tQphIWFwWAwoLi42CymsLAQYWFhAICwsLAad95U/1xfjEajgbe3N1q3bg2lUllrzO1t1JeLtVioEBER2cAdCpXr16/j9OnTaNu2LWJiYuDp6Yldu3aZjp84cQK5ubnQarUAAK1Wi+zsbLO7c3bu3AmNRoOoqChTzO1tVMdUt6FSqRATE2MWI8sydu3aZYqxJhdrceiHiIioiXjllVcwZMgQdOjQAfn5+Zg9ezaUSiVGjRoFf39/jB07FikpKQgKCoJGo8HEiROh1WpNd9kMHjwYUVFReOaZZ7Bw4ULodDrMnDkTycnJpnkx48aNw5IlSzB16lQ8++yz2L17Nz7//HNs2bLFlEdKSgqSkpLQt29f9O/fH4sWLUJZWRnGjBkDAFblYi0WKkRERDZwxcq0Fy5cwKhRo3DlyhW0adMG9913H/bv3482bdoAAN5//30oFAoMGzYMFRUViI+Px9KlS02vVyqV2Lx5M8aPHw+tVotWrVohKSkJ8+bNM8V06tQJW7ZswZQpU7B48WJERETg448/Rnx8vClmxIgRuHTpEmbNmgWdTofevXtj+/btZhNs68vFWlxHhYiImhVnraNy36ZkeLSq/e4ca1WVVeD7P3zI7zcLOEeFiIiI3BaHfoiIiGwghARh59CPva9vCVioEBER2UCGZPc6Kva+viWwa+hnwYIFkCQJkydPrjNm5cqVGDRoEAIDAxEYGIi4uLgaT1gkIiIiqo3NhUpGRgY++ugjREdHW4xLS0vDqFGj8O233yI9PR2RkZEYPHgwLl68aOupiYiIXM4d1lFpCWwqVK5fv47ExESsXLkSgYGBFmM/++wzvPjii+jduze6deuGjz/+2LQwDBERUVNVPUfF3o0ss6lQSU5ORkJCQo2nK1rjxo0bqKysRFBQUJ0xFRUV0Ov1ZhsRERG1PA2eTLtu3TocPnwYGRkZNp1w2rRpCA8Pt1jkpKamYu7cuTa1T0RE5AyuWPCtJWpQj0peXh4mTZqEzz77DF5eXg0+2YIFC7Bu3Tps3LjR4utnzJiBkpIS05aXl9fgcxERETUmDv04R4N6VDIzM1FUVIQ+ffqY9hmNRuzduxdLlixBRUUFlEplra995513sGDBAnzzzTf1TsBVq9WmZw4QERG5I+GAHhUWKvVrUKHy8MMPIzs722zfmDFj0K1bN0ybNq3OImXhwoV46623sGPHDvTt29f2bImIiKhFaVCh4ufnhx49epjta9WqFYKDg037R48ejXbt2iE1NRUA8Pe//x2zZs3C2rVr0bFjR+h0OgCAr68vfH19HXENRERETicA2Pu0PLd/2J4bcPizfnJzc1FQUGD6edmyZTAYDBg+fDjatm1r2t555x1Hn5qIiMhpqlemtXcjy+xeQj8tLc3iz+fOnbP3FERERNRC8Vk/RERENuBDCZ2DhQoREZENZCFB4joqjc7hc1SIiIiIHIU9KtRojFVGXCm4Bk+VBwJDA1ydDhGRQwnhgLt+eNtPvViokMMZyg1Yt+BLbFq6HSWXSwEAd/bqgFEznsQDf7rXxdkRETkG56g4BwsVcihDRSWmP/omjn2fA1n+9U+FM9m5eHPk+yg4W4SR04a6LkGyiRACx49dxL7vTqKivBJ3dA7B7+Luhre3ytWpEVEzx0KFHGrz8q9x9LsciN/0Z4r/FS2fvPYZBg2LRbvObV2RHtlAX3IDs2dswNEf86BUKiBJQFWVjGWLd2L67Cdw76Curk6RyCXYo+IcnExLDrVp6XYIC2stKhQKbPt4lxMzInsIITDz1fX4+egFAIDRKKOqSgYAlJcbMPf1L3D82EVXpkjkMtVPT7Z3I8tYqJDDCCFw8RedxTWhZaOM88cvOC8pskvW4XM4fuwiZGPNN7W602zdP35wclZE7qF6Mq29G1nGQoUcRpIkqOuZs6BQKuDj5+2kjMhe36XlQKms+2NCNgrs/+EXGAxVTsyKiFoSFirkUA/86V4oPSx9sckYNGyAEzMie5TfMFgcygMAWRaoZKFCLdCtHhHJzs3VV+H+WKiQQw1/eQgUSgUkRc1xV6WHAh3ujoR2SF8XZEa2iOzQut7HuwYGtYK3j9o5CRG5EfuLFPsn47YELFTIoTr1aI83N7+GVv4+AAClpxJKDyUA4I5eHfH3r/9m+pncX3xCL0hS3R+kCoWEIX+MgaKWwpSIyBF4ezI5XJ+He2LdhY+wd8N+nMw8DZXaE/0T+iD6/iiLX3rkfoKCfZE8JR4fvLMNCoVktjaOQiHhzi6hGD6SQ3nUMgnU2+FoVRtkGQsVahRqbzUeGf0AHhn9gKtTITsN+WMMWrfxw9o13yPn53wAgK+vFx4f2gdPJ90Hbx8u+kYtE9dRcQ4WKkRUL+19d0F7310oKb6BiopKBAX7woNDeETkBCxUiMhq/gE+rk6ByH1w7McpWKgQERHZwhF37XDop14sVIiIiGzgiJVluY5K/Xh7MhEREbkt9qgQERHZgHf9OAcLFSIiIlsIyf45JixU6sWhHyIiInJb7FEhIiKyASfTOgcLFSIiIltwHRWn4NAPERERuS32qBAREdmAd/04B3tUiIjI5U4VX0F6fi7OFF91dSoNI+zc7LBgwQJIkoTJkyeb9pWXlyM5ORnBwcHw9fXFsGHDUFhYaPa63NxcJCQkwMfHByEhIXj11VdRVVVlFpOWloY+ffpArVajc+fOWL16dY3zf/jhh+jYsSO8vLwQGxuLgwcPmh23JhdrsFAhIiKXSc/PRcK/1yDu808xavN6PPT5J/jDxn8gQ3fB1am5tYyMDHz00UeIjo422z9lyhR89dVX2LBhA/bs2YP8/Hw8+eSTpuNGoxEJCQkwGAzYt28f1qxZg9WrV2PWrFmmmLNnzyIhIQEPPvggsrKyMHnyZDz33HPYsWOHKWb9+vVISUnB7NmzcfjwYfTq1Qvx8fEoKiqyOhdrSUK4/5xjvV4Pf39/lJSUQKPRuDodIiJygO8unMNftn0BIQD5tu4FhSRBAQn/TPgTBoRHNrjdxv7OqG4/8qPZUHh72dWWfLMceX+d26Bcr1+/jj59+mDp0qV488030bt3byxatAglJSVo06YN1q5di+HDhwMAcnJy0L17d6Snp2PAgAHYtm0bHn/8ceTn5yM0NBQAsHz5ckybNg2XLl2CSqXCtGnTsGXLFhw9etR0zpEjR6K4uBjbt28HAMTGxqJfv35YsmTJreuQZURGRmLixImYPn26VblYiz0qRETkdLIQeP27ryELYVakVB+ThcDM77+GW/8tbe+wz23DP3q93myrqKio87TJyclISEhAXFyc2f7MzExUVlaa7e/WrRvat2+P9PR0AEB6ejp69uxpKlIAID4+Hnq9HseOHTPF/Lbt+Ph4UxsGgwGZmZlmMQqFAnFxcaYYa3KxFgsVIiJyukO6i8gtLalzmoYMgVPFV/HTJZ1T82oYyUEbEBkZCX9/f9OWmppa6xnXrVuHw4cP13pcp9NBpVIhICDAbH9oaCh0Op0p5vYipfp49TFLMXq9Hjdv3sTly5dhNBprjbm9jfpysRbv+iEiIqe7UFpiXdx1PXqFtG3kbFwvLy/PbOhHrVbXGjNp0iTs3LkTXl72DTk1JexRISIipwv08nZonEs4cOhHo9GYbbUVKpmZmSgqKkKfPn3g4eEBDw8P7NmzBx988AE8PDwQGhoKg8GA4uJis9cVFhYiLCwMABAWFlbjzpvqn+uL0Wg08Pb2RuvWraFUKmuNub2N+nKxFgsVIiJyuoHtOiBAbblXoI13K/QPi3BSRjZwYKFijYcffhjZ2dnIysoybX379kViYqLpvz09PbFr1y7Ta06cOIHc3FxotVoAgFarRXZ2ttndOTt37oRGo0FUVJQp5vY2qmOq21CpVIiJiTGLkWUZu3btMsXExMTUm4u1OPRDREROp1Iq8Wq/QXj9+511xkyLvR8eCv49Xc3Pzw89evQw29eqVSsEBweb9o8dOxYpKSkICgqCRqPBxIkTodVqTXfZDB48GFFRUXjmmWewcOFC6HQ6zJw5E8nJyaZenHHjxmHJkiWYOnUqnn32WezevRuff/45tmzZYjpvSkoKkpKS0LdvX/Tv3x+LFi1CWVkZxowZAwDw9/evNxdrsVAhIiKXSIzqjSpZxt8P7sWNqkooJAmyEGjlqcJrsQ9g+F096m/ElYR0a7O3DQd6//33oVAoMGzYMFRUVCA+Ph5Lly41HVcqldi8eTPGjx8PrVaLVq1aISkpCfPmzTPFdOrUCVu2bMGUKVOwePFiRERE4OOPP0Z8fLwpZsSIEbh06RJmzZoFnU6H3r17Y/v27WYTbOvLxVpcR4WIiFzqRqUBO8+fxqUb1xHi44tHOnaGt4enze05ax2ViCVzHbKOyoUJs/n9ZgF7VIiIyKV8PFV4onN3V6dBboqFChERkS0c8Lweu1/fArBQISIisoUbzlFpjjidmoiIiNwWe1SIiIhsIIlbm71tkGV29agsWLAAkiRh8uTJFuM2bNiAbt26wcvLCz179sTWrVvtOS0REZHrOXnBt5bK5kIlIyMDH330EaKjoy3G7du3D6NGjcLYsWNx5MgRDB06FEOHDjV7fDQREVGTUz1Hxd6NLLKpULl+/ToSExOxcuVKBAYGWoxdvHgxHn30Ubz66qvo3r073njjDfTp0wdLliyxKWEiIiJqOWwqVJKTk5GQkIC4uLh6Y9PT02vExcfHIz09vc7XVFRUQK/Xm21ERERuhUM/TtHgybTr1q3D4cOHkZGRYVW8TqczW1IXAEJDQ6HT6ep8TWpqKubOndvQ1IiIiJyH66g4RYN6VPLy8jBp0iR89tln8PKyb9lgS2bMmIGSkhLTlpeX12jnIiIiIvfVoB6VzMxMFBUVoU+fPqZ9RqMRe/fuxZIlS1BRUQGlUmn2mrCwMBQWFprtKywsRFhYWJ3nUavVpqc4EhERuSX2qDhFg3pUHn74YWRnZyMrK8u09e3bF4mJicjKyqpRpACAVqvFrl27zPbt3LkTWq3WvsyJiIhciXf9OEWDelT8/PzQo4f5Y7dbtWqF4OBg0/7Ro0ejXbt2SE1NBQBMmjQJDzzwAN59910kJCRg3bp1OHToEFasWOGgSyAiIqLmyuFL6Ofm5qKgoMD087333ou1a9dixYoV6NWrF7744gt8+eWXNQoeIiKipqR6ZVp7N7LM7iX009LSLP4MAE899RSeeuope09FRETkPjhHxSn4UEIiIiJyWyxUiIiIyG3x6clEREQ2kOCApyc7JJPmjYUKEVmtUjai0miEt4cnJIkfsdTCOeL2Yt6eXC8WKkRUr4zCPCw9mo60i2cgAIS30iCpawz+0j0GaiU/Roio8fAThogs2nT2Z0z6bhMUkmS6QSG/TI8Fh79F2sXTWB33JxYr1DLxrh+n4GRaIqrTtfKbePmHLRAAjML8E1UA2F+Yi1XHD7kkNyKX49OTnYKFChHV6d9nsmGU5TqPCwBrcjIhBD9tiahxsL+WiOr089UiSBIs/tVXcKMU1ysN8FPxQaLUsjhiZVmuTFs/FipEVCcvDw9IsFypSAA8a3kgKVGzxzkqTsGhHyKq0yMRXVAl6h76UUoSBoV3ghcn0xJRI2GhQkR1uj+8E7oGtIayjjVTZCEwvscAJ2dF5CY4mdYpWKgQUZ2UCgX+L24EOvsHAwA8JAUUkgQFJHhICrwzMAHasA4uzpLINfj0ZOdgfy0RWRTq44etjz+LtPwz2Jn7C8qNVega2AZP3dkTrb1buTo9ImrmWKgQUb2UCgUejuiMhyM6uzoVIvfBJfSdgoUKERGRLXjXj1OwUCEiIrIB11FxDk6mJSIiIrfFHhUiIiJbcOjHKVioEBER2cIRtxezUKkXh36IiIjIbbFHhYiIyBYc+nEKFipERES2YKHiFBz6ISIiIrfFHhUiIiIbcB0V52CPChEREbktFipERERNxLJlyxAdHQ2NRgONRgOtVott27aZjpeXlyM5ORnBwcHw9fXFsGHDUFhYaNZGbm4uEhIS4OPjg5CQELz66quoqqoyi0lLS0OfPn2gVqvRuXNnrF69ukYuH374ITp27AgvLy/Exsbi4MGDZsetycUaLFSIiIhsIRy0NUBERAQWLFiAzMxMHDp0CA899BCeeOIJHDt2DAAwZcoUfPXVV9iwYQP27NmD/Px8PPnkk6bXG41GJCQkwGAwYN++fVizZg1Wr16NWbNmmWLOnj2LhIQEPPjgg8jKysLkyZPx3HPPYceOHaaY9evXIyUlBbNnz8bhw4fRq1cvxMfHo6ioyBRTXy7WkoQQbj9Cptfr4e/vj5KSEmg0GlenQ0REbqyxvzOq2+88fT6UXl52tWUsL8epBa/ZlWtQUBDefvttDB8+HG3atMHatWsxfPhwAEBOTg66d++O9PR0DBgwANu2bcPjjz+O/Px8hIaGAgCWL1+OadOm4dKlS1CpVJg2bRq2bNmCo0ePms4xcuRIFBcXY/v27QCA2NhY9OvXD0uWLAEAyLKMyMhITJw4EdOnT0dJSUm9uViLPSpERES2clBvil6vN9sqKirqPbXRaMS6detQVlYGrVaLzMxMVFZWIi4uzhTTrVs3tG/fHunp6QCA9PR09OzZ01SkAEB8fDz0er2pVyY9Pd2sjeqY6jYMBgMyMzPNYhQKBeLi4kwx1uRiLRYqRERELhYZGQl/f3/TlpqaWmdsdnY2fH19oVarMW7cOGzcuBFRUVHQ6XRQqVQICAgwiw8NDYVOpwMA6HQ6syKl+nj1MUsxer0eN2/exOXLl2E0GmuNub2N+nKxFm9PJiIisoUDF3zLy8szG/pRq9V1vqRr167IyspCSUkJvvjiCyQlJWHPnj12JuK+WKgQOdnZ7PO48IsOrfx90HNQN3iqPF2dEhHZwJHrqFTfxWMNlUqFzp07AwBiYmKQkZGBxYsXY8SIETAYDCguLjbrySgsLERYWBgAICwsrMbdOdV34twe89u7cwoLC6HRaODt7Q2lUgmlUllrzO1t1JeLtTj0Q+Qkvxw+gxf7TsMLvV7BvOHvYNoj8zAq4q/4atmO+l/cSERVLoThIETlL2gC8+qJqBayLKOiogIxMTHw9PTErl27TMdOnDiB3NxcaLVaAIBWq0V2drbZ3Tk7d+6ERqNBVFSUKeb2NqpjqttQqVSIiYkxi5FlGbt27TLFWJOLtdijQuQEZ7PPY8r9s1BZUWm2v+RyKT5I/hg3r5fjT68+4bR8ROVRCP18oPLQrzs97gL8XoWkfsBpeRA1aS541s+MGTPw2GOPoX379igtLcXatWuRlpaGHTt2wN/fH2PHjkVKSgqCgoKg0WgwceJEaLVa0102gwcPRlRUFJ555hksXLgQOp0OM2fORHJysmm4ady4cViyZAmmTp2KZ599Frt378bnn3+OLVu2mPJISUlBUlIS+vbti/79+2PRokUoKyvDmDFjAMCqXKzFQoXICT6e8RkqKyohG+Vaj6/62zo8OvYhaIL8Gj0XUXkU4sooAOZFE6p+gbj2AhDw/yB5DW70PIiaOlcsoV9UVITRo0ejoKAA/v7+iI6Oxo4dO/DII48AAN5//30oFAoMGzYMFRUViI+Px9KlS02vVyqV2Lx5M8aPHw+tVotWrVohKSkJ8+bNM8V06tQJW7ZswZQpU7B48WJERETg448/Rnx8vClmxIgRuHTpEmbNmgWdTofevXtj+/btZhNs68vF+n+jJtDfy3VUqCm7VlSCP7V9zuJfTpIkYeKSsRgyPr7uIAeRr4wAKn8EUFvRJAGKIEht9kKSOHeGmiZnraNy1yvzoVTbuY5KRTlOvmPfOirNHeeoEDWya7riert3FR4KXL54tdFzEVVngcojqL1IAQAByFeAiu8aPReiJs8FK9O2RBz6cQBZCBTeKIUAEObjB4UkuTolciMBIfX/lSQbZQSGBTR+MsZ8K4IkwHih0VMhavJcMEelJWKhYgchBP558ghWHDuAvOslAG4VKs9F9cOYbn2hVLDDioCgsEDc83BP/Jh2rM45KgqFAr8bcW/jJ6MIsCJIAIrAxs6EiMgqLFRsJITAzAM78NnJLNzef6K7UYq3Du3GT5cLsGjQH9i7QgCAsamJmDJoJoQQEHLNP6Gefu1JBLTxb/xEPKIAZXvAmIe6/5TzAtQPNn4uNsq/UYzPzx9Emu4EKmUjegVFYmTH/ogOjHR1atTCuGIybUvEP/ltlK47j89OZgGo+XEvAGw6dxxf5510dlrkprr2vRMLd85C+J3mCx15+3phbGoinpn9lFPykCQJkt9UWOpvlnwnQFL4OiWfhkq/dAp/+PYDrD71A85cv4S8G1ex7WI2/vz9Snx6ivNqyMk4R8Up2KNio89OZkEpKWAUtXflKyUJ/zxxBI+27+rkzMhd9bivO1blLMaxfSeQf+rWyrQxg3vBy6fupbIbg+Q1GPB/B0I/DxB63Pp7RQaghuSbDLR63qn5WOtaRRkmZfwLlbIR4rZP9+rfwUXHd6K7f1to23R2VYrU0nCOilOwULHRieJLdRYpAGAUAieLLzsxI2oKJElCj4Hd0GNgN9fm4f0HwCseqNh9a4KtIghQx0FSNP46LrbamHcYFcYqsyLldkpI+MfpdBYqRM1Mg4Z+li1bhujoaNMzCbRaLbZt22bxNYsWLULXrl3h7e2NyMhITJkyBeXl5XYl7Q58Pev/K9jXU+WETIhsI0lqSF6PQWo1FpL3H926SAGAQ1fO1VmkAIARAhlXzjoxI2rpqueo2LuRZQ3qUYmIiMCCBQvQpUsXCCGwZs0aPPHEEzhy5AjuvvvuGvFr167F9OnT8emnn+Lee+/FyZMn8Ze//AWSJOG9995z2EW4wpCO3fHj5fw6PzYVkoQ/dIpyak5EzVkTWJuSWhoO/ThFgwqVIUOGmP381ltvYdmyZdi/f3+thcq+ffswcOBAPP300wCAjh07YtSoUThw4IDF81RUVKCiosL0s16vb0iaTjG8c098dOwArpSXwfibD1ClJMHXU42n7+rtmuSImqF+rTth36XTFod++gZ3dG5SRNTobL7rx2g0Yt26dSgrK6vzSYj33nsvMjMzTY+UPnPmDLZu3Yrf//73FttOTU2Fv7+/aYuMdL/bDv1VXlgX/zQifQMAAB6SAh7SrX/OEG9f/GvwKIR4u+edE0RN0dDIPlArPSCh9lv+jRAYfacT1qIh+h8O/ThHgyfTZmdnQ6vVory8HL6+vti4caPp0dC/9fTTT+Py5cu47777IIRAVVUVxo0bh9dee83iOWbMmIGUlBTTz3q93i2LlTs0Qdj1xPPYk38G6bpcCAj0DYlAXEQXeHCxNyKHClK3wuJ+ozDx4FpUCSPk//VkVt99N6lbHCfSknNx6McpGlyodO3aFVlZWSgpKcEXX3yBpKQk7Nmzp9ZiJS0tDfPnz8fSpUsRGxuLU6dOYdKkSXjjjTfwt7/9rc5zqNVq0+Om3Z1SocBDEZ3xUAQ/IIkam7ZNZ/z3wYn4/FwG0gpzbi34FhiJkZ1i0YsLvhE1S3Y/PTkuLg533nknPvrooxrHBg0ahAEDBuDtt9827fvnP/+JF154AdevX4fCyl4HPj2ZiIis5aynJ3d/0TFPTz6+lE9PtsTudVRkWTab+Hq7Gzdu1ChGlEolAM7gJyKipk3632ZvG2RZgwqVGTNm4LHHHkP79u1RWlqKtWvXIi0tDTt27AAAjB49Gu3atUNqaiqAW3cJvffee7jnnntMQz9/+9vfMGTIEFPBQkRERFSXBhUqRUVFGD16NAoKCuDv74/o6Gjs2LEDjzzyCAAgNzfXrAdl5syZkCQJM2fOxMWLF9GmTRsMGTIEb731lmOvgoiIyNk4mdYp7J6j4gyco0JERNZy1hyVu8c5Zo7KseWco2IJn/VDRERkC/aoOAUX+yAiIiK3xR4VIiIiW7FHpNGxUCEiIrKBI5bA5xL69ePQDxEREbkt9qgQERHZgpNpnYKFChERkQ049OMcHPohIiIit8UeFSIiIltw6McpWKgQERHZgEM/zsGhHyIiInJb7FEhIiKyBYd+nIKFChERkS1YqDgFCxUiIiIbcI6Kc3COChEREbkt9qgQERHZgkM/TsFChYiIyAaSEJCEfZWGva9vCTj0Q0RERG6LPSpE1KTIQkZO6RHk6LNgFEZE+tyJewIGQq30cnVq1NJw6McpWKgQUZNxpaIIH5+dj0sVBVBACQA4cHUXvsr/P4zumIKufr1cnCG1JLzrxzk49ENETUKlbMBHZ97AlYpCAIAMI2QYAQAGuQKfnl0IXfkFV6ZIRI2AhQoRNQk/FqfjqqEIMuQaxwQEhJCx99IWF2RGLZZw0EYWsVAhoiYhuyQDEqQ6j8uQ8VPxfidmRC1d9dCPvRtZxkKFiJoEg1wOUc+fn5XC4KRsiMhZWKgQUZPQ1rs9FBY+siRICFW3c2JG1OK5YOgnNTUV/fr1g5+fH0JCQjB06FCcOHHCLKa8vBzJyckIDg6Gr68vhg0bhsLCQrOY3NxcJCQkwMfHByEhIXj11VdRVVVlFpOWloY+ffpArVajc+fOWL16dY18PvzwQ3Ts2BFeXl6IjY3FwYMHG5xLfVioEFGTMCAortb5KdUEBAa2ftSJGVFL54qhnz179iA5ORn79+/Hzp07UVlZicGDB6OsrMwUM2XKFHz11VfYsGED9uzZg/z8fDz55JOm40ajEQkJCTAYDNi3bx/WrFmD1atXY9asWaaYs2fPIiEhAQ8++CCysrIwefJkPPfcc9ixY4cpZv369UhJScHs2bNx+PBh9OrVC/Hx8SgqKrI6F+v+nYX7L4un1+vh7++PkpISaDQaV6dDjSD3ajH+++NxXL5ehhA/XzzRqzsiAv1dnRa5mW+L/ostBWshQfrNMJCEKE0fJHV8GUpJ6bL8yD009ndGdfsxf3oLSpV96/cYDeXI/Px1m3O9dOkSQkJCsGfPHtx///0oKSlBmzZtsHbtWgwfPhwAkJOTg+7duyM9PR0DBgzAtm3b8PjjjyM/Px+hoaEAgOXLl2PatGm4dOkSVCoVpk2bhi1btuDo0aOmc40cORLFxcXYvn07ACA2Nhb9+vXDkiVLAACyLCMyMhITJ07E9OnTrcrFGuxRIZeSZYE3t36LwR+swvK9B/DvI8fw4Z79eGTxp/j7jr2QZbevo8mJHgx5An/p+AoifTqb9gV4tsbjbROR1DGFRQo1WXq93myrqKiw6nUlJSUAgKCgIABAZmYmKisrERcXZ4rp1q0b2rdvj/T0dABAeno6evbsaSpSACA+Ph56vR7Hjh0zxdzeRnVMdRsGgwGZmZlmMQqFAnFxcaYYa3KxBhd8I5dakpaOfx7MAgAYhQBu6+BblZ4Jf28vjLu/v4uyI3fUw78fevj3Q7nxBoyiCj5KP0hS3XcDETUmR921ExkZafbz7NmzMWfOHIuvkWUZkydPxsCBA9GjRw8AgE6ng0qlQkBAgFlsaGgodDqdKeb2IqX6ePUxSzF6vR43b97EtWvXYDQaa43JycmxOhdrsFAhl7leYcCn6ZkWY1b+kIG/aPvAy5P/q5I5L6WPq1Oglu43f1zZ3AaAvLw8s6EftVpd70uTk5Nx9OhRfP/99/bl4OY49EMuk34mF+WVVRZjyioMOHguz0kZERG5hkajMdvqK1QmTJiAzZs349tvv0VERIRpf1hYGAwGA4qLi83iCwsLERYWZor57Z031T/XF6PRaODt7Y3WrVtDqVTWGnN7G/XlYg0WKuQyNw2VVsXdsDKOiMiZXHHXjxACEyZMwMaNG7F792506tTJ7HhMTAw8PT2xa9cu074TJ04gNzcXWq0WAKDVapGdnW12d87OnTuh0WgQFRVlirm9jeqY6jZUKhViYmLMYmRZxq5du0wx1uRiDfank8t0Dgm2Lq6NdXFERE7lgqcnJycnY+3atfjvf/8LPz8/01wPf39/eHt7w9/fH2PHjkVKSgqCgoKg0WgwceJEaLVa0102gwcPRlRUFJ555hksXLgQOp0OM2fORHJysqknZ9y4cViyZAmmTp2KZ599Frt378bnn3+OLVt+fUxFSkoKkpKS0LdvX/Tv3x+LFi1CWVkZxowZY8qpvlyswUKFXCaqbQiiwkKQU3gJci3jvEpJQnS7MKsLGiJquvRXSlF8SY+AEA00QX6uTsdtLVu2DADwu9/9zmz/qlWr8Je//AUA8P7770OhUGDYsGGoqKhAfHw8li5daopVKpXYvHkzxo8fD61Wi1atWiEpKQnz5s0zxXTq1AlbtmzBlClTsHjxYkRERODjjz9GfHy8KWbEiBG4dOkSZs2aBZ1Oh969e2P79u1mE2zry8UaXEeFXOqE7hKe/nQ9yiurbt318z9KhQQflQrrxo7AnexRIWq2Tv94Dqv/tg4HthyGEAKSQoJ2SF+MeXMUOt4dWX8DtXDWOir9/vgmPDztW0elqrIcGRtn8vvNAs5RIZfqGtYGX7yQiISe3eChuPW/o6dSgT9Ed8e/X3iaRQpRM5Zz8Be8dO/rOLjtCKr/ZhaywP7NmZg4YAZOHTnr4gzrwacnOwWHfsjlOrUOxMInH8W8IXEouVkOf28v3o5M1MwJIfDuc8tQVVFZY2FH2SjDUF6J915YjqUZf3dRhuQu+G1AbiH/tA7f/GMvruqKERweiLhn7kfbTqH1v5CImqSTh07j3NG6lx6QjTJ+yTyDMz+dxx3RHZyYmfVsuWuntjbIMhYq5FKyLGPp5FX475LtUCgVkBQShCzwf3M/x/ApQ/D8wj9DoeAIJVFzk3ci36q4Cyfz3bZQceSCb1Q3fgOQS/3fnM/x3w9vPeBKNsowVhohG2VAAF+89xXWLfjStQkSUaPw0XhbGee+KxC7Yh2VloiFCrnMjdKb+OK9zRYnk61f+CUqblr3cC4CKuUbKLp5DJfKc2CUDa5Oh6hOfeKi4e1n+Y4Z38BWiH4gykkZkbvi0A+5zOFvfkLFDctFyA39TfyY9jP6P3aPk7Jqmqrkchy6vAI5JV+hSpQDAFQKP/QIfAq9g56BQuKvOrkXLx81Rk1/Ep++vrbOmD/PHA6V2tOJWTWQCxZ8a4n46UUuU15mXU9JeVl5I2fStBlFJbZffBmFN49CQDbtN8ilOHxlFYorzuHBtnP4hGFyOyOnD0V5WTnW/f1LCCGgVCpgNMpQKCQkzhyOJycnuDpFiziZ1jkaNPSzbNkyREdHmx6apNVqsW3bNouvKS4uRnJyMtq2bQu1Wo277roLW7dutStpah6sXcypQ1RE/UEt2Cn9Duhu/mRWpPxK4Mz13ci/Yfkp1USuIEkSxrw5Cmtzl+OFhc/gyUkJGPdOEv6V9xGemfUUi2sC0MAelYiICCxYsABdunSBEAJr1qzBE088gSNHjuDuu++uEW8wGPDII48gJCQEX3zxBdq1a4fz588jICDAUfk3ayWGQhy6+iWOl6ShUi5HoCoc9wQ9jh4Bj0DZDLryO9/TCZ3v6YQzP52/NYH2NxRKBbr174wOUbatTtlSHC/+LwAJdfUhS1Aip2QT2rXq69S8iKwV3DYQw1OGuDqNhuNdP07RoG+7IUPM/0d66623sGzZMuzfv7/WQuXTTz/F1atXsW/fPnh63hpn7Nixo+3ZtiAFN09g/fkZqJQrTH8pF1WcxY6CD5Cj34thkXPhoVC5OEv7vfLpi5hy/99QccNgVqwoPBTw9vVCyspxLsyuaSitzIelgW4BI/SVF5yXEFELwaEf57D5rh+j0Yh169ahrKyszsc1b9q0CVqtFsnJyQgNDUWPHj0wf/58GI1Gi21XVFRAr9ebbS2JLIzYmPeGWZFyy63/o3PLfsT+y5+7JjkHu7NXRyzN+DseHDkQSg8lAMBD5YGHnx6EpRl/Z2+KFdTK+p4PIkGt9HdKLkREjtbg8YPs7GxotVqUl5fD19cXGzduRFRU7bePnTlzBrt370ZiYiK2bt2KU6dO4cUXX0RlZSVmz55d5zlSU1Mxd+7chqbWbJwq3Y/rVVfqPC4gcOTaV9C2GdkshoAi7grH9H+8hCkr/orSa2XwC2wFtbfa1Wk1GV008Th8ZVUdc1QAQKCLJr6OY0RkM9714xQN7lHp2rUrsrKycODAAYwfPx5JSUn4+eefa42VZRkhISFYsWIFYmJiMGLECLz++utYvny5xXPMmDEDJSUlpi0vr+5llpujgpsnoIDSYsxNox76yiInZeQcam81WocHsUhpoG7+T8BLGQCplv9nJCgRoOqITr4PuiAzouaNC745R4P/HFepVOjcuTMAICYmBhkZGVi8eDE++uijGrFt27aFp6cnlMpfP0C7d+8OnU4Hg8EAlar2ORZqtRpqdcv9slJIlosUU1w9xQy1DN4egXg88kPsyp+Jq4bTkKDArX43gVDvnnio7Vx4KFru7xMRNW12jxvIsoyKitrXwxg4cCDWrl0LWZZNz2s5efIk2rZtW2eRQkDHVn2QfnmdhQgJAZ5h0HiGOC0ncm/+qgj8scMqFJZno+jmUUiSAuE+fRGs7uzq1IiaL1nc2uxtgyxqUKEyY8YMPPbYY2jfvj1KS0uxdu1apKWlYceOHQCA0aNHo127dkhNTQUAjB8/HkuWLMGkSZMwceJE/PLLL5g/fz5eeuklx19JMxLh0wMhXnfiUvnZOtfGiG3NNQbInCRJCPOORph3tKtTIWoZOEfFKRpUqBQVFWH06NEoKCiAv78/oqOjsWPHDjzyyCMAgNzcXLMn3UZGRmLHjh2YMmUKoqOj0a5dO0yaNAnTpk1z7FU0M5IkYVjkHKw7Px3XDBchQYKAgAJKyDCiX/AwRAc86uo0HU4IgQq5EmqFJ4swInJ7Ehxwe7JDMmneJCHcf7UZvV4Pf39/lJSUQKOp71bM5qNKNuCE/nvk6PeiQi5DsCoSvQIfQ5h3F1en5lC6m8X4vzN7sfniYdw0GuCjVGNIRB8k3fEA2ni1nPebiByjsb8zqtsfGDcXHh6WH6xYn6qqcvzwzewW9/3WEE3/3tZmzEOhwt0BD+HugIdcnUqjOV92GWPTl+N6VTmM4tYw1w1jBb7IPYCdBdn4VDsO7XyCXJwlEVEtuDKtU9i84BuRI8z76QuzIqWaUcgoqbyBN7P/46LMiIgs4+3JzsFChVzmVKkOPxXn1ihSqhmFjENXzyC37LKTMyMiInfBQoVc5qS+wKq4U6W6Rs6EiMgGwkEbWcQ5KuQyXkpPq+JUCv5vSkTuRxICkp1zTOx9fUvQIr4ByquqsOPcLzh57TJ8PDwR37ELOgcGO6RtWRhQWLYLV8sPABAIVMcgtFU8lFwJtF79gu+Ep0KJSrnuh1R6KTzRJ6iTE7MiIiJ30uwLlW/zzmDyt1tQUlEOD4UCQgi8feg7DO7QGe8/mIBWnravkFtqOIlM3V9RYSyE9L9/ygulG5Bz9e/oE7YMAWouvGWJn6c3/tRei7Xnvq+191MC8HSngfDxYNFHRG5I/t9mbxtkUbOeo5JVVIDnd2yEvqIcAFAlyzD+r5vtm9zTSN61yea2DcZiZBT8BRXGWxM9BaogUAUAqJRLcKhgLMqrOLeiPhO6xuPR8N4AAKWkgAISlNKt/y2HRMTghS5xLsyOiKhu1UM/9m5kWbPuUfl/R9L/92i2mmQhkJZ3Fj9eKkCvNm0b3PbF0n+jUi5B7TOhZBjFTeTp16NL0KQGt92SeCiUmNfrT0jseB+25B/B5Qo92qg1eLxdH3TRNPx9ISKi5qXZFio3Kg3YnXva4oRqD0mBLWdO2FSo6Mp2wPJ0bRm6su0sVKzU1T8cXf3DXZ0GEZH1+Kwfp2i2hUpZZaVV73+pofYnP9fHKG46JIaIiJoorkzrFM12jkqglzd865koKwuBDpoAm9rXqLpBgtJChBJ+qq42tU1ERO6PK9M6R7MtVDwUCozqFg2lhafwShIwrEsPm9qP1IyEQN231QJGRGpG2dQ2ERER3dJsCxUASO49AO39AmoUK9U/zdI+hDY+rWxqO9ArBh39x/ymxV//u53vMLTxfsCmtomIqAmoHvqxdyOLmnWhEuDljf88kYgRXaOhVv46HeeuwNZY+vAfkHR3H7vavyvwFfRsvQC+nl1M+3w8OiIqeC7ubj0PkoXeHCIiatok2TEbWdZsJ9NWC/TyxvxBg/H6gN8h/7oeXh6eiPDVOKSIkCQJ4X5/QLjfH1Bp1AMQ8FA4pm0iIiJqAYVKtVaeKnQJbN1o7XsqNY3WNhERuSHe9eMULaZQIaLmodJoxK6c0/j+1HlUGo3o0S4UT0R3h8bby9WpUUvDdVScgoUKETUZ565cw3P/+A8uFOuhVEiAADb9dBzvfvM9Fj2VgN/ddYerUyQiB2vWk2mJqPkor6zCmDX/RkFJKQDAKAsYxa1HZFRUVmHCuq9wovCya5OkFoXP+nEOFipE1CRsOXoCBfpS04NFb3erB15gTXqm8xOjlou3JzsFCxUiahJ255yCpfvpjLLA18dPOS0fInIOzlEhoibhZmVVvfMODVWWVosmcjABwN51UNihUi/2qBCRW9BfKcX54xegv1pa6/HuYW3qfSRGl5DgxkqPqAZXzFHZu3cvhgwZgvDwcEiShC+//NLsuBACs2bNQtu2beHt7Y24uDj88ssvZjFXr15FYmIiNBoNAgICMHbsWFy/ft0s5qeffsKgQYPg5eWFyMhILFy4sEYuGzZsQLdu3eDl5YWePXti69atDc7FGixUiMilTv94DjOHpGJ4yFg8d/cUDA8Zi1lD/46zR3PN4v4UEw3Zwoe6EEBi/96NnC3RbQQcMEelYacsKytDr1698OGHH9Z6fOHChfjggw+wfPlyHDhwAK1atUJ8fDzKy8tNMYmJiTh27Bh27tyJzZs3Y+/evXjhhRdMx/V6PQYPHowOHTogMzMTb7/9NubMmYMVK1aYYvbt24dRo0Zh7NixOHLkCIYOHYqhQ4fi6NGjDcrFGpIQ7j+TR6/Xw9/fHyUlJdBouLAaUXNx/MAveOWhOagyVEE2/tqHrlAq4Kn2xKLv3kDnezqZ9v/f/sOYv30PFJJkKlok3PqsH9y9M95/KgFKBf/+auka+zujuv2Hek+Hh1JtV1tVxgrszlqAvLw8s1zVajXUasttS5KEjRs3YujQoQBu9WCEh4fj5ZdfxiuvvAIAKCkpQWhoKFavXo2RI0fi+PHjiIqKQkZGBvr27QsA2L59O37/+9/jwoULCA8Px7Jly/D6669Dp9NBpVIBAKZPn44vv/wSOTk5AIARI0agrKwMmzdvNuUzYMAA9O7dG8uXL7cqF2vxN5qIXEIIgXefW4aqikqzIgUAZKOMyopKvPf8crP9owf0wYrEoYhp3860LzIoAH/7/YMsUsj5HHjXT2RkJPz9/U1bampqg9M5e/YsdDod4uLiTPv8/f0RGxuL9PR0AEB6ejoCAgJMRQoAxMXFQaFQ4MCBA6aY+++/31SkAEB8fDxOnDiBa9eumWJuP091TPV5rMnFWpxMS0QucSLjFM4fy6vzuGyU8cvhMzjz03ncEd3BtP/+Lp1wf5dOMFRVodIow0flyedrkWvIgMVb0axtA6i1R6WhdDodACA0NNRsf2hoqOmYTqdDSEiI2XEPDw8EBQWZxXTq1KlGG9XHAgMDodPp6j1PfblYi4UKkRMJYcTlm/tRVpULT4UvQrwfaLHPico7kW9V3IWT+WaFSjWVhwdU/ASjZkKj0XBqQx34a07kJJdvpuOnSzNRbixE9cwKhaTCHf7PokvAi5CkljVs0crfx6o4H411cUTO5oiVZR25Mm1YWBgAoLCwEG3btjXtLywsRO/evU0xRUVFZq+rqqrC1atXTa8PCwtDYWGhWUz1z/XF3H68vlys1bI+GYlc5Fr5EWToxqPcWP0BcevDSRYGnCpejhPXFrsuORfpExcNbz/LDxL0C2yF6AeinJQRUQO52cq0nTp1QlhYGHbt2mXap9frceDAAWi1WgCAVqtFcXExMjN/XcV59+7dkGUZsbGxppi9e/eisrLSFLNz50507doVgYGBppjbz1MdU30ea3KxFgsVIic4cW0xBGTUdS/imZJVqKhqWc+p8fJR4+kZT1qM+fPfnoJK7emkjIjc3/Xr15GVlYWsrCwAtyatZmVlITc3F5IkYfLkyXjzzTexadMmZGdnY/To0QgPDzfdGdS9e3c8+uijeP7553Hw4EH88MMPmDBhAkaOHInw8HAAwNNPPw2VSoWxY8fi2LFjWL9+PRYvXoyUlBRTHpMmTcL27dvx7rvvIicnB3PmzMGhQ4cwYcIEALAqF2tx6IeokZVXFeFq+aF6ogTyy7ahk/8zTsnJXYyYNhQ3r5dj3d+/hBACSqUCRqMMhUJC4szh+OOk37s6RaK6OaJHpIGvP3ToEB588EHTz9XFQ1JSElavXo2pU6eirKwML7zwAoqLi3Hfffdh+/bt8PL6tffys88+w4QJE/Dwww9DoVBg2LBh+OCDD0zH/f398fXXXyM5ORkxMTFo3bo1Zs2aZbbWyr333ou1a9di5syZeO2119ClSxd8+eWX6NGjhynGmlyswXVUiBqZ3nAC318cZjFGggfu8H8WXYNeclJW7uWq7hq+/dcPuFpwDcHtgvDgqPsQGOLv6rSoiXLWOioPd3/ZIeuo7Dr+Lr/fLGCPCjWKStmInXm/4OiVQngqFHgoojN6tW5b/wubIS9lCH5dlqx2AkZ4e7TMfx8ACAoLxLApj7s6DSJyQyxUyOEyCvMwbs9GXCm/AQ9JAQGBxT/9gP4hkVj2uz8i2Kv2uziEEKgwGqFWKpvVuhgqZSBCfH6HSzf2QqD2h+YpJE+09X3UyZkRkV0cuI4K1Y2FCjnUqZIr+PM361FpvPWFXCV+/S3MvHQBo79Zj02/TzJbQbTgeik++ukgNpw4irJKA3w9VRjRrSf+2qs/Qnx8nX4NjaFb4BRcvXkQVeImavtk6hb0CjwVfs5PjIhs5m63JzdXLaJQMZQb8P3Ggzh/LA9erbxw79B+6NA9wtVpNUsrjx1AlSxDrmWYwygEjl0txLcXTyMusgsA4GzJNQz772coqSiH8X+/sNcrDVh99DA2ncrBf4YmItKv6c9V8FXdAW34Zzh25S1cLc8w7fdShuGuwImI8HvChdkRkU1cMJm2JWr2hcrBbUew4M+LUXqtDEpPJYQs8Onra3HvE/0w/R8T4e3r7eoUm5Wvzh2HUdTdl6mUJGw5n2MqVF5J22pWpFQzCoGr5Tcwbc92rH18RKPm7Cx+qs4Y0HYVyipzcaMyD54KP/ire7S4hd6IiBqiWX9C5hz8BbOe+DuuF98AABgrjaaHn+3fnIk3RrzvyvSaHSEEblRVWowxCoFSQwUA4OTVy8gszK9RpNweuy8/F2dLrjk8V1dq5dkebXwGIsArmkUKUVMmC8dsZFGz/pT87M1/QwiB2u7Alo0yMrYdwYmMUy7IrHmSJAmRvpaHaZSShDs0QQCAn68UWYytlmNlHBGRU7nZyrTNVbMtVG6WlePA1sM1Hh9/O6WHEns+3+fErJq/0V37QGFhGrxRCIzo0gsAoFYqrWpT7dHsRyiJiKgOzbZQKb9eDmFFl1pZyQ0nZNNyPNO1D6Jbh0Hxm9uLq396KXog7vQPBgDc264DVArLxYq3hwf6h0U2RqpERHZyRG8Ke1Tq06BCZdmyZYiOjjY9jlqr1WLbtm1WvXbdunWQJKnBa/zbShPsBx+N5YmysiwjvHPLXWSrMXh5eGLtI6PwfFR/+HqqTPs7+gXi7Xt/jym97jPt81d74Zm7e9fZ/yIBeLZHX/iqVHVEEBG5EId+nKJBfeoRERFYsGABunTpAiEE1qxZgyeeeAJHjhzB3XffXefrzp07h1deeQWDBg2yO2FrKT2U+P1zcfjP4i11Dv8oFBIGJz3gtJxaCh9PFWbEPIgpvQfhwvUSqBVKRPj617qI2/TYB3Dpxg1sOn0cSkkBWQgopFtDRMPu6oGUvgNdcAVEROQuGlSoDBkyxOznt956C8uWLcP+/fvrLFSMRiMSExMxd+5cfPfddyguLrY52YYa9dofkf7VIRScKTQrViRJghAC4977CwJDA5yWT0vjpfRA5/8N89TFU6HEBw8/juej++LfvxzDpRtlCPHxxfC77sbdrUOdlCkRkQ1kBwzd8K6fetk8S9FoNGLDhg0oKyuDVqutM27evHkICQnB2LFj8d1331nVdkVFBSoqKkw/6/V6m3LUBPlh8Q9vYtXMdfj6/9JQWX7r1tn2UREYPfsp3D+87rzJuXq2CUPPNmGuToOIyHpCvrXZ2wZZ1OBCJTs7G1qtFuXl5fD19cXGjRsRFRVVa+z333+PTz75BFlZWQ06R2pqKubOndvQ1Grl31qDyctfwF/feQaF5y9D7aNCWMeQZvUsGSIiouaqwXf9dO3aFVlZWThw4ADGjx+PpKQk/PzzzzXiSktL8cwzz2DlypVo3bp1g84xY8YMlJSUmLa8vLyGplmDt683Ot4dibadQlmkEBGR/TiZ1ika3KOiUqnQuXNnAEBMTAwyMjKwePFifPTRR2Zxp0+fxrlz58zmtcjyrS4uDw8PnDhxAnfeeWet51Cr1VCr1Q1NjYiIyHk4R8Up7F5JS5Zls/kk1bp164bs7GyzfTNnzkRpaSkWL16MyEiujUFERE0YH0roFA0qVGbMmIHHHnsM7du3R2lpKdauXYu0tDTs2LEDADB69Gi0a9cOqamp8PLyQo8ePcxeHxAQAAA19hMRERHVpkGFSlFREUaPHo2CggL4+/sjOjoaO3bswCOPPAIAyM3NhULRbBe7JSIi+pWAA3pUHJJJs9agQuWTTz6xeDwtLc3i8dWrVzfkdERERO6LQz9Owe4PIjcnhEClodLVaRARuQQfS0vkps5mn8f6t/+LPZ+no8pQhTaRwfjD+HgMfen38PLhXXFELifLAOxcsE3mgm/1YY8KkRs6sjsbyf2nI23dD6gyVAEALuVdwacz/4WXfzcbN6/fdHGGRMR1VJyDhQqRmzFUVOKNP72HqkojjFXmf20JWeDUkbP457wvXJRd82coN2D/5kx8vSYNP+45Zlr/iYhcg0M/RG7m+/8cQOnV63Uel40yNq/4BklvjIRK7enEzJq/TUt3YNXMf+F6cZlpX1inEExe/gJiHunlwszILXEyrVOwR4XIzZzOOgelp9JizA39DRTlXnZSRi3DfxZvwf+b8LFZkQIAhecv4fWE+fhxzzEXZUZuSxaO2cgiFipEbsZT7WHVX1kqL/amOMqN0pv49PV/1XpMyAKyLLDi1X84OSsiAlioELmdAY/H1JibcjtJktDh7ki0iQh2YlbN277/ZqDiRs1HgVQTssDJQ6dx4WS+E7MidyeE7JCNLGOhQuRmuvbrjJ6DukOhrP3XUwiBp197kk8Bd6BruuI6/71vd1VX3PjJUNMhHDDswzkq9WKhQuRmJEnCnP+8is73dAIAKD0UkCTp1hepBIyd/zQeGnWfi7NsXoLDAyEb6//LNjg80AnZUJPB25Odgnf9ELkhTbAf/t/++cj8+kfs3ZCOstKbiOjSFo899zDadgp1dXrNzr1D+8Pb1ws3r5fXelyhkNA1tgvadW7r5MyIiIUKkZtSKBTo9+g96PfoPa5Opdnz8lHjhbdHY/H4FTWOSYpbvVnj3hntgszIrckyINk5x4RzVOrFQoWICMDjf30EKi9PfDzjM1y7bS5K++4RmLzseURpu7ouOXJPQsDuxx9z6KdeLFSIiP5ncNLv8HDiIBz9Pgf6K6UI7dgGXfrcwYnLRC7EQoWI6DZKDyV6/e5uV6dBTYCQZQg7h354e3L9WKgQERHZgkM/TsHbk4mIiMhtsUeFiIjIFrIAJPaoNDYWKkRERLYQAoC9tyezUKkPh36IiIjIbbFHhYiIyAZCFhB2Dv0I9qjUi4UKERGRLYQM+4d+eHtyfTj0Q0REZAMhC4dsDfXhhx+iY8eO8PLyQmxsLA4ePNgIV+c+WKgQERE1EevXr0dKSgpmz56Nw4cPo1evXoiPj0dRUZGrU2s0TWLop3oMT6/XuzgTIiJyd9XfFY09/6NKVNg9dFOFSgA1v9/UajXUanWN+Pfeew/PP/88xowZAwBYvnw5tmzZgk8//RTTp0+3Kxd31SQKldLSUgBAZGSkizMhIqKmorS0FP7+/g5vV6VSISwsDN/rtjqkPV9f3xrfb7Nnz8acOXPM9hkMBmRmZmLGjBmmfQqFAnFxcUhPT3dILu6oSRQq4eHhyMvLg5+fn9nDwfR6PSIjI5GXlweNRuPCDBsXr7P5aSnXyutsXprKdQohUFpaivDw8EZp38vLC2fPnoXBYHBIe0KIGg++rK035fLlyzAajQgNDTXbHxoaipycHIfk4o6aRKGiUCgQERFR53GNRuPWvzSOwutsflrKtfI6m5emcJ2N0ZNyOy8vL3h5eTXqOegWTqYlIiJqAlq3bg2lUonCwkKz/YWFhQgLC3NRVo2PhQoREVEToFKpEBMTg127dpn2ybKMXbt2QavVujCzxtUkhn7qolarMXv27FrH8poTXmfz01KuldfZvLSU63RnKSkpSEpKQt++fdG/f38sWrQIZWVlpruAmiNJcP1eIiKiJmPJkiV4++23odPp0Lt3b3zwwQeIjY11dVqNhoUKERERuS3OUSEiIiK3xUKFiIiI3BYLFSIiInJbLFSIiIjIbbldoZKamop+/frBz88PISEhGDp0KE6cOGEWU15ejuTkZAQHB8PX1xfDhg2rsQCOJEk1tnXr1jnzUiyy5jpXrFiB3/3ud9BoNJAkCcXFxTXauXr1KhITE6HRaBAQEICxY8fi+vXrTrqK+jnqOjt27Fjj/VywYIGTrsI69V3r1atXMXHiRHTt2hXe3t5o3749XnrpJZSUlJi1k5ubi4SEBPj4+CAkJASvvvoqqqqqnH05dXLUdTaH39G//vWvuPPOO+Ht7Y02bdrgiSeeqLGUeVN/PwHrrtPd309qutyuUNmzZw+Sk5Oxf/9+7Ny5E5WVlRg8eDDKyspMMVOmTMFXX32FDRs2YM+ePcjPz8eTTz5Zo61Vq1ahoKDAtA0dOtSJV2KZNdd548YNPProo3jttdfqbCcxMRHHjh3Dzp07sXnzZuzduxcvvPCCMy7BKo66TgCYN2+e2fs5ceLExk6/Qeq71vz8fOTn5+Odd97B0aNHsXr1amzfvh1jx441tWE0GpGQkACDwYB9+/ZhzZo1WL16NWbNmuWqy6rBEddZran/jsbExGDVqlU4fvw4duzYASEEBg8eDKPRCKB5vJ9A/ddZzZ3fT2rChJsrKioSAMSePXuEEEIUFxcLT09PsWHDBlPM8ePHBQCRnp5u2gdAbNy40dnp2uy313m7b7/9VgAQ165dM9v/888/CwAiIyPDtG/btm1CkiRx8eLFxk7ZJrZcpxBCdOjQQbz//vuNn6ADWbrWap9//rlQqVSisrJSCCHE1q1bhUKhEDqdzhSzbNkyodFoREVFRaPnbAtbrlOI5vU7Wu3HH38UAMSpU6eEEM33/fztdQrR9N5Pajrcrkflt6q7i4OCggAAmZmZqKysRFxcnCmmW7duaN++fY3HXCcnJ6N169bo378/Pv30Uwg3XjLmt9dpjfT0dAQEBKBv376mfXFxcVAoFDhw4IDDc3QEW66z2oIFCxAcHIx77rkHb7/9tlt1n9fGmmstKSmBRqOBh8etRaLT09PRs2dPs6ejxsfHQ6/X49ixY42bsI1suc5qzel3tKysDKtWrUKnTp0QGRkJoHm+n7VdZ7Wm9H5S0+HWS+jLsozJkydj4MCB6NGjBwBAp9NBpVIhICDALDY0NBQ6nc7087x58/DQQw/Bx8cHX3/9NV588UVcv34dL730kjMvwSq1Xac1dDodQkJCzPZ5eHggKCjI7N/CXdh6nQDw0ksvoU+fPggKCsK+ffswY8YMFBQU4L333mukbO1jzbVevnwZb7zxhtlQnU6nq/UR7tXH3I2t1wk0n9/RpUuXYurUqSgrK0PXrl2xc+dOqFQqAM3r/bR0nUDTej+piXFxj45F48aNEx06dBB5eXmmfZ999plQqVQ1Yvv16yemTp1aZ1t/+9vfRERERKPkaa/arvN2dQ2JvPXWW+Kuu+6qEd+mTRuxdOnSxkjVLrZeZ20++eQT4eHhIcrLyx2cpWPUd60lJSWif//+4tFHHxUGg8G0//nnnxeDBw82iy0rKxMAxNatWxs1Z1vYep21aaq/o8XFxeLkyZNiz549YsiQIaJPnz7i5s2bQojm9X5aus7auPP7SU2L2w79TJgwAZs3b8a3336LiIgI0/6wsDAYDIYad4bU95jr2NhYXLhwARUVFY2Vsk3quk5rhIWFoaioyGxfVVUVrl696naP/LbnOmsTGxuLqqoqnDt3zv7kHKy+ay0tLcWjjz4KPz8/bNy4EZ6enqZjYWFhtT7CvfqYO7HnOmvTVH9H/f390aVLF9x///344osvkJOTg40bNwJoXu+npeusjbu+n9T0uF2hIoTAhAkTsHHjRuzevRudOnUyOx4TEwNPT0+zx1yfOHECubm5Fh9znZWVhcDAQLd56md912kNrVaL4uJiZGZmmvbt3r0bsiy7zQOqHHGdtcnKyoJCoagx9OVK1lyrXq/H4MGDoVKpsGnTJnh5eZkd12q1yM7ONitAd+7cCY1Gg6ioqEa/Bms44jpr0xx+R4UQEEKYvpyby/tZ22tuv87auNv7SU2Yi3py6jR+/Hjh7+8v0tLSREFBgWm7ceOGKWbcuHGiffv2Yvfu3eLQoUNCq9UKrVZrOr5p0yaxcuVKkZ2dLX755RexdOlS4ePjI2bNmuWKS6qVNddZUFAgjhw5IlauXCkAiL1794ojR46IK1eumGIeffRRcc8994gDBw6I77//XnTp0kWMGjXKFZdUK0dc5759+8T7778vsrKyxOnTp8U///lP0aZNGzF69GhXXVat6rvWkpISERsbK3r27ClOnTplFlNVVSWEEKKqqkr06NFDDB48WGRlZYnt27eLNm3aiBkzZrjy0sw44jqbw+/o6dOnxfz588WhQ4fE+fPnxQ8//CCGDBkigoKCRGFhoRCiebyf1lxnU3g/qelyu0IFQK3bqlWrTDE3b94UL774oggMDBQ+Pj7ij3/8oygoKDAd37Ztm+jdu7fw9fUVrVq1Er169RLLly8XRqPRBVdUO2uuc/bs2fXGXLlyRYwaNUr4+voKjUYjxowZI0pLS51/QXVwxHVmZmaK2NhY4e/vL7y8vET37t3F/Pnz3W5+Sn3XWj0Hp7bt7NmzpnbOnTsnHnvsMeHt7S1at24tXn75ZbPbel3NEdfZHH5HL168KB577DEREhIiPD09RUREhHj66adFTk6OWTtN/f205jqbwvtJTZckBO8fIyIiIvfkdnNUiIiIiKqxUCEiIiK3xUKFiIiI3BYLFSIiInJbLFSIiIjIbbFQISIiIrfFQoWIiIjcFgsVIiIiclssVIiIiMhtsVAhIiIit8VChYiIiNzW/wc0v3u58JdTQQAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "#list(defenders_filtered_stats['name'][:10])\n", + "list_best = list(best_defenders['name'])[:5]\n", + "list_best" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xNEUgpxmIga4", + "outputId": "d616d510-dd70-4fbf-f8f0-30d355c1e5ad" + }, + "execution_count": 453, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['M. Ambrosini', 'P. García', 'P. Neville', 'A. Tymoshchuk', 'S. Cherundolo']" + ] + }, + "metadata": {}, + "execution_count": 453 + } + ] + }, + { + "cell_type": "code", + "source": [ + "top_defenders = defenders[(defenders['name'].isin(list_best))]\n", + "top_defenders" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 496 + }, + "id": "-Jn_7gfhP-Co", + "outputId": "ad8cc927-47aa-43cd-a031-286405aef196" + }, + "execution_count": 454, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id name age ova nationality club \\\n", + "10 249 P. Neville 35 74 England Everton \n", + "235 51003 S. Cherundolo 34 73 United States Hannover 96 \n", + "257 53055 M. Ambrosini 36 74 Italy Fiorentina \n", + "363 121622 A. Tymoshchuk 35 75 Ukraine Zenit St. Petersburg \n", + "404 135616 P. García 36 73 Uruguay PAOK \n", + "\n", + " bov bp position player photo \\\n", + "10 75 CB CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", + "235 73 RB RB https://cdn.sofifa.com/players/051/003/14_120.png \n", + "257 78 CB NaN https://cdn.sofifa.com/players/053/055/14_120.png \n", + "363 76 CB NaN https://cdn.sofifa.com/players/121/622/15_120.png \n", + "404 75 CB NaN https://cdn.sofifa.com/players/135/616/14_120.png \n", + "\n", + " club logo \\\n", + "10 https://cdn.sofifa.com/teams/7/light_60.png \n", + "235 https://cdn.sofifa.com/teams/485/light_60.png \n", + "257 https://cdn.sofifa.com/teams/110374/light_60.png \n", + "363 https://cdn.sofifa.com/teams/100769/light_60.png \n", + "404 https://cdn.sofifa.com/teams/393/light_60.png \n", + "\n", + " flag photo pot \\\n", + "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", + "235 https://cdn.sofifa.com/flags/us.png 73 \n", + "257 https://cdn.sofifa.com/flags/it.png 74 \n", + "363 https://cdn.sofifa.com/flags/ua.png 75 \n", + "404 https://cdn.sofifa.com/flags/uy.png 73 \n", + "\n", + " team & contract height weight foot growth \\\n", + "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 \n", + "235 Hannover 96 1999 ~ 2014 5'8\" 152lbs Right 0 \n", + "257 Fiorentina 2013 ~ 2014 6'0\" 159lbs Right 0 \n", + "363 Zenit St. Petersburg 2013 ~ 2015 5'11\" 154lbs Right 0 \n", + "404 PAOK 2014 ~ 2014 6'1\" 161lbs Left 0 \n", + "\n", + " joined loan date end value wage release clause contract \\\n", + "10 Aug 1, 2005 NaN 120K €7K 0 2005 ~ 2013 \n", + "235 Jan 1, 1999 NaN 400K €25K 0 1999 ~ 2014 \n", + "257 Jul 4, 2013 NaN 0 €35K 0 2013 ~ 2014 \n", + "363 Jul 1, 2013 NaN 375K €45K 0 2013 ~ 2015 \n", + "404 Jan 12, 2014 NaN 0 €20K 0 2014 ~ 2014 \n", + "\n", + " attacking crossing finishing heading accuracy short passing volleys \\\n", + "10 315 73 36 69 74 63.0 \n", + "235 291 70 36 65 75 45.0 \n", + "257 342 66 56 84 76 60.0 \n", + "363 304 65 38 70 72 59.0 \n", + "404 302 74 53 72 77 26.0 \n", + "\n", + " skill dribbling curve fk accuracy long passing ball control \\\n", + "10 283 53 45.0 41 72 72 \n", + "235 347 68 67.0 68 71 73 \n", + "257 314 62 56.0 48 76 72 \n", + "363 325 58 56.0 73 70 68 \n", + "404 364 66 75.0 77 76 70 \n", + "\n", + " movement acceleration sprint speed agility reactions balance power \\\n", + "10 321 52 51 65.0 83 70.0 349 \n", + "235 353 67 69 68.0 74 75.0 333 \n", + "257 285 50 50 60.0 70 55.0 360 \n", + "363 318 44 61 66.0 79 68.0 372 \n", + "404 257 38 40 34.0 70 75.0 361 \n", + "\n", + " shot power jumping stamina strength long shots mentality \\\n", + "10 77 71.0 61 76 64 335 \n", + "235 65 81.0 71 64 52 324 \n", + "257 72 92.0 55 75 66 376 \n", + "363 84 76.0 70 69 73 359 \n", + "404 76 65.0 53 85 82 345 \n", + "\n", + " aggression interceptions positioning vision penalties composure \\\n", + "10 78 83.0 48.0 57.0 69 NaN \n", + "235 72 73.0 57.0 58.0 64 NaN \n", + "257 84 82.0 68.0 74.0 68 NaN \n", + "363 83 80.0 57.0 66.0 73 NaN \n", + "404 94 75.0 47.0 67.0 62 NaN \n", + "\n", + " defending marking standing tackle sliding tackle goalkeeping \\\n", + "10 224 77 75 72.0 41 \n", + "235 224 74 73 77.0 46 \n", + "257 226 70 78 78.0 54 \n", + "363 237 77 81 79.0 50 \n", + "404 214 70 73 71.0 57 \n", + "\n", + " gk diving gk handling gk kicking gk positioning gk reflexes \\\n", + "10 10 7 12 5 7 \n", + "235 11 14 9 6 6 \n", + "257 10 12 15 5 12 \n", + "363 5 15 10 8 12 \n", + "404 15 15 6 12 9 \n", + "\n", + " total stats base stats w/f sm a/w d/w ir pac sho pas \\\n", + "10 1868 381 4 ★ 2★ Medium High 2 ★ 51 53 67 \n", + "235 1918 396 3 ★ 2★ Medium Medium 2 ★ 68 48 69 \n", + "257 1957 399 3 ★ 2★ Low High 2 ★ 50 63 71 \n", + "363 1965 392 2 ★ 2★ Low High 3 ★ 53 58 68 \n", + "404 1900 390 3 ★ 2★ Medium Medium 2 ★ 39 62 74 \n", + "\n", + " dri def phy hits ls st rs lw lf cf rf rw lam \\\n", + "10 62 76 72 7 59+0 59+0 59+0 63+0 61+0 61+0 61+0 63+0 63+0 \n", + "235 70 73 68 5 59+0 59+0 59+0 67+0 62+0 62+0 62+0 67+0 65+0 \n", + "257 65 77 73 3 66+0 66+0 66+0 65+0 67+0 67+0 67+0 65+0 69+0 \n", + "363 63 78 72 3 61+1 61+1 61+1 63+1 64+1 64+1 64+1 63+1 65+1 \n", + "404 65 72 78 3 60+0 60+0 60+0 63+0 64+0 64+0 64+0 63+0 65+0 \n", + "\n", + " cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb \\\n", + "10 63+0 63+0 65+0 68+0 68+0 68+0 65+0 72+0 74+0 74+0 74+0 72+0 \n", + "235 65+0 65+0 69+0 68+0 68+0 68+0 69+0 73+0 71+0 71+0 71+0 73+0 \n", + "257 69+0 69+0 67+0 72+0 72+0 72+0 67+0 70+0 74+0 74+0 74+0 70+0 \n", + "363 65+1 65+1 65+1 70+1 70+1 70+1 65+1 73+1 74+1 74+1 74+1 73+1 \n", + "404 65+0 65+0 64+0 69+0 69+0 69+0 64+0 68+0 73+0 73+0 73+0 68+0 \n", + "\n", + " lb lcb cb rcb rb gk gender unit number market_value \\\n", + "10 73+0 75+-1 75+-1 75+-1 73+0 12+0 Male 1000 120.0 120000 \n", + "235 73+0 72+0 72+0 72+0 73+0 13+0 Male 1000 400.0 400000 \n", + "257 73+0 78+-4 78+-4 78+-4 73+0 14+0 Male 1 0.0 0 \n", + "363 75+0 76+-1 76+-1 76+-1 75+0 14+1 Male 1000 375.0 375000 \n", + "404 70+0 75+-2 75+-2 75+-2 70+0 16+0 Male 1 0.0 0 \n", + "\n", + " unit2 number2 release clause_total total_price \n", + "10 1 0.0 0 120000 \n", + "235 1 0.0 0 400000 \n", + "257 1 0.0 0 0 \n", + "363 1 0.0 0 375000 \n", + "404 1 0.0 0 0 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_price
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000
23551003S. Cherundolo3473United StatesHannover 9673RBRBhttps://cdn.sofifa.com/players/051/003/14_120.pnghttps://cdn.sofifa.com/teams/485/light_60.pnghttps://cdn.sofifa.com/flags/us.png73Hannover 96 1999 ~ 20145'8\"152lbsRight0Jan 1, 1999NaN400K€25K01999 ~ 20142917036657545.03476867.0687173353676968.07475.03336581.07164523247273.057.058.064NaN224747377.046111496619183963 ★2★MediumMedium2 ★684869707368559+059+059+067+062+062+062+067+065+065+065+069+068+068+068+069+073+071+071+071+073+073+072+072+072+073+013+0Male1000400.040000010.00400000
25753055M. Ambrosini3674ItalyFiorentina78CBNaNhttps://cdn.sofifa.com/players/053/055/14_120.pnghttps://cdn.sofifa.com/teams/110374/light_60.pnghttps://cdn.sofifa.com/flags/it.png74Fiorentina 2013 ~ 20146'0\"159lbsRight0Jul 4, 2013NaN0€35K02013 ~ 20143426656847660.03146256.0487672285505060.07055.03607292.05575663768482.068.074.068NaN226707878.05410121551219573993 ★2★LowHigh2 ★506371657773366+066+066+065+067+067+067+065+069+069+069+067+072+072+072+067+070+074+074+074+070+073+078+-478+-478+-473+014+0Male10.0010.000
363121622A. Tymoshchuk3575UkraineZenit St. Petersburg76CBNaNhttps://cdn.sofifa.com/players/121/622/15_120.pnghttps://cdn.sofifa.com/teams/100769/light_60.pnghttps://cdn.sofifa.com/flags/ua.png75Zenit St. Petersburg 2013 ~ 20155'11\"154lbsRight0Jul 1, 2013NaN375K€45K02013 ~ 20153046538707259.03255856.0737068318446166.07968.03728476.07069733598380.057.066.073NaN237778179.0505151081219653922 ★2★LowHigh3 ★535868637872361+161+161+163+164+164+164+163+165+165+165+165+170+170+170+165+173+174+174+174+173+175+076+-176+-176+-175+014+1Male1000375.037500010.00375000
404135616P. García3673UruguayPAOK75CBNaNhttps://cdn.sofifa.com/players/135/616/14_120.pnghttps://cdn.sofifa.com/teams/393/light_60.pnghttps://cdn.sofifa.com/flags/uy.png73PAOK 2014 ~ 20146'1\"161lbsLeft0Jan 12, 2014NaN0€20K02014 ~ 20143027453727726.03646675.0777670257384034.07075.03617665.05385823459475.047.067.062NaN214707371.0571515612919003903 ★2★MediumMedium2 ★396274657278360+060+060+063+064+064+064+063+065+065+065+064+069+069+069+064+068+073+073+073+068+070+075+-275+-275+-270+016+0Male10.0010.000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 454 + } + ] + }, + { + "cell_type": "code", + "source": [ + "top_defenders = top_defenders[(defenders['nationality'].isin(['England','United States','Italy']))]\n", + "top_defenders" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 383 + }, + "id": "93W3xDY7QopC", + "outputId": "08ff386f-8752-4122-b83c-ad568cd31680" + }, + "execution_count": 455, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " top_defenders = top_defenders[(defenders['nationality'].isin(['England','United States','Italy']))]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id name age ova nationality club bov bp \\\n", + "10 249 P. Neville 35 74 England Everton 75 CB \n", + "235 51003 S. Cherundolo 34 73 United States Hannover 96 73 RB \n", + "257 53055 M. Ambrosini 36 74 Italy Fiorentina 78 CB \n", + "\n", + " position player photo \\\n", + "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", + "235 RB https://cdn.sofifa.com/players/051/003/14_120.png \n", + "257 NaN https://cdn.sofifa.com/players/053/055/14_120.png \n", + "\n", + " club logo \\\n", + "10 https://cdn.sofifa.com/teams/7/light_60.png \n", + "235 https://cdn.sofifa.com/teams/485/light_60.png \n", + "257 https://cdn.sofifa.com/teams/110374/light_60.png \n", + "\n", + " flag photo pot team & contract \\\n", + "10 https://cdn.sofifa.com/flags/gb-eng.png 74 Everton 2005 ~ 2013 \n", + "235 https://cdn.sofifa.com/flags/us.png 73 Hannover 96 1999 ~ 2014 \n", + "257 https://cdn.sofifa.com/flags/it.png 74 Fiorentina 2013 ~ 2014 \n", + "\n", + " height weight foot growth joined loan date end value wage \\\n", + "10 5'11\" 168lbs Right 0 Aug 1, 2005 NaN 120K €7K \n", + "235 5'8\" 152lbs Right 0 Jan 1, 1999 NaN 400K €25K \n", + "257 6'0\" 159lbs Right 0 Jul 4, 2013 NaN 0 €35K \n", + "\n", + " release clause contract attacking crossing finishing \\\n", + "10 0 2005 ~ 2013 315 73 36 \n", + "235 0 1999 ~ 2014 291 70 36 \n", + "257 0 2013 ~ 2014 342 66 56 \n", + "\n", + " heading accuracy short passing volleys skill dribbling curve \\\n", + "10 69 74 63.0 283 53 45.0 \n", + "235 65 75 45.0 347 68 67.0 \n", + "257 84 76 60.0 314 62 56.0 \n", + "\n", + " fk accuracy long passing ball control movement acceleration \\\n", + "10 41 72 72 321 52 \n", + "235 68 71 73 353 67 \n", + "257 48 76 72 285 50 \n", + "\n", + " sprint speed agility reactions balance power shot power jumping \\\n", + "10 51 65.0 83 70.0 349 77 71.0 \n", + "235 69 68.0 74 75.0 333 65 81.0 \n", + "257 50 60.0 70 55.0 360 72 92.0 \n", + "\n", + " stamina strength long shots mentality aggression interceptions \\\n", + "10 61 76 64 335 78 83.0 \n", + "235 71 64 52 324 72 73.0 \n", + "257 55 75 66 376 84 82.0 \n", + "\n", + " positioning vision penalties composure defending marking \\\n", + "10 48.0 57.0 69 NaN 224 77 \n", + "235 57.0 58.0 64 NaN 224 74 \n", + "257 68.0 74.0 68 NaN 226 70 \n", + "\n", + " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", + "10 75 72.0 41 10 7 \n", + "235 73 77.0 46 11 14 \n", + "257 78 78.0 54 10 12 \n", + "\n", + " gk kicking gk positioning gk reflexes total stats base stats w/f \\\n", + "10 12 5 7 1868 381 4 ★ \n", + "235 9 6 6 1918 396 3 ★ \n", + "257 15 5 12 1957 399 3 ★ \n", + "\n", + " sm a/w d/w ir pac sho pas dri def phy hits ls st \\\n", + "10 2★ Medium High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 \n", + "235 2★ Medium Medium 2 ★ 68 48 69 70 73 68 5 59+0 59+0 \n", + "257 2★ Low High 2 ★ 50 63 71 65 77 73 3 66+0 66+0 \n", + "\n", + " rs lw lf cf rf rw lam cam ram lm lcm cm \\\n", + "10 59+0 63+0 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 \n", + "235 59+0 67+0 62+0 62+0 62+0 67+0 65+0 65+0 65+0 69+0 68+0 68+0 \n", + "257 66+0 65+0 67+0 67+0 67+0 65+0 69+0 69+0 69+0 67+0 72+0 72+0 \n", + "\n", + " rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb \\\n", + "10 68+0 65+0 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 \n", + "235 68+0 69+0 73+0 71+0 71+0 71+0 73+0 73+0 72+0 72+0 72+0 \n", + "257 72+0 67+0 70+0 74+0 74+0 74+0 70+0 73+0 78+-4 78+-4 78+-4 \n", + "\n", + " rb gk gender unit number market_value unit2 number2 \\\n", + "10 73+0 12+0 Male 1000 120.0 120000 1 0.0 \n", + "235 73+0 13+0 Male 1000 400.0 400000 1 0.0 \n", + "257 73+0 14+0 Male 1 0.0 0 1 0.0 \n", + "\n", + " release clause_total total_price \n", + "10 0 120000 \n", + "235 0 400000 \n", + "257 0 0 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_price
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000
23551003S. Cherundolo3473United StatesHannover 9673RBRBhttps://cdn.sofifa.com/players/051/003/14_120.pnghttps://cdn.sofifa.com/teams/485/light_60.pnghttps://cdn.sofifa.com/flags/us.png73Hannover 96 1999 ~ 20145'8\"152lbsRight0Jan 1, 1999NaN400K€25K01999 ~ 20142917036657545.03476867.0687173353676968.07475.03336581.07164523247273.057.058.064NaN224747377.046111496619183963 ★2★MediumMedium2 ★684869707368559+059+059+067+062+062+062+067+065+065+065+069+068+068+068+069+073+071+071+071+073+073+072+072+072+073+013+0Male1000400.040000010.00400000
25753055M. Ambrosini3674ItalyFiorentina78CBNaNhttps://cdn.sofifa.com/players/053/055/14_120.pnghttps://cdn.sofifa.com/teams/110374/light_60.pnghttps://cdn.sofifa.com/flags/it.png74Fiorentina 2013 ~ 20146'0\"159lbsRight0Jul 4, 2013NaN0€35K02013 ~ 20143426656847660.03146256.0487672285505060.07055.03607292.05575663768482.068.074.068NaN226707878.05410121551219573993 ★2★LowHigh2 ★506371657773366+066+066+065+067+067+067+065+069+069+069+067+072+072+072+067+070+074+074+074+070+073+078+-478+-478+-473+014+0Male10.0010.000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 455 + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "etIBgo7YYt1X" + }, + "execution_count": 449, + "outputs": [] + } + ] +} \ No newline at end of file From e15fe4f3ef8a58b1e09217ea9568b1140c7af76c Mon Sep 17 00:00:00 2001 From: martaferreiro <125505098+martaferreiro@users.noreply.github.com> Date: Sat, 22 Apr 2023 21:36:44 +0200 Subject: [PATCH 2/3] Delete Copia_de_Project2.ipynb --- Copia_de_Project2.ipynb | 13909 -------------------------------------- 1 file changed, 13909 deletions(-) delete mode 100644 Copia_de_Project2.ipynb diff --git a/Copia_de_Project2.ipynb b/Copia_de_Project2.ipynb deleted file mode 100644 index 54bb7df..0000000 --- a/Copia_de_Project2.ipynb +++ /dev/null @@ -1,13909 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyMWrXF4cKlSipGaTpYWYVcv", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 408, - "metadata": { - "id": "k8WL3zjmGtN0" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "source": [ - "fifa = pd.read_csv('fifa21_male2 copia.csv')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "HQmSdpDmHC8g", - "outputId": "cf54de61-e473-4d8e-82a7-26cbea95a9bf" - }, - "execution_count": 409, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":1: DtypeWarning: Columns (78) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " fifa = pd.read_csv('fifa21_male2 copia.csv')\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "fifa.head()" - ], - "metadata": { - "id": "dUQzMNTlHT1a", - "outputId": "7ff5c463-09ad-40ee-8b3c-646661be9334", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 479 - } - }, - "execution_count": 410, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Name Age OVA Nationality Club BOV BP \\\n", - "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", - "1 16 Luis García 37 71 Spain KAS Eupen 70 CM \n", - "2 27 J. Cole 33 71 England Coventry City 71 CAM \n", - "3 36 D. Yorke 36 68 Trinidad & Tobago Sunderland 70 ST \n", - "4 41 Iniesta 36 81 Spain Vissel Kobe 82 CAM \n", - "\n", - " Position Player Photo \\\n", - "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", - "1 CM CAM CDM https://cdn.sofifa.com/players/000/016/19_120.png \n", - "2 CAM RM RW LM https://cdn.sofifa.com/players/000/027/16_120.png \n", - "3 NaN https://cdn.sofifa.com/players/000/036/09_120.png \n", - "4 CM CAM https://cdn.sofifa.com/players/000/041/20_120.png \n", - "\n", - " Club Logo \\\n", - "0 https://cdn.sofifa.com/teams/55/light_60.png \n", - "1 https://cdn.sofifa.com/teams/2013/light_60.png \n", - "2 https://cdn.sofifa.com/teams/1800/light_60.png \n", - "3 https://cdn.sofifa.com/teams/106/light_60.png \n", - "4 https://cdn.sofifa.com/teams/101146/light_60.png \n", - "\n", - " Flag Photo POT Team & Contract \\\n", - "0 https://cdn.sofifa.com/flags/it.png 69 Udinese 2008 ~ 2016 \n", - "1 https://cdn.sofifa.com/flags/es.png 71 KAS Eupen 2014 ~ 2019 \n", - "2 https://cdn.sofifa.com/flags/gb-eng.png 71 Coventry City 2016 ~ 2020 \n", - "3 https://cdn.sofifa.com/flags/tt.png 82 Sunderland 2009 \n", - "4 https://cdn.sofifa.com/flags/es.png 81 Vissel Kobe 2018 ~ 2021 \n", - "\n", - " Height Weight foot Growth Joined Loan Date End Value Wage \\\n", - "0 6'0\" 181lbs Left 0 Jul 1, 2008 NaN €625K €7K \n", - "1 5'10\" 143lbs Right 0 Jul 19, 2014 NaN €600K €7K \n", - "2 5'9\" 161lbs Right 0 Jan 7, 2016 NaN €1.1M €15K \n", - "3 5'11\" 165lbs Right 14 NaN NaN €0 €0 \n", - "4 5'7\" 150lbs Right 0 Jul 16, 2018 NaN €5.5M €12K \n", - "\n", - " Release Clause Contract Attacking Crossing Finishing \\\n", - "0 €0 2008 ~ 2016 313 75 50 \n", - "1 €1.1M 2014 ~ 2019 337 68 64 \n", - "2 €0 2016 ~ 2020 337 80 64 \n", - "3 €0 2009 264 54 70 \n", - "4 €7.2M 2018 ~ 2021 367 75 69 \n", - "\n", - " Heading Accuracy Short Passing Volleys Skill Dribbling Curve \\\n", - "0 59 71 58.0 338 73 65.0 \n", - "1 61 76 68.0 369 69 79.0 \n", - "2 41 77 75.0 387 79 84.0 \n", - "3 60 80 NaN 255 68 NaN \n", - "4 54 90 79.0 408 85 80.0 \n", - "\n", - " FK Accuracy Long Passing Ball Control Movement Acceleration \\\n", - "0 60 69 71 347 68 \n", - "1 79 71 71 305 56 \n", - "2 77 69 78 295 48 \n", - "3 46 64 77 176 59 \n", - "4 70 83 90 346 61 \n", - "\n", - " Sprint Speed Agility Reactions Balance Power Shot Power Jumping \\\n", - "0 74 68.0 69 68.0 347 74 68.0 \n", - "1 50 62.0 65 72.0 324 75 54.0 \n", - "2 42 71.0 59 75.0 284 72 58.0 \n", - "3 62 NaN 55 NaN 239 63 NaN \n", - "4 56 79.0 75 75.0 297 67 40.0 \n", - "\n", - " Stamina Strength Long Shots Mentality Aggression Interceptions \\\n", - "0 69 68 68 320 72 69.0 \n", - "1 64 60 71 362 71 71.0 \n", - "2 29 56 69 317 69 39.0 \n", - "3 51 66 59 271 59 70.0 \n", - "4 58 62 70 370 58 70.0 \n", - "\n", - " Positioning Vision Penalties Composure Defending Marking \\\n", - "0 63.0 66.0 50 NaN 208 70 \n", - "1 72.0 73.0 75 79.0 153 70 \n", - "2 69.0 74.0 66 NaN 99 35 \n", - "3 72.0 NaN 70 NaN 75 34 \n", - "4 78.0 93.0 71 89.0 181 68 \n", - "\n", - " Standing Tackle Sliding Tackle Goalkeeping GK Diving GK Handling \\\n", - "0 69 69.0 56 14 5 \n", - "1 43 40.0 56 9 12 \n", - "2 34 30.0 51 9 6 \n", - "3 41 NaN 68 5 21 \n", - "4 57 56.0 45 6 13 \n", - "\n", - " GK Kicking GK Positioning GK Reflexes Total Stats Base Stats W/F SM \\\n", - "0 15 10 12 1929 408 3 ★ 2★ \n", - "1 13 11 11 1906 385 4 ★ 3★ \n", - "2 13 16 7 1770 354 4 ★ 4★ \n", - "3 64 21 21 1348 369 3 ★ 1★ \n", - "4 6 13 7 2014 420 4 ★ 4★ \n", - "\n", - " A/W D/W IR PAC SHO PAS DRI DEF PHY Hits LS ST RS \\\n", - "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", - "1 Medium Medium 1 ★ 53 69 73 69 58 63 4 67+1 67+1 67+1 \n", - "2 Medium Low 2 ★ 45 68 76 77 36 52 11 64+0 64+0 64+0 \n", - "3 NaN NaN 1 ★ 61 66 66 69 47 60 3 67+0 67+0 67+0 \n", - "4 High Medium 4 ★ 58 70 85 85 63 59 149 72+3 72+3 72+3 \n", - "\n", - " LW LF CF RF RW LAM CAM RAM LM LCM CM RCM \\\n", - "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", - "1 67+0 68+0 68+0 68+0 67+0 70+1 70+1 70+1 68+1 70+1 70+1 70+1 \n", - "2 70+0 69+0 69+0 69+0 70+0 71+0 71+0 71+0 68+0 66+0 66+0 66+0 \n", - "3 66+0 67+0 67+0 67+0 66+0 70+0 70+0 70+0 66+0 68+0 68+0 68+0 \n", - "4 79+0 79+0 79+0 79+0 79+0 82+-1 82+-1 82+-1 79+2 81+0 81+0 81+0 \n", - "\n", - " RM LWB LDM CDM RDM RWB LB LCB CB RCB RB \\\n", - "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 70+-1 \n", - "1 68+1 62+1 66+1 66+1 66+1 62+1 60+1 60+1 60+1 60+1 60+1 \n", - "2 68+0 52+0 54+0 54+0 54+0 52+0 47+0 46+0 46+0 46+0 47+0 \n", - "3 66+0 56+0 65+0 65+0 65+0 56+0 57+0 51+0 51+0 51+0 57+0 \n", - "4 79+2 70+3 73+3 73+3 73+3 70+3 67+3 64+3 64+3 64+3 67+3 \n", - "\n", - " GK Gender \n", - "0 17+0 Male \n", - "1 17+1 Male \n", - "2 15+0 Male \n", - "3 22+0 Male \n", - "4 17+3 Male " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
IDNameAgeOVANationalityClubBOVBPPositionPlayer PhotoClub LogoFlag PhotoPOTTeam & ContractHeightWeightfootGrowthJoinedLoan Date EndValueWageRelease ClauseContractAttackingCrossingFinishingHeading AccuracyShort PassingVolleysSkillDribblingCurveFK AccuracyLong PassingBall ControlMovementAccelerationSprint SpeedAgilityReactionsBalancePowerShot PowerJumpingStaminaStrengthLong ShotsMentalityAggressionInterceptionsPositioningVisionPenaltiesComposureDefendingMarkingStanding TackleSliding TackleGoalkeepingGK DivingGK HandlingGK KickingGK PositioningGK ReflexesTotal StatsBase StatsW/FSMA/WD/WIRPACSHOPASDRIDEFPHYHitsLSSTRSLWLFCFRFRWLAMCAMRAMLMLCMCMRCMRMLWBLDMCDMRDMRWBLBLCBCBRCBRBGKGender
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN€625K€7K€02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male
116Luis García3771SpainKAS Eupen70CMCM CAM CDMhttps://cdn.sofifa.com/players/000/016/19_120.pnghttps://cdn.sofifa.com/teams/2013/light_60.pnghttps://cdn.sofifa.com/flags/es.png71KAS Eupen 2014 ~ 20195'10\"143lbsRight0Jul 19, 2014NaN€600K€7K€1.1M2014 ~ 20193376864617668.03696979.0797171305565062.06572.03247554.06460713627171.072.073.07579.0153704340.05691213111119063854 ★3★MediumMedium1 ★536973695863467+167+167+167+068+068+068+067+070+170+170+168+170+170+170+168+162+166+166+166+162+160+160+160+160+160+117+1Male
227J. Cole3371EnglandCoventry City71CAMCAM RM RW LMhttps://cdn.sofifa.com/players/000/027/16_120.pnghttps://cdn.sofifa.com/teams/1800/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png71Coventry City 2016 ~ 20205'9\"161lbsRight0Jan 7, 2016NaN€1.1M€15K€02016 ~ 20203378064417775.03877984.0776978295484271.05975.02847258.02956693176939.069.074.066NaN99353430.051961316717703544 ★4★MediumLow2 ★4568767736521164+064+064+070+069+069+069+070+071+071+071+068+066+066+066+068+052+054+054+054+052+047+046+046+046+047+015+0Male
336D. Yorke3668Trinidad &amp; TobagoSunderland70STNaNhttps://cdn.sofifa.com/players/000/036/09_120.pnghttps://cdn.sofifa.com/teams/106/light_60.pnghttps://cdn.sofifa.com/flags/tt.png82Sunderland 20095'11\"165lbsRight14NaNNaN€0€0€0200926454706080NaN25568NaN4664771765962NaN55NaN23963NaN5166592715970.072.0NaN70NaN753441NaN6852164212113483693 ★1★NaNNaN1 ★616666694760367+067+067+066+067+067+067+066+070+070+070+066+068+068+068+066+056+065+065+065+056+057+051+051+051+057+022+0Male
441Iniesta3681SpainVissel Kobe82CAMCM CAMhttps://cdn.sofifa.com/players/000/041/20_120.pnghttps://cdn.sofifa.com/teams/101146/light_60.pnghttps://cdn.sofifa.com/flags/es.png81Vissel Kobe 2018 ~ 20215'7\"150lbsRight0Jul 16, 2018NaN€5.5M€12K€7.2M2018 ~ 20213677569549079.04088580.0708390346615679.07575.02976740.05862703705870.078.093.07189.0181685756.045613613720144204 ★4★HighMedium4 ★58708585635914972+372+372+379+079+079+079+079+082+-182+-182+-179+281+081+081+079+270+373+373+373+370+367+364+364+364+367+317+3Male
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 410 - } - ] - }, - { - "cell_type": "code", - "source": [ - "fifa.columns = fifa.columns.str.lower()\n", - "fifa.shape" - ], - "metadata": { - "id": "KRNUoNteHWOZ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "3f6be6b7-a28e-4f3e-9ae3-b32164b86887" - }, - "execution_count": 411, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(17125, 107)" - ] - }, - "metadata": {}, - "execution_count": 411 - } - ] - }, - { - "cell_type": "code", - "source": [ - "fifa['value']=[i.replace('€','') for i in fifa['value']]\n", - "fifa['unit'] = fifa['value'].str[-1:]\n", - "fifa['number']=[i.replace('K','') for i in fifa['value']]\n", - "fifa['number']=[i.replace('M','') for i in fifa['number']]\n", - "fifa['number']=fifa['number'].astype(float)" - ], - "metadata": { - "id": "wgvuU_aFKzGd" - }, - "execution_count": 412, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "fifa['unit'].value_counts()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hD7VYzcfOSjh", - "outputId": "c1877080-662d-4e90-d48e-bad5e6b59cbc" - }, - "execution_count": 413, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "K 9354\n", - "M 7314\n", - "0 457\n", - "Name: unit, dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 413 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#multiply number column by unit\n", - "fifa['unit']=[i.replace('0', '1') for i in fifa['unit']]\n", - "fifa['unit']=[i.replace('K', '1000') for i in fifa['unit']]\n", - "fifa['unit']=[i.replace('M', '1000000') for i in fifa['unit']]\n", - "fifa['unit']=fifa['unit'].astype(int)" - ], - "metadata": { - "id": "gs82HUwbOWGb" - }, - "execution_count": 414, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "fifa['market_value'] = (fifa['unit']*fifa['number']).astype(int)" - ], - "metadata": { - "id": "jxltKplFOYIb" - }, - "execution_count": 415, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "fifa['release clause']=[i.replace('€','') for i in fifa['release clause']]\n", - "fifa['unit2'] = fifa['release clause'].str[-1:]\n", - "fifa['number2']=[i.replace('K','') for i in fifa['release clause']]\n", - "fifa['number2']=[i.replace('M','') for i in fifa['number2']]\n", - "fifa['number2']=fifa['number2'].astype(float)" - ], - "metadata": { - "id": "qPnXDzcqjLOB" - }, - "execution_count": 416, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "fifa['unit2'].value_counts()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mxhZYr0LTZPt", - "outputId": "5ddf4906-7cd2-4f90-eb33-f788b1d249ac" - }, - "execution_count": 417, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "M 9883\n", - "K 5457\n", - "0 1785\n", - "Name: unit2, dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 417 - } - ] - }, - { - "cell_type": "code", - "source": [ - "fifa['unit2']=[i.replace('0', '1') for i in fifa['unit2']]\n", - "fifa['unit2']=[i.replace('K', '1000') for i in fifa['unit2']]\n", - "fifa['unit2']=[i.replace('M', '1000000') for i in fifa['unit2']]\n", - "fifa['unit2']=fifa['unit2'].astype(int)\n" - ], - "metadata": { - "id": "87nI_kIVjQ9o" - }, - "execution_count": 418, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "fifa['release clause_total'] = (fifa['unit2']*fifa['number2']).astype(int)" - ], - "metadata": { - "id": "ae7_PgwPjq7l" - }, - "execution_count": 419, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "display(fifa.shape)\n", - "fifa.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 497 - }, - "id": "5GhGa7YwOZDq", - "outputId": "d1703bfd-2e39-4e7f-96d1-709fd745ba23" - }, - "execution_count": 420, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "(17125, 113)" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id name age ova nationality club bov bp \\\n", - "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", - "1 16 Luis García 37 71 Spain KAS Eupen 70 CM \n", - "2 27 J. Cole 33 71 England Coventry City 71 CAM \n", - "3 36 D. Yorke 36 68 Trinidad & Tobago Sunderland 70 ST \n", - "4 41 Iniesta 36 81 Spain Vissel Kobe 82 CAM \n", - "\n", - " position player photo \\\n", - "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", - "1 CM CAM CDM https://cdn.sofifa.com/players/000/016/19_120.png \n", - "2 CAM RM RW LM https://cdn.sofifa.com/players/000/027/16_120.png \n", - "3 NaN https://cdn.sofifa.com/players/000/036/09_120.png \n", - "4 CM CAM https://cdn.sofifa.com/players/000/041/20_120.png \n", - "\n", - " club logo \\\n", - "0 https://cdn.sofifa.com/teams/55/light_60.png \n", - "1 https://cdn.sofifa.com/teams/2013/light_60.png \n", - "2 https://cdn.sofifa.com/teams/1800/light_60.png \n", - "3 https://cdn.sofifa.com/teams/106/light_60.png \n", - "4 https://cdn.sofifa.com/teams/101146/light_60.png \n", - "\n", - " flag photo pot team & contract \\\n", - "0 https://cdn.sofifa.com/flags/it.png 69 Udinese 2008 ~ 2016 \n", - "1 https://cdn.sofifa.com/flags/es.png 71 KAS Eupen 2014 ~ 2019 \n", - "2 https://cdn.sofifa.com/flags/gb-eng.png 71 Coventry City 2016 ~ 2020 \n", - "3 https://cdn.sofifa.com/flags/tt.png 82 Sunderland 2009 \n", - "4 https://cdn.sofifa.com/flags/es.png 81 Vissel Kobe 2018 ~ 2021 \n", - "\n", - " height weight foot growth joined loan date end value wage \\\n", - "0 6'0\" 181lbs Left 0 Jul 1, 2008 NaN 625K €7K \n", - "1 5'10\" 143lbs Right 0 Jul 19, 2014 NaN 600K €7K \n", - "2 5'9\" 161lbs Right 0 Jan 7, 2016 NaN 1.1M €15K \n", - "3 5'11\" 165lbs Right 14 NaN NaN 0 €0 \n", - "4 5'7\" 150lbs Right 0 Jul 16, 2018 NaN 5.5M €12K \n", - "\n", - " release clause contract attacking crossing finishing \\\n", - "0 0 2008 ~ 2016 313 75 50 \n", - "1 1.1M 2014 ~ 2019 337 68 64 \n", - "2 0 2016 ~ 2020 337 80 64 \n", - "3 0 2009 264 54 70 \n", - "4 7.2M 2018 ~ 2021 367 75 69 \n", - "\n", - " heading accuracy short passing volleys skill dribbling curve \\\n", - "0 59 71 58.0 338 73 65.0 \n", - "1 61 76 68.0 369 69 79.0 \n", - "2 41 77 75.0 387 79 84.0 \n", - "3 60 80 NaN 255 68 NaN \n", - "4 54 90 79.0 408 85 80.0 \n", - "\n", - " fk accuracy long passing ball control movement acceleration \\\n", - "0 60 69 71 347 68 \n", - "1 79 71 71 305 56 \n", - "2 77 69 78 295 48 \n", - "3 46 64 77 176 59 \n", - "4 70 83 90 346 61 \n", - "\n", - " sprint speed agility reactions balance power shot power jumping \\\n", - "0 74 68.0 69 68.0 347 74 68.0 \n", - "1 50 62.0 65 72.0 324 75 54.0 \n", - "2 42 71.0 59 75.0 284 72 58.0 \n", - "3 62 NaN 55 NaN 239 63 NaN \n", - "4 56 79.0 75 75.0 297 67 40.0 \n", - "\n", - " stamina strength long shots mentality aggression interceptions \\\n", - "0 69 68 68 320 72 69.0 \n", - "1 64 60 71 362 71 71.0 \n", - "2 29 56 69 317 69 39.0 \n", - "3 51 66 59 271 59 70.0 \n", - "4 58 62 70 370 58 70.0 \n", - "\n", - " positioning vision penalties composure defending marking \\\n", - "0 63.0 66.0 50 NaN 208 70 \n", - "1 72.0 73.0 75 79.0 153 70 \n", - "2 69.0 74.0 66 NaN 99 35 \n", - "3 72.0 NaN 70 NaN 75 34 \n", - "4 78.0 93.0 71 89.0 181 68 \n", - "\n", - " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", - "0 69 69.0 56 14 5 \n", - "1 43 40.0 56 9 12 \n", - "2 34 30.0 51 9 6 \n", - "3 41 NaN 68 5 21 \n", - "4 57 56.0 45 6 13 \n", - "\n", - " gk kicking gk positioning gk reflexes total stats base stats w/f sm \\\n", - "0 15 10 12 1929 408 3 ★ 2★ \n", - "1 13 11 11 1906 385 4 ★ 3★ \n", - "2 13 16 7 1770 354 4 ★ 4★ \n", - "3 64 21 21 1348 369 3 ★ 1★ \n", - "4 6 13 7 2014 420 4 ★ 4★ \n", - "\n", - " a/w d/w ir pac sho pas dri def phy hits ls st rs \\\n", - "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", - "1 Medium Medium 1 ★ 53 69 73 69 58 63 4 67+1 67+1 67+1 \n", - "2 Medium Low 2 ★ 45 68 76 77 36 52 11 64+0 64+0 64+0 \n", - "3 NaN NaN 1 ★ 61 66 66 69 47 60 3 67+0 67+0 67+0 \n", - "4 High Medium 4 ★ 58 70 85 85 63 59 149 72+3 72+3 72+3 \n", - "\n", - " lw lf cf rf rw lam cam ram lm lcm cm rcm \\\n", - "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", - "1 67+0 68+0 68+0 68+0 67+0 70+1 70+1 70+1 68+1 70+1 70+1 70+1 \n", - "2 70+0 69+0 69+0 69+0 70+0 71+0 71+0 71+0 68+0 66+0 66+0 66+0 \n", - "3 66+0 67+0 67+0 67+0 66+0 70+0 70+0 70+0 66+0 68+0 68+0 68+0 \n", - "4 79+0 79+0 79+0 79+0 79+0 82+-1 82+-1 82+-1 79+2 81+0 81+0 81+0 \n", - "\n", - " rm lwb ldm cdm rdm rwb lb lcb cb rcb rb \\\n", - "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 70+-1 \n", - "1 68+1 62+1 66+1 66+1 66+1 62+1 60+1 60+1 60+1 60+1 60+1 \n", - "2 68+0 52+0 54+0 54+0 54+0 52+0 47+0 46+0 46+0 46+0 47+0 \n", - "3 66+0 56+0 65+0 65+0 65+0 56+0 57+0 51+0 51+0 51+0 57+0 \n", - "4 79+2 70+3 73+3 73+3 73+3 70+3 67+3 64+3 64+3 64+3 67+3 \n", - "\n", - " gk gender unit number market_value unit2 number2 \\\n", - "0 17+0 Male 1000 625.0 625000 1 0.0 \n", - "1 17+1 Male 1000 600.0 600000 1000000 1.1 \n", - "2 15+0 Male 1000000 1.1 1100000 1 0.0 \n", - "3 22+0 Male 1 0.0 0 1 0.0 \n", - "4 17+3 Male 1000000 5.5 5500000 1000000 7.2 \n", - "\n", - " release clause_total \n", - "0 0 \n", - "1 1100000 \n", - "2 0 \n", - "3 0 \n", - "4 7200000 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_total
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00
116Luis García3771SpainKAS Eupen70CMCM CAM CDMhttps://cdn.sofifa.com/players/000/016/19_120.pnghttps://cdn.sofifa.com/teams/2013/light_60.pnghttps://cdn.sofifa.com/flags/es.png71KAS Eupen 2014 ~ 20195'10\"143lbsRight0Jul 19, 2014NaN600K€7K1.1M2014 ~ 20193376864617668.03696979.0797171305565062.06572.03247554.06460713627171.072.073.07579.0153704340.05691213111119063854 ★3★MediumMedium1 ★536973695863467+167+167+167+068+068+068+067+070+170+170+168+170+170+170+168+162+166+166+166+162+160+160+160+160+160+117+1Male1000600.060000010000001.11100000
227J. Cole3371EnglandCoventry City71CAMCAM RM RW LMhttps://cdn.sofifa.com/players/000/027/16_120.pnghttps://cdn.sofifa.com/teams/1800/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png71Coventry City 2016 ~ 20205'9\"161lbsRight0Jan 7, 2016NaN1.1M€15K02016 ~ 20203378064417775.03877984.0776978295484271.05975.02847258.02956693176939.069.074.066NaN99353430.051961316717703544 ★4★MediumLow2 ★4568767736521164+064+064+070+069+069+069+070+071+071+071+068+066+066+066+068+052+054+054+054+052+047+046+046+046+047+015+0Male10000001.1110000010.00
336D. Yorke3668Trinidad &amp; TobagoSunderland70STNaNhttps://cdn.sofifa.com/players/000/036/09_120.pnghttps://cdn.sofifa.com/teams/106/light_60.pnghttps://cdn.sofifa.com/flags/tt.png82Sunderland 20095'11\"165lbsRight14NaNNaN0€00200926454706080NaN25568NaN4664771765962NaN55NaN23963NaN5166592715970.072.0NaN70NaN753441NaN6852164212113483693 ★1★NaNNaN1 ★616666694760367+067+067+066+067+067+067+066+070+070+070+066+068+068+068+066+056+065+065+065+056+057+051+051+051+057+022+0Male10.0010.00
441Iniesta3681SpainVissel Kobe82CAMCM CAMhttps://cdn.sofifa.com/players/000/041/20_120.pnghttps://cdn.sofifa.com/teams/101146/light_60.pnghttps://cdn.sofifa.com/flags/es.png81Vissel Kobe 2018 ~ 20215'7\"150lbsRight0Jul 16, 2018NaN5.5M€12K7.2M2018 ~ 20213677569549079.04088580.0708390346615679.07575.02976740.05862703705870.078.093.07189.0181685756.045613613720144204 ★4★HighMedium4 ★58708585635914972+372+372+379+079+079+079+079+082+-182+-182+-179+281+081+081+079+270+373+373+373+370+367+364+364+364+367+317+3Male10000005.5550000010000007.27200000
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 420 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#fifa.drop(columns=['value','unit','number','value2','unit2','number2'])" - ], - "metadata": { - "id": "4XpVXl7ROaiK" - }, - "execution_count": 421, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "fifa['bp'].value_counts()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "miofeBLjO4md", - "outputId": "e0b9c6da-980c-4b80-c327-a5bdcca2a0d8" - }, - "execution_count": 422, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "CB 3252\n", - "ST 2660\n", - "CAM 2246\n", - "GK 1576\n", - "RM 1404\n", - "CDM 1246\n", - "CM 990\n", - "LB 921\n", - "RB 894\n", - "LM 805\n", - "RW 329\n", - "LWB 252\n", - "RWB 252\n", - "LW 209\n", - "CF 89\n", - "Name: bp, dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 422 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#Taking only the defenders\n", - "defenders = fifa[(fifa['bp']==('RB')) | (fifa['bp']==('LB')) | (fifa['bp']==('LWB')) | (fifa['bp']==('RWB')) | (fifa['bp']==('CB'))]\n", - "display(defenders.shape)\n", - "defenders.head()" - ], - "metadata": { - "id": "EKbuMPguQ4d2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 549 - }, - "outputId": "b1ab777b-f716-4123-a8fc-50836f530898" - }, - "execution_count": 423, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "(5571, 113)" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id name age ova nationality club bov bp \\\n", - "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", - "8 244 G. Neville B 35 76 England Manchester United 78 CB \n", - "10 249 P. Neville 35 74 England Everton 75 CB \n", - "13 388 S. Campbell 35 75 England Newcastle United 75 CB \n", - "15 496 D. Hamann 36 61 Germany Milton Keynes Dons 67 CB \n", - "\n", - " position player photo \\\n", - "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", - "8 RB CB RWB https://cdn.sofifa.com/players/000/244/11_120.png \n", - "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", - "13 NaN https://cdn.sofifa.com/players/000/388/11_120.png \n", - "15 NaN https://cdn.sofifa.com/players/000/496/11_120.png \n", - "\n", - " club logo \\\n", - "0 https://cdn.sofifa.com/teams/55/light_60.png \n", - "8 https://cdn.sofifa.com/teams/11/light_60.png \n", - "10 https://cdn.sofifa.com/teams/7/light_60.png \n", - "13 https://cdn.sofifa.com/teams/13/light_60.png \n", - "15 https://cdn.sofifa.com/teams/1798/light_60.png \n", - "\n", - " flag photo pot \\\n", - "0 https://cdn.sofifa.com/flags/it.png 69 \n", - "8 https://cdn.sofifa.com/flags/gb-eng.png 82 \n", - "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", - "13 https://cdn.sofifa.com/flags/gb-eng.png 79 \n", - "15 https://cdn.sofifa.com/flags/de.png 82 \n", - "\n", - " team & contract height weight foot growth joined \\\n", - "0 Udinese 2008 ~ 2016 6'0\" 181lbs Left 0 Jul 1, 2008 \n", - "8 Manchester United 1991 ~ 2011 5'10\" 174lbs Right 6 Jan 1, 1991 \n", - "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 Aug 1, 2005 \n", - "13 Newcastle United 2010 ~ 2011 6'2\" 190lbs Right 4 Sep 2, 2010 \n", - "15 Milton Keynes Dons 2010 ~ 2011 6'2\" 168lbs Right 21 Sep 2, 2010 \n", - "\n", - " loan date end value wage release clause contract attacking crossing \\\n", - "0 NaN 625K €7K 0 2008 ~ 2016 313 75 \n", - "8 NaN 0 €0 0 1991 ~ 2011 304 72 \n", - "10 NaN 120K €7K 0 2005 ~ 2013 315 73 \n", - "13 NaN 0 €0 0 2010 ~ 2011 206 20 \n", - "15 NaN 0 €0 0 2010 ~ 2011 270 51 \n", - "\n", - " finishing heading accuracy short passing volleys skill dribbling \\\n", - "0 50 59 71 58.0 338 73 \n", - "8 31 75 71 55.0 258 44 \n", - "10 36 69 74 63.0 283 53 \n", - "13 28 81 54 23.0 173 40 \n", - "15 35 62 76 46.0 288 37 \n", - "\n", - " curve fk accuracy long passing ball control movement acceleration \\\n", - "0 65.0 60 69 71 347 68 \n", - "8 56.0 33 61 64 324 64 \n", - "10 45.0 41 72 72 321 52 \n", - "13 19.0 15 44 55 321 61 \n", - "15 47.0 76 64 64 212 40 \n", - "\n", - " sprint speed agility reactions balance power shot power jumping \\\n", - "0 74 68.0 69 68.0 347 74 68.0 \n", - "8 70 51.0 72 67.0 284 47 70.0 \n", - "10 51 65.0 83 70.0 349 77 71.0 \n", - "13 68 44.0 68 80.0 319 57 85.0 \n", - "15 44 28.0 30 70.0 330 71 83.0 \n", - "\n", - " stamina strength long shots mentality aggression interceptions \\\n", - "0 69 68 68 320 72 69.0 \n", - "8 65 74 28 319 87 85.0 \n", - "10 61 76 64 335 78 83.0 \n", - "13 64 87 26 296 84 77.0 \n", - "15 28 82 66 344 74 75.0 \n", - "\n", - " positioning vision penalties composure defending marking \\\n", - "0 63.0 66.0 50 NaN 208 70 \n", - "8 45.0 70.0 32 NaN 242 78 \n", - "10 48.0 57.0 69 NaN 224 77 \n", - "13 41.0 57.0 37 NaN 222 72 \n", - "15 51.0 78.0 66 NaN 198 70 \n", - "\n", - " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", - "0 69 69.0 56 14 5 \n", - "8 81 83.0 43 12 9 \n", - "10 75 72.0 41 10 7 \n", - "13 77 73.0 44 11 7 \n", - "15 72 56.0 56 11 12 \n", - "\n", - " gk kicking gk positioning gk reflexes total stats base stats w/f sm \\\n", - "0 15 10 12 1929 408 3 ★ 2★ \n", - "8 5 6 11 1774 378 3 ★ 2★ \n", - "10 12 5 7 1868 381 4 ★ 2★ \n", - "13 12 5 9 1581 347 3 ★ 2★ \n", - "15 10 8 15 1698 343 3 ★ 2★ \n", - "\n", - " a/w d/w ir pac sho pas dri def phy hits ls st rs \\\n", - "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", - "8 NaN NaN 3 ★ 67 36 67 53 81 74 4 54+0 54+0 54+0 \n", - "10 Medium High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 59+0 \n", - "13 NaN NaN 3 ★ 65 34 43 48 76 81 3 51+0 51+0 51+0 \n", - "15 NaN NaN 3 ★ 42 51 68 46 69 67 3 51+0 51+0 51+0 \n", - "\n", - " lw lf cf rf rw lam cam ram lm lcm cm rcm \\\n", - "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", - "8 60+0 56+0 56+0 56+0 60+0 56+0 56+0 56+0 62+0 64+0 64+0 64+0 \n", - "10 63+0 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 68+0 \n", - "13 48+0 48+0 48+0 48+0 48+0 49+0 49+0 49+0 48+0 56+0 56+0 56+0 \n", - "15 50+0 51+0 51+0 51+0 50+0 59+0 59+0 59+0 53+0 61+0 61+0 61+0 \n", - "\n", - " rm lwb ldm cdm rdm rwb lb lcb cb rcb \\\n", - "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 \n", - "8 62+0 73+0 74+0 74+0 74+0 73+0 76+0 78+0 78+0 78+0 \n", - "10 65+0 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 \n", - "13 48+0 63+0 68+0 68+0 68+0 63+0 70+0 75+0 75+0 75+0 \n", - "15 53+0 58+0 65+0 65+0 65+0 58+0 62+0 67+0 67+0 67+0 \n", - "\n", - " rb gk gender unit number market_value unit2 number2 \\\n", - "0 70+-1 17+0 Male 1000 625.0 625000 1 0.0 \n", - "8 76+0 13+0 Male 1 0.0 0 1 0.0 \n", - "10 73+0 12+0 Male 1000 120.0 120000 1 0.0 \n", - "13 70+0 12+0 Male 1 0.0 0 1 0.0 \n", - "15 62+0 14+0 Male 1 0.0 0 1 0.0 \n", - "\n", - " release clause_total \n", - "0 0 \n", - "8 0 \n", - "10 0 \n", - "13 0 \n", - "15 0 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_total
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00
8244G. Neville B3576EnglandManchester United78CBRB CB RWBhttps://cdn.sofifa.com/players/000/244/11_120.pnghttps://cdn.sofifa.com/teams/11/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png82Manchester United 1991 ~ 20115'10\"174lbsRight6Jan 1, 1991NaN0€001991 ~ 20113047231757155.02584456.0336164324647051.07267.02844770.06574283198785.045.070.032NaN242788183.043129561117743783 ★2★NaNNaN3 ★673667538174454+054+054+060+056+056+056+060+056+056+056+062+064+064+064+062+073+074+074+074+073+076+078+078+078+076+013+0Male10.0010.00
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00
13388S. Campbell3575EnglandNewcastle United75CBNaNhttps://cdn.sofifa.com/players/000/388/11_120.pnghttps://cdn.sofifa.com/teams/13/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png79Newcastle United 2010 ~ 20116'2\"190lbsRight4Sep 2, 2010NaN0€002010 ~ 20112062028815423.01734019.0154455321616844.06880.03195785.06487262968477.041.057.037NaN222727773.044117125915813473 ★2★NaNNaN3 ★653443487681351+051+051+048+048+048+048+048+049+049+049+048+056+056+056+048+063+068+068+068+063+070+075+075+075+070+012+0Male10.0010.00
15496D. Hamann3661GermanyMilton Keynes Dons67CBNaNhttps://cdn.sofifa.com/players/000/496/11_120.pnghttps://cdn.sofifa.com/teams/1798/light_60.pnghttps://cdn.sofifa.com/flags/de.png82Milton Keynes Dons 2010 ~ 20116'2\"168lbsRight21Sep 2, 2010NaN0€002010 ~ 20112705135627646.02883747.0766464212404428.03070.03307183.02882663447475.051.078.066NaN198707256.05611121081516983433 ★2★NaNNaN3 ★425168466967351+051+051+050+051+051+051+050+059+059+059+053+061+061+061+053+058+065+065+065+058+062+067+067+067+062+014+0Male10.0010.00
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 423 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#Create the column for the total value\n", - "defenders['total_price'] = defenders['market_value']+defenders['release clause_total']\n", - "display(defenders.shape)\n", - "defenders.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 658 - }, - "id": "Z5-wtZatgMcq", - "outputId": "15e3b8bc-7daf-480e-e5ad-840ad0f8cb13" - }, - "execution_count": 424, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders['total_price'] = defenders['market_value']+defenders['release clause_total']\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "(5571, 114)" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id name age ova nationality club bov bp \\\n", - "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", - "8 244 G. Neville B 35 76 England Manchester United 78 CB \n", - "10 249 P. Neville 35 74 England Everton 75 CB \n", - "13 388 S. Campbell 35 75 England Newcastle United 75 CB \n", - "15 496 D. Hamann 36 61 Germany Milton Keynes Dons 67 CB \n", - "\n", - " position player photo \\\n", - "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", - "8 RB CB RWB https://cdn.sofifa.com/players/000/244/11_120.png \n", - "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", - "13 NaN https://cdn.sofifa.com/players/000/388/11_120.png \n", - "15 NaN https://cdn.sofifa.com/players/000/496/11_120.png \n", - "\n", - " club logo \\\n", - "0 https://cdn.sofifa.com/teams/55/light_60.png \n", - "8 https://cdn.sofifa.com/teams/11/light_60.png \n", - "10 https://cdn.sofifa.com/teams/7/light_60.png \n", - "13 https://cdn.sofifa.com/teams/13/light_60.png \n", - "15 https://cdn.sofifa.com/teams/1798/light_60.png \n", - "\n", - " flag photo pot \\\n", - "0 https://cdn.sofifa.com/flags/it.png 69 \n", - "8 https://cdn.sofifa.com/flags/gb-eng.png 82 \n", - "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", - "13 https://cdn.sofifa.com/flags/gb-eng.png 79 \n", - "15 https://cdn.sofifa.com/flags/de.png 82 \n", - "\n", - " team & contract height weight foot growth joined \\\n", - "0 Udinese 2008 ~ 2016 6'0\" 181lbs Left 0 Jul 1, 2008 \n", - "8 Manchester United 1991 ~ 2011 5'10\" 174lbs Right 6 Jan 1, 1991 \n", - "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 Aug 1, 2005 \n", - "13 Newcastle United 2010 ~ 2011 6'2\" 190lbs Right 4 Sep 2, 2010 \n", - "15 Milton Keynes Dons 2010 ~ 2011 6'2\" 168lbs Right 21 Sep 2, 2010 \n", - "\n", - " loan date end value wage release clause contract attacking crossing \\\n", - "0 NaN 625K €7K 0 2008 ~ 2016 313 75 \n", - "8 NaN 0 €0 0 1991 ~ 2011 304 72 \n", - "10 NaN 120K €7K 0 2005 ~ 2013 315 73 \n", - "13 NaN 0 €0 0 2010 ~ 2011 206 20 \n", - "15 NaN 0 €0 0 2010 ~ 2011 270 51 \n", - "\n", - " finishing heading accuracy short passing volleys skill dribbling \\\n", - "0 50 59 71 58.0 338 73 \n", - "8 31 75 71 55.0 258 44 \n", - "10 36 69 74 63.0 283 53 \n", - "13 28 81 54 23.0 173 40 \n", - "15 35 62 76 46.0 288 37 \n", - "\n", - " curve fk accuracy long passing ball control movement acceleration \\\n", - "0 65.0 60 69 71 347 68 \n", - "8 56.0 33 61 64 324 64 \n", - "10 45.0 41 72 72 321 52 \n", - "13 19.0 15 44 55 321 61 \n", - "15 47.0 76 64 64 212 40 \n", - "\n", - " sprint speed agility reactions balance power shot power jumping \\\n", - "0 74 68.0 69 68.0 347 74 68.0 \n", - "8 70 51.0 72 67.0 284 47 70.0 \n", - "10 51 65.0 83 70.0 349 77 71.0 \n", - "13 68 44.0 68 80.0 319 57 85.0 \n", - "15 44 28.0 30 70.0 330 71 83.0 \n", - "\n", - " stamina strength long shots mentality aggression interceptions \\\n", - "0 69 68 68 320 72 69.0 \n", - "8 65 74 28 319 87 85.0 \n", - "10 61 76 64 335 78 83.0 \n", - "13 64 87 26 296 84 77.0 \n", - "15 28 82 66 344 74 75.0 \n", - "\n", - " positioning vision penalties composure defending marking \\\n", - "0 63.0 66.0 50 NaN 208 70 \n", - "8 45.0 70.0 32 NaN 242 78 \n", - "10 48.0 57.0 69 NaN 224 77 \n", - "13 41.0 57.0 37 NaN 222 72 \n", - "15 51.0 78.0 66 NaN 198 70 \n", - "\n", - " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", - "0 69 69.0 56 14 5 \n", - "8 81 83.0 43 12 9 \n", - "10 75 72.0 41 10 7 \n", - "13 77 73.0 44 11 7 \n", - "15 72 56.0 56 11 12 \n", - "\n", - " gk kicking gk positioning gk reflexes total stats base stats w/f sm \\\n", - "0 15 10 12 1929 408 3 ★ 2★ \n", - "8 5 6 11 1774 378 3 ★ 2★ \n", - "10 12 5 7 1868 381 4 ★ 2★ \n", - "13 12 5 9 1581 347 3 ★ 2★ \n", - "15 10 8 15 1698 343 3 ★ 2★ \n", - "\n", - " a/w d/w ir pac sho pas dri def phy hits ls st rs \\\n", - "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", - "8 NaN NaN 3 ★ 67 36 67 53 81 74 4 54+0 54+0 54+0 \n", - "10 Medium High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 59+0 \n", - "13 NaN NaN 3 ★ 65 34 43 48 76 81 3 51+0 51+0 51+0 \n", - "15 NaN NaN 3 ★ 42 51 68 46 69 67 3 51+0 51+0 51+0 \n", - "\n", - " lw lf cf rf rw lam cam ram lm lcm cm rcm \\\n", - "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", - "8 60+0 56+0 56+0 56+0 60+0 56+0 56+0 56+0 62+0 64+0 64+0 64+0 \n", - "10 63+0 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 68+0 \n", - "13 48+0 48+0 48+0 48+0 48+0 49+0 49+0 49+0 48+0 56+0 56+0 56+0 \n", - "15 50+0 51+0 51+0 51+0 50+0 59+0 59+0 59+0 53+0 61+0 61+0 61+0 \n", - "\n", - " rm lwb ldm cdm rdm rwb lb lcb cb rcb \\\n", - "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 \n", - "8 62+0 73+0 74+0 74+0 74+0 73+0 76+0 78+0 78+0 78+0 \n", - "10 65+0 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 \n", - "13 48+0 63+0 68+0 68+0 68+0 63+0 70+0 75+0 75+0 75+0 \n", - "15 53+0 58+0 65+0 65+0 65+0 58+0 62+0 67+0 67+0 67+0 \n", - "\n", - " rb gk gender unit number market_value unit2 number2 \\\n", - "0 70+-1 17+0 Male 1000 625.0 625000 1 0.0 \n", - "8 76+0 13+0 Male 1 0.0 0 1 0.0 \n", - "10 73+0 12+0 Male 1000 120.0 120000 1 0.0 \n", - "13 70+0 12+0 Male 1 0.0 0 1 0.0 \n", - "15 62+0 14+0 Male 1 0.0 0 1 0.0 \n", - "\n", - " release clause_total total_price \n", - "0 0 625000 \n", - "8 0 0 \n", - "10 0 120000 \n", - "13 0 0 \n", - "15 0 0 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_price
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00625000
8244G. Neville B3576EnglandManchester United78CBRB CB RWBhttps://cdn.sofifa.com/players/000/244/11_120.pnghttps://cdn.sofifa.com/teams/11/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png82Manchester United 1991 ~ 20115'10\"174lbsRight6Jan 1, 1991NaN0€001991 ~ 20113047231757155.02584456.0336164324647051.07267.02844770.06574283198785.045.070.032NaN242788183.043129561117743783 ★2★NaNNaN3 ★673667538174454+054+054+060+056+056+056+060+056+056+056+062+064+064+064+062+073+074+074+074+073+076+078+078+078+076+013+0Male10.0010.000
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000
13388S. Campbell3575EnglandNewcastle United75CBNaNhttps://cdn.sofifa.com/players/000/388/11_120.pnghttps://cdn.sofifa.com/teams/13/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png79Newcastle United 2010 ~ 20116'2\"190lbsRight4Sep 2, 2010NaN0€002010 ~ 20112062028815423.01734019.0154455321616844.06880.03195785.06487262968477.041.057.037NaN222727773.044117125915813473 ★2★NaNNaN3 ★653443487681351+051+051+048+048+048+048+048+049+049+049+048+056+056+056+048+063+068+068+068+063+070+075+075+075+070+012+0Male10.0010.000
15496D. Hamann3661GermanyMilton Keynes Dons67CBNaNhttps://cdn.sofifa.com/players/000/496/11_120.pnghttps://cdn.sofifa.com/teams/1798/light_60.pnghttps://cdn.sofifa.com/flags/de.png82Milton Keynes Dons 2010 ~ 20116'2\"168lbsRight21Sep 2, 2010NaN0€002010 ~ 20112705135627646.02883747.0766464212404428.03070.03307183.02882663447475.051.078.066NaN198707256.05611121081516983433 ★2★NaNNaN3 ★425168466967351+051+051+050+051+051+051+050+059+059+059+053+061+061+061+053+058+065+065+065+058+062+067+067+067+062+014+0Male10.0010.000
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 424 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#Filter the old players between 30 and 37, so they are not too old\n", - "defenders_filtered = defenders[(defenders['age'] >= 30) & (defenders['age'] <= 37)]\n", - "display(defenders_filtered.shape)\n", - "defenders_filtered.head()" - ], - "metadata": { - "id": "rKgELZ9zi8uT", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 549 - }, - "outputId": "37376b89-ebe2-44ef-99e5-41df2a0e1d36" - }, - "execution_count": 425, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "(1182, 114)" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id name age ova nationality club bov bp \\\n", - "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", - "8 244 G. Neville B 35 76 England Manchester United 78 CB \n", - "10 249 P. Neville 35 74 England Everton 75 CB \n", - "13 388 S. Campbell 35 75 England Newcastle United 75 CB \n", - "15 496 D. Hamann 36 61 Germany Milton Keynes Dons 67 CB \n", - "\n", - " position player photo \\\n", - "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", - "8 RB CB RWB https://cdn.sofifa.com/players/000/244/11_120.png \n", - "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", - "13 NaN https://cdn.sofifa.com/players/000/388/11_120.png \n", - "15 NaN https://cdn.sofifa.com/players/000/496/11_120.png \n", - "\n", - " club logo \\\n", - "0 https://cdn.sofifa.com/teams/55/light_60.png \n", - "8 https://cdn.sofifa.com/teams/11/light_60.png \n", - "10 https://cdn.sofifa.com/teams/7/light_60.png \n", - "13 https://cdn.sofifa.com/teams/13/light_60.png \n", - "15 https://cdn.sofifa.com/teams/1798/light_60.png \n", - "\n", - " flag photo pot \\\n", - "0 https://cdn.sofifa.com/flags/it.png 69 \n", - "8 https://cdn.sofifa.com/flags/gb-eng.png 82 \n", - "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", - "13 https://cdn.sofifa.com/flags/gb-eng.png 79 \n", - "15 https://cdn.sofifa.com/flags/de.png 82 \n", - "\n", - " team & contract height weight foot growth joined \\\n", - "0 Udinese 2008 ~ 2016 6'0\" 181lbs Left 0 Jul 1, 2008 \n", - "8 Manchester United 1991 ~ 2011 5'10\" 174lbs Right 6 Jan 1, 1991 \n", - "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 Aug 1, 2005 \n", - "13 Newcastle United 2010 ~ 2011 6'2\" 190lbs Right 4 Sep 2, 2010 \n", - "15 Milton Keynes Dons 2010 ~ 2011 6'2\" 168lbs Right 21 Sep 2, 2010 \n", - "\n", - " loan date end value wage release clause contract attacking crossing \\\n", - "0 NaN 625K €7K 0 2008 ~ 2016 313 75 \n", - "8 NaN 0 €0 0 1991 ~ 2011 304 72 \n", - "10 NaN 120K €7K 0 2005 ~ 2013 315 73 \n", - "13 NaN 0 €0 0 2010 ~ 2011 206 20 \n", - "15 NaN 0 €0 0 2010 ~ 2011 270 51 \n", - "\n", - " finishing heading accuracy short passing volleys skill dribbling \\\n", - "0 50 59 71 58.0 338 73 \n", - "8 31 75 71 55.0 258 44 \n", - "10 36 69 74 63.0 283 53 \n", - "13 28 81 54 23.0 173 40 \n", - "15 35 62 76 46.0 288 37 \n", - "\n", - " curve fk accuracy long passing ball control movement acceleration \\\n", - "0 65.0 60 69 71 347 68 \n", - "8 56.0 33 61 64 324 64 \n", - "10 45.0 41 72 72 321 52 \n", - "13 19.0 15 44 55 321 61 \n", - "15 47.0 76 64 64 212 40 \n", - "\n", - " sprint speed agility reactions balance power shot power jumping \\\n", - "0 74 68.0 69 68.0 347 74 68.0 \n", - "8 70 51.0 72 67.0 284 47 70.0 \n", - "10 51 65.0 83 70.0 349 77 71.0 \n", - "13 68 44.0 68 80.0 319 57 85.0 \n", - "15 44 28.0 30 70.0 330 71 83.0 \n", - "\n", - " stamina strength long shots mentality aggression interceptions \\\n", - "0 69 68 68 320 72 69.0 \n", - "8 65 74 28 319 87 85.0 \n", - "10 61 76 64 335 78 83.0 \n", - "13 64 87 26 296 84 77.0 \n", - "15 28 82 66 344 74 75.0 \n", - "\n", - " positioning vision penalties composure defending marking \\\n", - "0 63.0 66.0 50 NaN 208 70 \n", - "8 45.0 70.0 32 NaN 242 78 \n", - "10 48.0 57.0 69 NaN 224 77 \n", - "13 41.0 57.0 37 NaN 222 72 \n", - "15 51.0 78.0 66 NaN 198 70 \n", - "\n", - " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", - "0 69 69.0 56 14 5 \n", - "8 81 83.0 43 12 9 \n", - "10 75 72.0 41 10 7 \n", - "13 77 73.0 44 11 7 \n", - "15 72 56.0 56 11 12 \n", - "\n", - " gk kicking gk positioning gk reflexes total stats base stats w/f sm \\\n", - "0 15 10 12 1929 408 3 ★ 2★ \n", - "8 5 6 11 1774 378 3 ★ 2★ \n", - "10 12 5 7 1868 381 4 ★ 2★ \n", - "13 12 5 9 1581 347 3 ★ 2★ \n", - "15 10 8 15 1698 343 3 ★ 2★ \n", - "\n", - " a/w d/w ir pac sho pas dri def phy hits ls st rs \\\n", - "0 Medium High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 \n", - "8 NaN NaN 3 ★ 67 36 67 53 81 74 4 54+0 54+0 54+0 \n", - "10 Medium High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 59+0 \n", - "13 NaN NaN 3 ★ 65 34 43 48 76 81 3 51+0 51+0 51+0 \n", - "15 NaN NaN 3 ★ 42 51 68 46 69 67 3 51+0 51+0 51+0 \n", - "\n", - " lw lf cf rf rw lam cam ram lm lcm cm rcm \\\n", - "0 68+0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 \n", - "8 60+0 56+0 56+0 56+0 60+0 56+0 56+0 56+0 62+0 64+0 64+0 64+0 \n", - "10 63+0 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 68+0 \n", - "13 48+0 48+0 48+0 48+0 48+0 49+0 49+0 49+0 48+0 56+0 56+0 56+0 \n", - "15 50+0 51+0 51+0 51+0 50+0 59+0 59+0 59+0 53+0 61+0 61+0 61+0 \n", - "\n", - " rm lwb ldm cdm rdm rwb lb lcb cb rcb \\\n", - "0 69+0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 \n", - "8 62+0 73+0 74+0 74+0 74+0 73+0 76+0 78+0 78+0 78+0 \n", - "10 65+0 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 \n", - "13 48+0 63+0 68+0 68+0 68+0 63+0 70+0 75+0 75+0 75+0 \n", - "15 53+0 58+0 65+0 65+0 65+0 58+0 62+0 67+0 67+0 67+0 \n", - "\n", - " rb gk gender unit number market_value unit2 number2 \\\n", - "0 70+-1 17+0 Male 1000 625.0 625000 1 0.0 \n", - "8 76+0 13+0 Male 1 0.0 0 1 0.0 \n", - "10 73+0 12+0 Male 1000 120.0 120000 1 0.0 \n", - "13 70+0 12+0 Male 1 0.0 0 1 0.0 \n", - "15 62+0 14+0 Male 1 0.0 0 1 0.0 \n", - "\n", - " release clause_total total_price \n", - "0 0 625000 \n", - "8 0 0 \n", - "10 0 120000 \n", - "13 0 0 \n", - "15 0 0 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_price
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN208706969.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00625000
8244G. Neville B3576EnglandManchester United78CBRB CB RWBhttps://cdn.sofifa.com/players/000/244/11_120.pnghttps://cdn.sofifa.com/teams/11/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png82Manchester United 1991 ~ 20115'10\"174lbsRight6Jan 1, 1991NaN0€001991 ~ 20113047231757155.02584456.0336164324647051.07267.02844770.06574283198785.045.070.032NaN242788183.043129561117743783 ★2★NaNNaN3 ★673667538174454+054+054+060+056+056+056+060+056+056+056+062+064+064+064+062+073+074+074+074+073+076+078+078+078+076+013+0Male10.0010.000
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000
13388S. Campbell3575EnglandNewcastle United75CBNaNhttps://cdn.sofifa.com/players/000/388/11_120.pnghttps://cdn.sofifa.com/teams/13/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png79Newcastle United 2010 ~ 20116'2\"190lbsRight4Sep 2, 2010NaN0€002010 ~ 20112062028815423.01734019.0154455321616844.06880.03195785.06487262968477.041.057.037NaN222727773.044117125915813473 ★2★NaNNaN3 ★653443487681351+051+051+048+048+048+048+048+049+049+049+048+056+056+056+048+063+068+068+068+063+070+075+075+075+070+012+0Male10.0010.000
15496D. Hamann3661GermanyMilton Keynes Dons67CBNaNhttps://cdn.sofifa.com/players/000/496/11_120.pnghttps://cdn.sofifa.com/teams/1798/light_60.pnghttps://cdn.sofifa.com/flags/de.png82Milton Keynes Dons 2010 ~ 20116'2\"168lbsRight21Sep 2, 2010NaN0€002010 ~ 20112705135627646.02883747.0766464212404428.03070.03307183.02882663447475.051.078.066NaN198707256.05611121081516983433 ★2★NaNNaN3 ★425168466967351+051+051+050+051+051+051+050+059+059+059+053+061+061+061+053+058+065+065+065+058+062+067+067+067+062+014+0Male10.0010.000
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 425 - } - ] - }, - { - "cell_type": "code", - "source": [ - "def ft_in_to_cm(height):\n", - " feet, inches = height.split(\"'\")\n", - " cm = int(feet) * 30.48 + int(inches[:-1]) * 2.54\n", - " return cm\n", - "\n", - "# apply the function to the 'Height' column and create a new 'Height_cm' column\n", - "defenders_filtered['height_cm'] = defenders_filtered['height'].apply(ft_in_to_cm)\n", - "defenders_filtered.drop(columns=['height'])\n", - "defenders_filtered.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 669 - }, - "id": "-WHNF347hwOd", - "outputId": "26168b02-303d-4649-9180-87fa3b0c488a" - }, - "execution_count": 426, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":7: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered['height_cm'] = defenders_filtered['height'].apply(ft_in_to_cm)\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id name age ova nationality club bov bp \\\n", - "0 2 G. Pasquale 33 69 Italy Udinese 71 LWB \n", - "8 244 G. Neville B 35 76 England Manchester United 78 CB \n", - "10 249 P. Neville 35 74 England Everton 75 CB \n", - "13 388 S. Campbell 35 75 England Newcastle United 75 CB \n", - "15 496 D. Hamann 36 61 Germany Milton Keynes Dons 67 CB \n", - "\n", - " position player photo \\\n", - "0 LM https://cdn.sofifa.com/players/000/002/16_120.png \n", - "8 RB CB RWB https://cdn.sofifa.com/players/000/244/11_120.png \n", - "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", - "13 NaN https://cdn.sofifa.com/players/000/388/11_120.png \n", - "15 NaN https://cdn.sofifa.com/players/000/496/11_120.png \n", - "\n", - " club logo \\\n", - "0 https://cdn.sofifa.com/teams/55/light_60.png \n", - "8 https://cdn.sofifa.com/teams/11/light_60.png \n", - "10 https://cdn.sofifa.com/teams/7/light_60.png \n", - "13 https://cdn.sofifa.com/teams/13/light_60.png \n", - "15 https://cdn.sofifa.com/teams/1798/light_60.png \n", - "\n", - " flag photo pot \\\n", - "0 https://cdn.sofifa.com/flags/it.png 69 \n", - "8 https://cdn.sofifa.com/flags/gb-eng.png 82 \n", - "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", - "13 https://cdn.sofifa.com/flags/gb-eng.png 79 \n", - "15 https://cdn.sofifa.com/flags/de.png 82 \n", - "\n", - " team & contract height weight foot growth joined \\\n", - "0 Udinese 2008 ~ 2016 6'0\" 181lbs Left 0 Jul 1, 2008 \n", - "8 Manchester United 1991 ~ 2011 5'10\" 174lbs Right 6 Jan 1, 1991 \n", - "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 Aug 1, 2005 \n", - "13 Newcastle United 2010 ~ 2011 6'2\" 190lbs Right 4 Sep 2, 2010 \n", - "15 Milton Keynes Dons 2010 ~ 2011 6'2\" 168lbs Right 21 Sep 2, 2010 \n", - "\n", - " loan date end value wage release clause contract attacking crossing \\\n", - "0 NaN 625K €7K 0 2008 ~ 2016 313 75 \n", - "8 NaN 0 €0 0 1991 ~ 2011 304 72 \n", - "10 NaN 120K €7K 0 2005 ~ 2013 315 73 \n", - "13 NaN 0 €0 0 2010 ~ 2011 206 20 \n", - "15 NaN 0 €0 0 2010 ~ 2011 270 51 \n", - "\n", - " finishing heading accuracy short passing volleys skill dribbling \\\n", - "0 50 59 71 58.0 338 73 \n", - "8 31 75 71 55.0 258 44 \n", - "10 36 69 74 63.0 283 53 \n", - "13 28 81 54 23.0 173 40 \n", - "15 35 62 76 46.0 288 37 \n", - "\n", - " curve fk accuracy long passing ball control movement acceleration \\\n", - "0 65.0 60 69 71 347 68 \n", - "8 56.0 33 61 64 324 64 \n", - "10 45.0 41 72 72 321 52 \n", - "13 19.0 15 44 55 321 61 \n", - "15 47.0 76 64 64 212 40 \n", - "\n", - " sprint speed agility reactions balance power shot power jumping \\\n", - "0 74 68.0 69 68.0 347 74 68.0 \n", - "8 70 51.0 72 67.0 284 47 70.0 \n", - "10 51 65.0 83 70.0 349 77 71.0 \n", - "13 68 44.0 68 80.0 319 57 85.0 \n", - "15 44 28.0 30 70.0 330 71 83.0 \n", - "\n", - " stamina strength long shots mentality aggression interceptions \\\n", - "0 69 68 68 320 72 69.0 \n", - "8 65 74 28 319 87 85.0 \n", - "10 61 76 64 335 78 83.0 \n", - "13 64 87 26 296 84 77.0 \n", - "15 28 82 66 344 74 75.0 \n", - "\n", - " positioning vision penalties composure defending marking ... \\\n", - "0 63.0 66.0 50 NaN 208 70 ... \n", - "8 45.0 70.0 32 NaN 242 78 ... \n", - "10 48.0 57.0 69 NaN 224 77 ... \n", - "13 41.0 57.0 37 NaN 222 72 ... \n", - "15 51.0 78.0 66 NaN 198 70 ... \n", - "\n", - " sliding tackle goalkeeping gk diving gk handling gk kicking \\\n", - "0 69.0 56 14 5 15 \n", - "8 83.0 43 12 9 5 \n", - "10 72.0 41 10 7 12 \n", - "13 73.0 44 11 7 12 \n", - "15 56.0 56 11 12 10 \n", - "\n", - " gk positioning gk reflexes total stats base stats w/f sm a/w \\\n", - "0 10 12 1929 408 3 ★ 2★ Medium \n", - "8 6 11 1774 378 3 ★ 2★ NaN \n", - "10 5 7 1868 381 4 ★ 2★ Medium \n", - "13 5 9 1581 347 3 ★ 2★ NaN \n", - "15 8 15 1698 343 3 ★ 2★ NaN \n", - "\n", - " d/w ir pac sho pas dri def phy hits ls st rs lw \\\n", - "0 High 2 ★ 71 59 70 71 68 69 4 65+0 65+0 65+0 68+0 \n", - "8 NaN 3 ★ 67 36 67 53 81 74 4 54+0 54+0 54+0 60+0 \n", - "10 High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 59+0 63+0 \n", - "13 NaN 3 ★ 65 34 43 48 76 81 3 51+0 51+0 51+0 48+0 \n", - "15 NaN 3 ★ 42 51 68 46 69 67 3 51+0 51+0 51+0 50+0 \n", - "\n", - " lf cf rf rw lam cam ram lm lcm cm rcm rm \\\n", - "0 67+0 67+0 67+0 68+0 68+0 68+0 68+0 69+0 69+0 69+0 69+0 69+0 \n", - "8 56+0 56+0 56+0 60+0 56+0 56+0 56+0 62+0 64+0 64+0 64+0 62+0 \n", - "10 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 68+0 65+0 \n", - "13 48+0 48+0 48+0 48+0 49+0 49+0 49+0 48+0 56+0 56+0 56+0 48+0 \n", - "15 51+0 51+0 51+0 50+0 59+0 59+0 59+0 53+0 61+0 61+0 61+0 53+0 \n", - "\n", - " lwb ldm cdm rdm rwb lb lcb cb rcb rb \\\n", - "0 71+-2 70+-1 70+-1 70+-1 71+-2 70+-1 69+0 69+0 69+0 70+-1 \n", - "8 73+0 74+0 74+0 74+0 73+0 76+0 78+0 78+0 78+0 76+0 \n", - "10 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 73+0 \n", - "13 63+0 68+0 68+0 68+0 63+0 70+0 75+0 75+0 75+0 70+0 \n", - "15 58+0 65+0 65+0 65+0 58+0 62+0 67+0 67+0 67+0 62+0 \n", - "\n", - " gk gender unit number market_value unit2 number2 \\\n", - "0 17+0 Male 1000 625.0 625000 1 0.0 \n", - "8 13+0 Male 1 0.0 0 1 0.0 \n", - "10 12+0 Male 1000 120.0 120000 1 0.0 \n", - "13 12+0 Male 1 0.0 0 1 0.0 \n", - "15 14+0 Male 1 0.0 0 1 0.0 \n", - "\n", - " release clause_total total_price height_cm \n", - "0 0 625000 182.88 \n", - "8 0 0 177.80 \n", - "10 0 120000 180.34 \n", - "13 0 0 187.96 \n", - "15 0 0 187.96 \n", - "\n", - "[5 rows x 115 columns]" - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarking...sliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_priceheight_cm
02G. Pasquale3369ItalyUdinese71LWBLMhttps://cdn.sofifa.com/players/000/002/16_120.pnghttps://cdn.sofifa.com/teams/55/light_60.pnghttps://cdn.sofifa.com/flags/it.png69Udinese 2008 ~ 20166'0\"181lbsLeft0Jul 1, 2008NaN625K€7K02008 ~ 20163137550597158.03387365.0606971347687468.06968.03477468.06968683207269.063.066.050NaN20870...69.05614515101219294083 ★2★MediumHigh2 ★715970716869465+065+065+068+067+067+067+068+068+068+068+069+069+069+069+069+071+-270+-170+-170+-171+-270+-169+069+069+070+-117+0Male1000625.062500010.00625000182.88
8244G. Neville B3576EnglandManchester United78CBRB CB RWBhttps://cdn.sofifa.com/players/000/244/11_120.pnghttps://cdn.sofifa.com/teams/11/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png82Manchester United 1991 ~ 20115'10\"174lbsRight6Jan 1, 1991NaN0€001991 ~ 20113047231757155.02584456.0336164324647051.07267.02844770.06574283198785.045.070.032NaN24278...83.043129561117743783 ★2★NaNNaN3 ★673667538174454+054+054+060+056+056+056+060+056+056+056+062+064+064+064+062+073+074+074+074+073+076+078+078+078+076+013+0Male10.0010.000177.80
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN22477...72.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000180.34
13388S. Campbell3575EnglandNewcastle United75CBNaNhttps://cdn.sofifa.com/players/000/388/11_120.pnghttps://cdn.sofifa.com/teams/13/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png79Newcastle United 2010 ~ 20116'2\"190lbsRight4Sep 2, 2010NaN0€002010 ~ 20112062028815423.01734019.0154455321616844.06880.03195785.06487262968477.041.057.037NaN22272...73.044117125915813473 ★2★NaNNaN3 ★653443487681351+051+051+048+048+048+048+048+049+049+049+048+056+056+056+048+063+068+068+068+063+070+075+075+075+070+012+0Male10.0010.000187.96
15496D. Hamann3661GermanyMilton Keynes Dons67CBNaNhttps://cdn.sofifa.com/players/000/496/11_120.pnghttps://cdn.sofifa.com/teams/1798/light_60.pnghttps://cdn.sofifa.com/flags/de.png82Milton Keynes Dons 2010 ~ 20116'2\"168lbsRight21Sep 2, 2010NaN0€002010 ~ 20112705135627646.02883747.0766464212404428.03070.03307183.02882663447475.051.078.066NaN19870...56.05611121081516983433 ★2★NaNNaN3 ★425168466967351+051+051+050+051+051+051+050+059+059+059+053+061+061+061+053+058+065+065+065+058+062+067+067+067+062+014+0Male10.0010.000187.96
\n", - "

5 rows × 115 columns

\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 426 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#list with the name of every column\n", - "pd.options.display.max_columns = len(defenders.columns)\n", - "list(defenders.columns)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LtKNlIfCfcJj", - "outputId": "82027135-9834-4d01-e3ff-e6eb22c46a5d" - }, - "execution_count": 427, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['id',\n", - " 'name',\n", - " 'age',\n", - " 'ova',\n", - " 'nationality',\n", - " 'club',\n", - " 'bov',\n", - " 'bp',\n", - " 'position',\n", - " 'player photo',\n", - " 'club logo',\n", - " 'flag photo',\n", - " 'pot',\n", - " 'team & contract',\n", - " 'height',\n", - " 'weight',\n", - " 'foot',\n", - " 'growth',\n", - " 'joined',\n", - " 'loan date end',\n", - " 'value',\n", - " 'wage',\n", - " 'release clause',\n", - " 'contract',\n", - " 'attacking',\n", - " 'crossing',\n", - " 'finishing',\n", - " 'heading accuracy',\n", - " 'short passing',\n", - " 'volleys',\n", - " 'skill',\n", - " 'dribbling',\n", - " 'curve',\n", - " 'fk accuracy',\n", - " 'long passing',\n", - " 'ball control',\n", - " 'movement',\n", - " 'acceleration',\n", - " 'sprint speed',\n", - " 'agility',\n", - " 'reactions',\n", - " 'balance',\n", - " 'power',\n", - " 'shot power',\n", - " 'jumping',\n", - " 'stamina',\n", - " 'strength',\n", - " 'long shots',\n", - " 'mentality',\n", - " 'aggression',\n", - " 'interceptions',\n", - " 'positioning',\n", - " 'vision',\n", - " 'penalties',\n", - " 'composure',\n", - " 'defending',\n", - " 'marking',\n", - " 'standing tackle',\n", - " 'sliding tackle',\n", - " 'goalkeeping',\n", - " 'gk diving',\n", - " 'gk handling',\n", - " 'gk kicking',\n", - " 'gk positioning',\n", - " 'gk reflexes',\n", - " 'total stats',\n", - " 'base stats',\n", - " 'w/f',\n", - " 'sm',\n", - " 'a/w',\n", - " 'd/w',\n", - " 'ir',\n", - " 'pac',\n", - " 'sho',\n", - " 'pas',\n", - " 'dri',\n", - " 'def',\n", - " 'phy',\n", - " 'hits',\n", - " 'ls',\n", - " 'st',\n", - " 'rs',\n", - " 'lw',\n", - " 'lf',\n", - " 'cf',\n", - " 'rf',\n", - " 'rw',\n", - " 'lam',\n", - " 'cam',\n", - " 'ram',\n", - " 'lm',\n", - " 'lcm',\n", - " 'cm',\n", - " 'rcm',\n", - " 'rm',\n", - " 'lwb',\n", - " 'ldm',\n", - " 'cdm',\n", - " 'rdm',\n", - " 'rwb',\n", - " 'lb',\n", - " 'lcb',\n", - " 'cb',\n", - " 'rcb',\n", - " 'rb',\n", - " 'gk',\n", - " 'gender',\n", - " 'unit',\n", - " 'number',\n", - " 'market_value',\n", - " 'unit2',\n", - " 'number2',\n", - " 'release clause_total',\n", - " 'total_price']" - ] - }, - "metadata": {}, - "execution_count": 427 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#defenders.drop(columns=['unit','number'])" - ], - "metadata": { - "id": "ia0UPlXph6S_" - }, - "execution_count": 428, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "#The budget is 800.000€, so we have to sum the value of the player and the release clause.\n", - "defenders_filtered = defenders_filtered[(defenders_filtered['total_price'] < 800000)]\n", - "defenders_filtered = defenders_filtered[(defenders['wage'] != '€0')]\n", - "\n", - "defenders_filtered.shape" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pQiX1diUkX_M", - "outputId": "e6a33380-c5b1-4d72-aa3d-8f6af112e0ea" - }, - "execution_count": 429, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", - " defenders_filtered = defenders_filtered[(defenders['wage'] != '€0')]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(261, 115)" - ] - }, - "metadata": {}, - "execution_count": 429 - } - ] - }, - { - "cell_type": "code", - "source": [ - "defenders_filtered_stats = defenders_filtered[['name','age','nationality','height_cm','club','short passing','long passing','jumping','mentality','interceptions','defending', 'marking', 'standing tackle','sliding tackle','total stats','pas','def','phy','total_price']]\n", - "defenders_filtered_stats" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "K0ln1NzNmUuR", - "outputId": "abb0c6fc-114b-4772-a042-6770a3b537cb" - }, - "execution_count": 430, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name age nationality height_cm club \\\n", - "0 G. Pasquale 33 Italy 182.88 Udinese \n", - "10 P. Neville 35 England 180.34 Everton \n", - "24 A. Nesta 37 Italy 187.96 Montreal Impact \n", - "53 J. O'Shea 37 Republic of Ireland 190.50 Reading \n", - "59 A. Radomski 34 Poland 177.80 Cracovia \n", - "... ... ... ... ... ... \n", - "15930 I. González 36 Uruguay 182.88 Zamora FC \n", - "15938 A. Șeroni 33 Romania 195.58 FC Botoşani \n", - "16221 J. Stöckner 31 Germany 187.96 SC Verl \n", - "16682 W. Burrell 30 England 177.80 Harrogate Town \n", - "16943 M. Sus 30 Czech Republic 180.34 Stal Mielec \n", - "\n", - " short passing long passing jumping mentality interceptions \\\n", - "0 71 69 68.0 320 69.0 \n", - "10 74 72 71.0 335 83.0 \n", - "24 65 62 74.0 277 76.0 \n", - "53 59 56 54.0 264 68.0 \n", - "59 61 70 59.0 335 78.0 \n", - "... ... ... ... ... ... \n", - "15930 53 33 67.0 233 59.0 \n", - "15938 45 35 76.0 253 57.0 \n", - "16221 42 35 71.0 206 53.0 \n", - "16682 50 46 75.0 245 55.0 \n", - "16943 58 52 61.0 255 58.0 \n", - "\n", - " defending marking standing tackle sliding tackle total stats pas \\\n", - "0 208 70 69 69.0 1929 70 \n", - "10 224 77 75 72.0 1868 67 \n", - "24 221 74 77 70.0 1548 54 \n", - "53 200 70 66 64.0 1463 55 \n", - "59 176 47 64 65.0 1698 59 \n", - "... ... ... ... ... ... ... \n", - "15930 180 56 66 58.0 1322 40 \n", - "15938 192 65 65 62.0 1444 39 \n", - "16221 182 60 64 58.0 1315 32 \n", - "16682 169 56 57 56.0 1481 47 \n", - "16943 171 56 57 58.0 1620 58 \n", - "\n", - " def phy total_price \n", - "0 68 69 625000 \n", - "10 76 72 120000 \n", - "24 75 62 0 \n", - "53 68 60 216000 \n", - "59 62 66 50000 \n", - "... ... ... ... \n", - "15930 60 60 95000 \n", - "15938 63 79 588000 \n", - "16221 60 74 466000 \n", - "16682 56 66 330000 \n", - "16943 56 56 564000 \n", - "\n", - "[261 rows x 19 columns]" - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_price
0G. Pasquale33Italy182.88Udinese716968.032069.0208706969.01929706869625000
10P. Neville35England180.34Everton747271.033583.0224777572.01868677672120000
24A. Nesta37Italy187.96Montreal Impact656274.027776.0221747770.015485475620
53J. O'Shea37Republic of Ireland190.50Reading595654.026468.0200706664.01463556860216000
59A. Radomski34Poland177.80Cracovia617059.033578.0176476465.0169859626650000
............................................................
15930I. González36Uruguay182.88Zamora FC533367.023359.0180566658.0132240606095000
15938A. Șeroni33Romania195.58FC Botoşani453576.025357.0192656562.01444396379588000
16221J. Stöckner31Germany187.96SC Verl423571.020653.0182606458.01315326074466000
16682W. Burrell30England177.80Harrogate Town504675.024555.0169565756.01481475666330000
16943M. Sus30Czech Republic180.34Stal Mielec585261.025558.0171565758.01620585656564000
\n", - "

261 rows × 19 columns

\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 430 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#HACER AQUI LA NORMALIZACIÓN DE TODOS LOS DATOS NUMÉRICOS\n" - ], - "metadata": { - "id": "vVYVMjqwnAQY" - }, - "execution_count": 431, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "defenders_filtered_stats.dtypes\n", - "num_cols = defenders_filtered_stats.select_dtypes(include=['int64', 'float64']).columns.tolist()" - ], - "metadata": { - "id": "hCp4TefQnYH-" - }, - "execution_count": 432, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import sklearn\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "#from sklearn.preprocessing import StandardScaler" - ], - "metadata": { - "id": "fetDa8tYDAvo" - }, - "execution_count": 433, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "for col in num_cols:\n", - " col_max = defenders_filtered_stats[col].max()\n", - " col_min = defenders_filtered_stats[col].min()\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - "defenders_filtered_stats.tail()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "Z6XuaqN7E0I6", - "outputId": "bd5ce47e-2590-4833-967f-153ed3d7dcad" - }, - "execution_count": 434, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n", - ":4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats[col+\"_n\"] = (defenders_filtered_stats[col]-col_min)/(col_max-col_min)\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name age nationality height_cm club \\\n", - "15930 I. González 36 Uruguay 182.88 Zamora FC \n", - "15938 A. Șeroni 33 Romania 195.58 FC Botoşani \n", - "16221 J. Stöckner 31 Germany 187.96 SC Verl \n", - "16682 W. Burrell 30 England 177.80 Harrogate Town \n", - "16943 M. Sus 30 Czech Republic 180.34 Stal Mielec \n", - "\n", - " short passing long passing jumping mentality interceptions \\\n", - "15930 53 33 67.0 233 59.0 \n", - "15938 45 35 76.0 253 57.0 \n", - "16221 42 35 71.0 206 53.0 \n", - "16682 50 46 75.0 245 55.0 \n", - "16943 58 52 61.0 255 58.0 \n", - "\n", - " defending marking standing tackle sliding tackle total stats pas \\\n", - "15930 180 56 66 58.0 1322 40 \n", - "15938 192 65 65 62.0 1444 39 \n", - "16221 182 60 64 58.0 1315 32 \n", - "16682 169 56 57 56.0 1481 47 \n", - "16943 171 56 57 58.0 1620 58 \n", - "\n", - " def phy total_price age_n height_cm_n short passing_n \\\n", - "15930 60 60 95000 0.857143 0.538462 0.428571 \n", - "15938 63 79 588000 0.428571 0.923077 0.238095 \n", - "16221 60 74 466000 0.142857 0.692308 0.166667 \n", - "16682 56 66 330000 0.000000 0.384615 0.357143 \n", - "16943 56 56 564000 0.000000 0.461538 0.547619 \n", - "\n", - " long passing_n jumping_n mentality_n interceptions_n defending_n \\\n", - "15930 0.218182 0.600000 0.247368 0.325 0.329412 \n", - "15938 0.254545 0.738462 0.352632 0.275 0.470588 \n", - "16221 0.254545 0.661538 0.105263 0.175 0.352941 \n", - "16682 0.454545 0.723077 0.310526 0.225 0.200000 \n", - "16943 0.563636 0.507692 0.363158 0.300 0.223529 \n", - "\n", - " marking_n standing tackle_n sliding tackle_n total stats_n \\\n", - "15930 0.333333 0.46875 0.34375 0.157431 \n", - "15938 0.583333 0.43750 0.46875 0.311083 \n", - "16221 0.444444 0.40625 0.34375 0.148615 \n", - "16682 0.333333 0.18750 0.28125 0.357683 \n", - "16943 0.333333 0.18750 0.34375 0.532746 \n", - "\n", - " pas_n def_n phy_n total_price_n \n", - "15930 0.227273 0.344828 0.384615 0.119048 \n", - "15938 0.204545 0.448276 0.871795 0.736842 \n", - "16221 0.045455 0.344828 0.743590 0.583960 \n", - "16682 0.386364 0.206897 0.538462 0.413534 \n", - "16943 0.636364 0.206897 0.282051 0.706767 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_n
15930I. González36Uruguay182.88Zamora FC533367.023359.0180566658.01322406060950000.8571430.5384620.4285710.2181820.6000000.2473680.3250.3294120.3333330.468750.343750.1574310.2272730.3448280.3846150.119048
15938A. Șeroni33Romania195.58FC Botoşani453576.025357.0192656562.014443963795880000.4285710.9230770.2380950.2545450.7384620.3526320.2750.4705880.5833330.437500.468750.3110830.2045450.4482760.8717950.736842
16221J. Stöckner31Germany187.96SC Verl423571.020653.0182606458.013153260744660000.1428570.6923080.1666670.2545450.6615380.1052630.1750.3529410.4444440.406250.343750.1486150.0454550.3448280.7435900.583960
16682W. Burrell30England177.80Harrogate Town504675.024555.0169565756.014814756663300000.0000000.3846150.3571430.4545450.7230770.3105260.2250.2000000.3333330.187500.281250.3576830.3863640.2068970.5384620.413534
16943M. Sus30Czech Republic180.34Stal Mielec585261.025558.0171565758.016205856565640000.0000000.4615380.5476190.5636360.5076920.3631580.3000.2235290.3333330.187500.343750.5327460.6363640.2068970.2820510.706767
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 434 - } - ] - }, - { - "cell_type": "code", - "source": [ - "'''scaler = MinMaxScaler()\n", - "defenders_stats_norm = pd.DataFrame(scaler.fit_transform(defenders_filtered_stats[num_cols]),columns=num_cols)\n", - "defenders_stats_norm\n", - "'''" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "_0Wq1ZHwDSbx", - "outputId": "351266eb-ef29-40bd-aa0e-076b8adedbed" - }, - "execution_count": 435, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'scaler = MinMaxScaler()\\ndefenders_stats_norm = pd.DataFrame(scaler.fit_transform(defenders_filtered_stats[num_cols]),columns=num_cols)\\ndefenders_stats_norm\\n'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 435 - } - ] - }, - { - "cell_type": "code", - "source": [ - "defenders_filtered_stats['quality_value'] = (defenders_filtered_stats['short passing_n']+defenders_filtered_stats['long passing_n']+defenders_filtered_stats['jumping_n']+defenders_filtered_stats['interceptions_n']+defenders_filtered_stats['phy_n']).astype('float64')\n", - "display(defenders_filtered_stats.shape)\n", - "defenders_filtered_stats.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 484 - }, - "id": "_zN2TNwRiZjn", - "outputId": "0bf5cca6-54cc-4b66-e928-94704c13a670" - }, - "execution_count": 436, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats['quality_value'] = (defenders_filtered_stats['short passing_n']+defenders_filtered_stats['long passing_n']+defenders_filtered_stats['jumping_n']+defenders_filtered_stats['interceptions_n']+defenders_filtered_stats['phy_n']).astype('float64')\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "(261, 36)" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name age nationality height_cm club \\\n", - "0 G. Pasquale 33 Italy 182.88 Udinese \n", - "10 P. Neville 35 England 180.34 Everton \n", - "24 A. Nesta 37 Italy 187.96 Montreal Impact \n", - "53 J. O'Shea 37 Republic of Ireland 190.50 Reading \n", - "59 A. Radomski 34 Poland 177.80 Cracovia \n", - "\n", - " short passing long passing jumping mentality interceptions defending \\\n", - "0 71 69 68.0 320 69.0 208 \n", - "10 74 72 71.0 335 83.0 224 \n", - "24 65 62 74.0 277 76.0 221 \n", - "53 59 56 54.0 264 68.0 200 \n", - "59 61 70 59.0 335 78.0 176 \n", - "\n", - " marking standing tackle sliding tackle total stats pas def phy \\\n", - "0 70 69 69.0 1929 70 68 69 \n", - "10 77 75 72.0 1868 67 76 72 \n", - "24 74 77 70.0 1548 54 75 62 \n", - "53 70 66 64.0 1463 55 68 60 \n", - "59 47 64 65.0 1698 59 62 66 \n", - "\n", - " total_price age_n height_cm_n short passing_n long passing_n \\\n", - "0 625000 0.428571 0.538462 0.857143 0.872727 \n", - "10 120000 0.714286 0.461538 0.928571 0.927273 \n", - "24 0 1.000000 0.692308 0.714286 0.745455 \n", - "53 216000 1.000000 0.769231 0.571429 0.636364 \n", - "59 50000 0.571429 0.384615 0.619048 0.890909 \n", - "\n", - " jumping_n mentality_n interceptions_n defending_n marking_n \\\n", - "0 0.615385 0.705263 0.575 0.658824 0.722222 \n", - "10 0.661538 0.784211 0.925 0.847059 0.916667 \n", - "24 0.707692 0.478947 0.750 0.811765 0.833333 \n", - "53 0.400000 0.410526 0.550 0.564706 0.722222 \n", - "59 0.476923 0.784211 0.800 0.282353 0.083333 \n", - "\n", - " standing tackle_n sliding tackle_n total stats_n pas_n def_n \\\n", - "0 0.56250 0.68750 0.921914 0.909091 0.620690 \n", - "10 0.75000 0.78125 0.845088 0.840909 0.896552 \n", - "24 0.81250 0.71875 0.442065 0.545455 0.862069 \n", - "53 0.46875 0.53125 0.335013 0.568182 0.620690 \n", - "59 0.40625 0.56250 0.630982 0.659091 0.413793 \n", - "\n", - " phy_n total_price_n quality_value \n", - "0 0.615385 0.783208 3.535639 \n", - "10 0.692308 0.150376 4.134690 \n", - "24 0.435897 0.000000 3.353330 \n", - "53 0.384615 0.270677 2.542408 \n", - "59 0.538462 0.062657 3.325341 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_nquality_value
0G. Pasquale33Italy182.88Udinese716968.032069.0208706969.019297068696250000.4285710.5384620.8571430.8727270.6153850.7052630.5750.6588240.7222220.562500.687500.9219140.9090910.6206900.6153850.7832083.535639
10P. Neville35England180.34Everton747271.033583.0224777572.018686776721200000.7142860.4615380.9285710.9272730.6615380.7842110.9250.8470590.9166670.750000.781250.8450880.8409090.8965520.6923080.1503764.134690
24A. Nesta37Italy187.96Montreal Impact656274.027776.0221747770.0154854756201.0000000.6923080.7142860.7454550.7076920.4789470.7500.8117650.8333330.812500.718750.4420650.5454550.8620690.4358970.0000003.353330
53J. O'Shea37Republic of Ireland190.50Reading595654.026468.0200706664.014635568602160001.0000000.7692310.5714290.6363640.4000000.4105260.5500.5647060.7222220.468750.531250.3350130.5681820.6206900.3846150.2706772.542408
59A. Radomski34Poland177.80Cracovia617059.033578.0176476465.01698596266500000.5714290.3846150.6190480.8909090.4769230.7842110.8000.2823530.0833330.406250.562500.6309820.6590910.4137930.5384620.0626573.325341
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 436 - } - ] - }, - { - "cell_type": "code", - "source": [ - "#Select only the players above the 70 percentil of the quality value\n", - "bins = pd.qcut(defenders_filtered_stats['quality_value'],(0,0.8,1),labels=['lower','higher'])\n", - "defenders_filtered_stats['rank'] = bins\n", - "defenders_filtered_stats = defenders_filtered_stats[defenders_filtered_stats['rank']=='higher']\n", - "display(defenders_filtered_stats.shape)\n", - "defenders_filtered_stats" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "0OX9dZ5kYG0F", - "outputId": "dc5a5a27-df28-490c-e5af-344a380f9f81" - }, - "execution_count": 437, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats['rank'] = bins\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "(52, 37)" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name age nationality height_cm \\\n", - "0 G. Pasquale 33 Italy 182.88 \n", - "10 P. Neville 35 England 180.34 \n", - "24 A. Nesta 37 Italy 187.96 \n", - "92 G. Heinze 35 Argentina 177.80 \n", - "151 W. Samuel 37 Argentina 182.88 \n", - "156 O. Mellberg 35 Sweden 187.96 \n", - "161 S. Distin 37 France 193.04 \n", - "182 S. Diawara 35 Senegal 187.96 \n", - "210 Gilberto Silva 36 Brazil 182.88 \n", - "235 S. Cherundolo 34 United States 172.72 \n", - "257 M. Ambrosini 36 Italy 182.88 \n", - "294 L. Cufré 36 Argentina 175.26 \n", - "309 Lúcio 35 Brazil 187.96 \n", - "321 Borja Fernández 37 Spain 187.96 \n", - "327 S. Riether 35 Germany 175.26 \n", - "354 K. Touré 35 Ivory Coast 177.80 \n", - "362 C. Schulz 34 Germany 185.42 \n", - "363 A. Tymoshchuk 35 Ukraine 180.34 \n", - "393 Junior Cesar 31 Brazil 165.10 \n", - "399 Cris 36 Brazil 182.88 \n", - "404 P. García 36 Uruguay 185.42 \n", - "424 M. Gobbi 37 Italy 182.88 \n", - "425 M. Cassani 33 Italy 182.88 \n", - "463 C. Rodríguez 36 Argentina 167.64 \n", - "496 D. Verón 36 Paraguay 180.34 \n", - "516 J. Thomsen 37 Denmark 180.34 \n", - "669 Àngel Rangel 36 Spain 177.80 \n", - "680 L. Perea 35 Colombia 180.34 \n", - "724 D. Pratley 35 England 185.42 \n", - "726 G. Sardo 37 Italy 190.50 \n", - "803 D. Pérez 34 Uruguay 177.80 \n", - "861 Evaldo 33 Brazil 182.88 \n", - "925 D. Moor 36 United States 182.88 \n", - "1004 J. Polák 34 Czech Republic 180.34 \n", - "1170 Bolaño 34 Spain 182.88 \n", - "1414 V. Elm 34 Sweden 190.50 \n", - "1465 A. Raineau 34 France 177.80 \n", - "1653 N. Topor-Stanley 35 Australia 190.50 \n", - "1664 L. Broxham 32 Australia 170.18 \n", - "1782 B. Sigmund 34 New Zealand 187.96 \n", - "1984 A. Collin 34 France 187.96 \n", - "2298 A. Meijers 32 Netherlands 175.26 \n", - "2467 Lombán 33 Spain 185.42 \n", - "2531 R. Austin 34 Jamaica 182.88 \n", - "2711 M. Inoha 34 Japan 177.80 \n", - "2881 H. Mulder 33 Netherlands 180.34 \n", - "3084 T. Makino 33 Japan 182.88 \n", - "3374 Carlos Ruiz 36 Spain 182.88 \n", - "4267 D. Kempe 34 Germany 187.96 \n", - "7770 M. Čovilo 34 Bosnia Herzegovina 193.04 \n", - "15408 C. Caraza 34 Peru 175.26 \n", - "15426 V. Balta 34 Peru 180.34 \n", - "\n", - " club short passing long passing jumping \\\n", - "0 Udinese 71 69 68.0 \n", - "10 Everton 74 72 71.0 \n", - "24 Montreal Impact 65 62 74.0 \n", - "92 Newell's Old Boys 60 66 76.0 \n", - "151 FC Basel 1893 66 67 75.0 \n", - "156 FC København 63 67 66.0 \n", - "161 Bournemouth 66 64 63.0 \n", - "182 OGC Nice 63 59 72.0 \n", - "210 Atlético Mineiro 68 63 51.0 \n", - "235 Hannover 96 75 71 81.0 \n", - "257 Fiorentina 76 76 92.0 \n", - "294 Leones Negros de la UdeG 63 61 82.0 \n", - "309 Palmeiras 65 65 80.0 \n", - "321 Real Valladolid CF 66 64 70.0 \n", - "327 FC Schalke 04 74 65 71.0 \n", - "354 Celtic 70 65 72.0 \n", - "362 SK Sturm Graz 67 65 72.0 \n", - "363 Zenit St. Petersburg 72 70 76.0 \n", - "393 Botafogo 71 66 82.0 \n", - "399 Vasco da Gama 72 70 73.0 \n", - "404 PAOK 77 76 65.0 \n", - "424 Parma 71 71 67.0 \n", - "425 Bari 70 67 70.0 \n", - "463 Club Atlético Colón 67 62 87.0 \n", - "496 Paraguay 63 64 80.0 \n", - "516 Randers FC 65 62 81.0 \n", - "669 Queens Park Rangers 68 67 75.0 \n", - "680 Cruz Azul 59 52 90.0 \n", - "724 Charlton Athletic 64 61 87.0 \n", - "726 Chievo Verona 60 60 78.0 \n", - "803 Bologna 65 66 74.0 \n", - "861 Moreirense FC 67 64 76.0 \n", - "925 Colorado Rapids 68 68 71.0 \n", - "1004 1. FC Nürnberg 68 63 68.0 \n", - "1170 Real Oviedo 62 61 75.0 \n", - "1414 Kalmar FF 66 63 81.0 \n", - "1465 La Berrichonne de Châteauroux 66 62 80.0 \n", - "1653 Newcastle Jets 58 60 82.0 \n", - "1664 Melbourne Victory 61 59 93.0 \n", - "1782 Wellington Phoenix 58 55 92.0 \n", - "1984 Philadelphia Union 59 63 82.0 \n", - "2298 Sparta Rotterdam 65 67 79.0 \n", - "2467 Málaga CF 63 69 71.0 \n", - "2531 Esbjerg fB 64 62 80.0 \n", - "2711 Yokohama FC 61 65 92.0 \n", - "2881 RKC Waalwijk 66 63 73.0 \n", - "3084 Urawa Red Diamonds 66 62 78.0 \n", - "3374 CD Tenerife 67 61 83.0 \n", - "4267 SV Wehen Wiesbaden 62 58 76.0 \n", - "7770 FC Lugano 63 62 86.0 \n", - "15408 Sport Huancayo 68 72 72.0 \n", - "15426 Sport Huancayo 60 57 85.0 \n", - "\n", - " mentality interceptions defending marking standing tackle \\\n", - "0 320 69.0 208 70 69 \n", - "10 335 83.0 224 77 75 \n", - "24 277 76.0 221 74 77 \n", - "92 305 76.0 227 76 76 \n", - "151 293 86.0 220 74 74 \n", - "156 302 78.0 215 75 77 \n", - "161 304 75.0 237 80 79 \n", - "182 269 71.0 214 71 71 \n", - "210 330 79.0 229 76 79 \n", - "235 324 73.0 224 74 73 \n", - "257 376 82.0 226 70 78 \n", - "294 284 65.0 206 69 69 \n", - "309 340 75.0 227 75 79 \n", - "321 337 70.0 206 70 74 \n", - "327 310 72.0 220 78 73 \n", - "354 315 72.0 223 72 75 \n", - "362 287 66.0 196 65 67 \n", - "363 359 80.0 237 77 81 \n", - "393 338 72.0 210 68 66 \n", - "399 320 69.0 201 68 68 \n", - "404 345 75.0 214 70 73 \n", - "424 313 75.0 222 65 78 \n", - "425 305 71.0 203 63 68 \n", - "463 311 67.0 199 60 70 \n", - "496 306 72.0 215 72 74 \n", - "516 289 68.0 196 64 65 \n", - "669 334 72.0 215 69 74 \n", - "680 284 74.0 227 75 76 \n", - "724 338 71.0 197 71 62 \n", - "726 303 71.0 226 75 78 \n", - "803 346 72.0 208 65 72 \n", - "861 307 66.0 215 68 75 \n", - "925 273 66.0 199 68 66 \n", - "1004 304 73.0 197 65 68 \n", - "1170 305 67.0 199 66 67 \n", - "1414 311 66.0 191 68 64 \n", - "1465 303 64.0 200 68 65 \n", - "1653 245 60.0 178 61 60 \n", - "1664 303 61.0 194 61 66 \n", - "1782 234 60.0 183 62 61 \n", - "1984 287 64.0 181 61 60 \n", - "2298 325 64.0 193 63 65 \n", - "2467 290 68.0 197 68 65 \n", - "2531 300 60.0 186 65 64 \n", - "2711 271 66.0 184 60 63 \n", - "2881 313 68.0 187 60 64 \n", - "3084 289 61.0 193 61 65 \n", - "3374 286 63.0 187 64 63 \n", - "4267 296 65.0 196 61 66 \n", - "7770 294 69.0 192 58 69 \n", - "15408 315 64.0 192 65 65 \n", - "15426 280 67.0 184 61 63 \n", - "\n", - " sliding tackle total stats pas def phy total_price age_n \\\n", - "0 69.0 1929 70 68 69 625000 0.428571 \n", - "10 72.0 1868 67 76 72 120000 0.714286 \n", - "24 70.0 1548 54 75 62 0 1.000000 \n", - "92 75.0 1784 63 76 68 300000 0.714286 \n", - "151 72.0 1560 54 76 66 0 1.000000 \n", - "156 63.0 1634 55 76 75 300000 0.714286 \n", - "161 78.0 1679 56 79 78 0 1.000000 \n", - "182 72.0 1606 55 71 75 400000 0.714286 \n", - "210 74.0 1695 61 77 69 0 0.857143 \n", - "235 77.0 1918 69 73 68 400000 0.571429 \n", - "257 78.0 1957 71 77 73 0 0.857143 \n", - "294 68.0 1612 56 69 73 0 0.857143 \n", - "309 73.0 1807 58 77 75 450000 0.714286 \n", - "321 62.0 1758 63 70 75 378000 1.000000 \n", - "327 69.0 1815 66 73 61 621000 0.714286 \n", - "354 76.0 1816 60 74 79 700000 0.714286 \n", - "362 64.0 1659 60 66 70 667000 0.571429 \n", - "363 79.0 1965 68 78 72 375000 0.714286 \n", - "393 76.0 1991 66 68 62 575000 0.142857 \n", - "399 65.0 1664 63 69 71 0 0.857143 \n", - "404 71.0 1900 74 72 78 0 0.857143 \n", - "424 79.0 1867 68 73 65 596000 1.000000 \n", - "425 72.0 1806 64 67 69 710000 0.428571 \n", - "463 69.0 1864 65 64 71 250000 0.857143 \n", - "496 69.0 1771 56 73 73 0 0.857143 \n", - "516 67.0 1729 63 64 68 225000 1.000000 \n", - "669 72.0 1853 69 72 67 464000 0.857143 \n", - "680 76.0 1724 51 75 80 550000 0.714286 \n", - "724 64.0 1880 62 67 78 633000 0.714286 \n", - "726 73.0 1807 60 75 75 0 1.000000 \n", - "803 71.0 1845 62 70 81 400000 0.571429 \n", - "861 72.0 1839 64 70 70 375000 0.428571 \n", - "925 65.0 1614 59 67 69 368000 0.857143 \n", - "1004 64.0 1735 61 67 69 220000 0.571429 \n", - "1170 66.0 1806 59 67 73 715000 0.571429 \n", - "1414 59.0 1743 61 66 77 731000 0.571429 \n", - "1465 67.0 1765 63 65 71 523000 0.571429 \n", - "1653 57.0 1507 53 60 84 315000 0.714286 \n", - "1664 67.0 1748 55 63 84 788000 0.285714 \n", - "1782 60.0 1416 44 61 82 130000 0.571429 \n", - "1984 60.0 1560 52 62 75 600000 0.571429 \n", - "2298 65.0 1915 67 64 72 550000 0.285714 \n", - "2467 64.0 1696 59 67 69 780000 0.428571 \n", - "2531 57.0 1685 59 63 80 731000 0.571429 \n", - "2711 61.0 1633 55 62 73 383000 0.571429 \n", - "2881 63.0 1742 62 63 75 735000 0.428571 \n", - "3084 67.0 1727 60 64 76 788000 0.428571 \n", - "3374 60.0 1752 57 64 77 390000 0.857143 \n", - "4267 69.0 1730 58 64 77 674000 0.571429 \n", - "7770 65.0 1636 58 65 76 515000 0.571429 \n", - "15408 62.0 1852 69 62 70 725000 0.571429 \n", - "15426 60.0 1652 52 63 75 630000 0.571429 \n", - "\n", - " height_cm_n short passing_n long passing_n jumping_n mentality_n \\\n", - "0 0.538462 0.857143 0.872727 0.615385 0.705263 \n", - "10 0.461538 0.928571 0.927273 0.661538 0.784211 \n", - "24 0.692308 0.714286 0.745455 0.707692 0.478947 \n", - "92 0.384615 0.595238 0.818182 0.738462 0.626316 \n", - "151 0.538462 0.738095 0.836364 0.723077 0.563158 \n", - "156 0.692308 0.666667 0.836364 0.584615 0.610526 \n", - "161 0.846154 0.738095 0.781818 0.538462 0.621053 \n", - "182 0.692308 0.666667 0.690909 0.676923 0.436842 \n", - "210 0.538462 0.785714 0.763636 0.353846 0.757895 \n", - "235 0.230769 0.952381 0.909091 0.815385 0.726316 \n", - "257 0.538462 0.976190 1.000000 0.984615 1.000000 \n", - "294 0.307692 0.666667 0.727273 0.830769 0.515789 \n", - "309 0.692308 0.714286 0.800000 0.800000 0.810526 \n", - "321 0.692308 0.738095 0.781818 0.646154 0.794737 \n", - "327 0.307692 0.928571 0.800000 0.661538 0.652632 \n", - "354 0.384615 0.833333 0.800000 0.676923 0.678947 \n", - "362 0.615385 0.761905 0.800000 0.676923 0.531579 \n", - "363 0.461538 0.880952 0.890909 0.738462 0.910526 \n", - "393 0.000000 0.857143 0.818182 0.830769 0.800000 \n", - "399 0.538462 0.880952 0.890909 0.692308 0.705263 \n", - "404 0.615385 1.000000 1.000000 0.569231 0.836842 \n", - "424 0.538462 0.857143 0.909091 0.600000 0.668421 \n", - "425 0.538462 0.833333 0.836364 0.646154 0.626316 \n", - "463 0.076923 0.761905 0.745455 0.907692 0.657895 \n", - "496 0.461538 0.666667 0.781818 0.800000 0.631579 \n", - "516 0.461538 0.714286 0.745455 0.815385 0.542105 \n", - "669 0.384615 0.785714 0.836364 0.723077 0.778947 \n", - "680 0.461538 0.571429 0.563636 0.953846 0.515789 \n", - "724 0.615385 0.690476 0.727273 0.907692 0.800000 \n", - "726 0.769231 0.595238 0.709091 0.769231 0.615789 \n", - "803 0.384615 0.714286 0.818182 0.707692 0.842105 \n", - "861 0.538462 0.761905 0.781818 0.738462 0.636842 \n", - "925 0.538462 0.785714 0.854545 0.661538 0.457895 \n", - "1004 0.461538 0.785714 0.763636 0.615385 0.621053 \n", - "1170 0.538462 0.642857 0.727273 0.723077 0.626316 \n", - "1414 0.769231 0.738095 0.763636 0.815385 0.657895 \n", - "1465 0.384615 0.738095 0.745455 0.800000 0.615789 \n", - "1653 0.769231 0.547619 0.709091 0.830769 0.310526 \n", - "1664 0.153846 0.619048 0.690909 1.000000 0.615789 \n", - "1782 0.692308 0.547619 0.618182 0.984615 0.252632 \n", - "1984 0.692308 0.571429 0.763636 0.830769 0.531579 \n", - "2298 0.307692 0.714286 0.836364 0.784615 0.731579 \n", - "2467 0.615385 0.666667 0.872727 0.661538 0.547368 \n", - "2531 0.538462 0.690476 0.745455 0.800000 0.600000 \n", - "2711 0.384615 0.619048 0.800000 0.984615 0.447368 \n", - "2881 0.461538 0.738095 0.763636 0.692308 0.668421 \n", - "3084 0.538462 0.738095 0.745455 0.769231 0.542105 \n", - "3374 0.538462 0.761905 0.727273 0.846154 0.526316 \n", - "4267 0.692308 0.642857 0.672727 0.738462 0.578947 \n", - "7770 0.846154 0.666667 0.745455 0.892308 0.568421 \n", - "15408 0.307692 0.785714 0.927273 0.676923 0.678947 \n", - "15426 0.461538 0.595238 0.654545 0.876923 0.494737 \n", - "\n", - " interceptions_n defending_n marking_n standing tackle_n \\\n", - "0 0.575 0.658824 0.722222 0.56250 \n", - "10 0.925 0.847059 0.916667 0.75000 \n", - "24 0.750 0.811765 0.833333 0.81250 \n", - "92 0.750 0.882353 0.888889 0.78125 \n", - "151 1.000 0.800000 0.833333 0.71875 \n", - "156 0.800 0.741176 0.861111 0.81250 \n", - "161 0.725 1.000000 1.000000 0.87500 \n", - "182 0.625 0.729412 0.750000 0.62500 \n", - "210 0.825 0.905882 0.888889 0.87500 \n", - "235 0.675 0.847059 0.833333 0.68750 \n", - "257 0.900 0.870588 0.722222 0.84375 \n", - "294 0.475 0.635294 0.694444 0.56250 \n", - "309 0.725 0.882353 0.861111 0.87500 \n", - "321 0.600 0.635294 0.722222 0.71875 \n", - "327 0.650 0.800000 0.944444 0.68750 \n", - "354 0.650 0.835294 0.777778 0.75000 \n", - "362 0.500 0.517647 0.583333 0.50000 \n", - "363 0.850 1.000000 0.916667 0.93750 \n", - "393 0.650 0.682353 0.666667 0.46875 \n", - "399 0.575 0.576471 0.666667 0.53125 \n", - "404 0.725 0.729412 0.722222 0.68750 \n", - "424 0.725 0.823529 0.583333 0.84375 \n", - "425 0.625 0.600000 0.527778 0.53125 \n", - "463 0.525 0.552941 0.444444 0.59375 \n", - "496 0.650 0.741176 0.777778 0.71875 \n", - "516 0.550 0.517647 0.555556 0.43750 \n", - "669 0.650 0.741176 0.694444 0.71875 \n", - "680 0.700 0.882353 0.861111 0.78125 \n", - "724 0.625 0.529412 0.750000 0.34375 \n", - "726 0.625 0.870588 0.861111 0.84375 \n", - "803 0.650 0.658824 0.583333 0.65625 \n", - "861 0.500 0.741176 0.666667 0.75000 \n", - "925 0.500 0.552941 0.666667 0.46875 \n", - "1004 0.675 0.529412 0.583333 0.53125 \n", - "1170 0.525 0.552941 0.611111 0.50000 \n", - "1414 0.500 0.458824 0.666667 0.40625 \n", - "1465 0.450 0.564706 0.666667 0.43750 \n", - "1653 0.350 0.305882 0.472222 0.28125 \n", - "1664 0.375 0.494118 0.472222 0.46875 \n", - "1782 0.350 0.364706 0.500000 0.31250 \n", - "1984 0.450 0.341176 0.472222 0.28125 \n", - "2298 0.450 0.482353 0.527778 0.43750 \n", - "2467 0.550 0.529412 0.666667 0.43750 \n", - "2531 0.350 0.400000 0.583333 0.40625 \n", - "2711 0.500 0.376471 0.444444 0.37500 \n", - "2881 0.550 0.411765 0.444444 0.40625 \n", - "3084 0.375 0.482353 0.472222 0.43750 \n", - "3374 0.425 0.411765 0.555556 0.37500 \n", - "4267 0.475 0.517647 0.472222 0.46875 \n", - "7770 0.575 0.470588 0.388889 0.56250 \n", - "15408 0.450 0.470588 0.583333 0.43750 \n", - "15426 0.525 0.376471 0.472222 0.37500 \n", - "\n", - " sliding tackle_n total stats_n pas_n def_n phy_n \\\n", - "0 0.68750 0.921914 0.909091 0.620690 0.615385 \n", - "10 0.78125 0.845088 0.840909 0.896552 0.692308 \n", - "24 0.71875 0.442065 0.545455 0.862069 0.435897 \n", - "92 0.87500 0.739295 0.750000 0.896552 0.589744 \n", - "151 0.78125 0.457179 0.545455 0.896552 0.538462 \n", - "156 0.50000 0.550378 0.568182 0.896552 0.769231 \n", - "161 0.96875 0.607053 0.590909 1.000000 0.846154 \n", - "182 0.78125 0.515113 0.568182 0.724138 0.769231 \n", - "210 0.84375 0.627204 0.704545 0.931034 0.615385 \n", - "235 0.93750 0.908060 0.886364 0.793103 0.589744 \n", - "257 0.96875 0.957179 0.931818 0.931034 0.717949 \n", - "294 0.65625 0.522670 0.590909 0.655172 0.717949 \n", - "309 0.81250 0.768262 0.636364 0.931034 0.769231 \n", - "321 0.46875 0.706549 0.750000 0.689655 0.769231 \n", - "327 0.68750 0.778338 0.818182 0.793103 0.410256 \n", - "354 0.90625 0.779597 0.681818 0.827586 0.871795 \n", - "362 0.53125 0.581864 0.681818 0.551724 0.641026 \n", - "363 1.00000 0.967254 0.863636 0.965517 0.692308 \n", - "393 0.90625 1.000000 0.818182 0.620690 0.435897 \n", - "399 0.56250 0.588161 0.750000 0.655172 0.666667 \n", - "404 0.75000 0.885390 1.000000 0.758621 0.846154 \n", - "424 1.00000 0.843829 0.863636 0.793103 0.512821 \n", - "425 0.78125 0.767003 0.772727 0.586207 0.615385 \n", - "463 0.68750 0.840050 0.795455 0.482759 0.666667 \n", - "496 0.68750 0.722922 0.590909 0.793103 0.717949 \n", - "516 0.62500 0.670025 0.750000 0.482759 0.589744 \n", - "669 0.78125 0.826196 0.886364 0.758621 0.564103 \n", - "680 0.90625 0.663728 0.477273 0.862069 0.897436 \n", - "724 0.53125 0.860202 0.727273 0.586207 0.846154 \n", - "726 0.81250 0.768262 0.681818 0.862069 0.769231 \n", - "803 0.75000 0.816121 0.727273 0.689655 0.923077 \n", - "861 0.78125 0.808564 0.772727 0.689655 0.641026 \n", - "925 0.56250 0.525189 0.659091 0.586207 0.615385 \n", - "1004 0.53125 0.677582 0.704545 0.586207 0.615385 \n", - "1170 0.59375 0.767003 0.659091 0.586207 0.717949 \n", - "1414 0.37500 0.687657 0.704545 0.551724 0.820513 \n", - "1465 0.62500 0.715365 0.750000 0.517241 0.666667 \n", - "1653 0.31250 0.390428 0.522727 0.344828 1.000000 \n", - "1664 0.62500 0.693955 0.568182 0.448276 1.000000 \n", - "1782 0.40625 0.275819 0.318182 0.379310 0.948718 \n", - "1984 0.40625 0.457179 0.500000 0.413793 0.769231 \n", - "2298 0.56250 0.904282 0.840909 0.482759 0.692308 \n", - "2467 0.53125 0.628463 0.659091 0.586207 0.615385 \n", - "2531 0.31250 0.614610 0.659091 0.448276 0.897436 \n", - "2711 0.43750 0.549118 0.568182 0.413793 0.717949 \n", - "2881 0.50000 0.686398 0.727273 0.448276 0.769231 \n", - "3084 0.62500 0.667506 0.681818 0.482759 0.794872 \n", - "3374 0.40625 0.698992 0.613636 0.482759 0.820513 \n", - "4267 0.68750 0.671285 0.636364 0.482759 0.820513 \n", - "7770 0.56250 0.552897 0.636364 0.517241 0.794872 \n", - "15408 0.46875 0.824937 0.886364 0.413793 0.641026 \n", - "15426 0.40625 0.573048 0.500000 0.448276 0.769231 \n", - "\n", - " total_price_n quality_value rank \n", - "0 0.783208 3.535639 higher \n", - "10 0.150376 4.134690 higher \n", - "24 0.000000 3.353330 higher \n", - "92 0.375940 3.491625 higher \n", - "151 0.000000 3.835997 higher \n", - "156 0.375940 3.656876 higher \n", - "161 0.000000 3.629529 higher \n", - "182 0.501253 3.428730 higher \n", - "210 0.000000 3.343581 higher \n", - "235 0.501253 3.941600 higher \n", - "257 0.000000 4.578755 higher \n", - "294 0.000000 3.417657 higher \n", - "309 0.563910 3.808516 higher \n", - "321 0.473684 3.535298 higher \n", - "327 0.778195 3.450366 higher \n", - "354 0.877193 3.832051 higher \n", - "362 0.835840 3.379853 higher \n", - "363 0.469925 4.052631 higher \n", - "393 0.720551 3.591991 higher \n", - "399 0.000000 3.705836 higher \n", - "404 0.000000 4.140385 higher \n", - "424 0.746867 3.604054 higher \n", - "425 0.889724 3.556235 higher \n", - "463 0.313283 3.606718 higher \n", - "496 0.000000 3.616434 higher \n", - "516 0.281955 3.414868 higher \n", - "669 0.581454 3.559257 higher \n", - "680 0.689223 3.686347 higher \n", - "724 0.793233 3.796595 higher \n", - "726 0.000000 3.467791 higher \n", - "803 0.501253 3.813237 higher \n", - "861 0.469925 3.423210 higher \n", - "925 0.461153 3.417183 higher \n", - "1004 0.275689 3.455120 higher \n", - "1170 0.895990 3.336156 higher \n", - "1414 0.916040 3.637629 higher \n", - "1465 0.655388 3.400216 higher \n", - "1653 0.394737 3.437479 higher \n", - "1664 0.987469 3.684957 higher \n", - "1782 0.162907 3.449134 higher \n", - "1984 0.751880 3.385065 higher \n", - "2298 0.689223 3.477572 higher \n", - "2467 0.977444 3.366317 higher \n", - "2531 0.916040 3.483367 higher \n", - "2711 0.479950 3.621612 higher \n", - "2881 0.921053 3.513270 higher \n", - "3084 0.987469 3.422652 higher \n", - "3374 0.488722 3.580844 higher \n", - "4267 0.844612 3.349559 higher \n", - "7770 0.645363 3.674301 higher \n", - "15408 0.908521 3.480936 higher \n", - "15426 0.789474 3.420937 higher " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_nquality_valuerank
0G. Pasquale33Italy182.88Udinese716968.032069.0208706969.019297068696250000.4285710.5384620.8571430.8727270.6153850.7052630.5750.6588240.7222220.562500.687500.9219140.9090910.6206900.6153850.7832083.535639higher
10P. Neville35England180.34Everton747271.033583.0224777572.018686776721200000.7142860.4615380.9285710.9272730.6615380.7842110.9250.8470590.9166670.750000.781250.8450880.8409090.8965520.6923080.1503764.134690higher
24A. Nesta37Italy187.96Montreal Impact656274.027776.0221747770.0154854756201.0000000.6923080.7142860.7454550.7076920.4789470.7500.8117650.8333330.812500.718750.4420650.5454550.8620690.4358970.0000003.353330higher
92G. Heinze35Argentina177.80Newell's Old Boys606676.030576.0227767675.017846376683000000.7142860.3846150.5952380.8181820.7384620.6263160.7500.8823530.8888890.781250.875000.7392950.7500000.8965520.5897440.3759403.491625higher
151W. Samuel37Argentina182.88FC Basel 1893666775.029386.0220747472.0156054766601.0000000.5384620.7380950.8363640.7230770.5631581.0000.8000000.8333330.718750.781250.4571790.5454550.8965520.5384620.0000003.835997higher
156O. Mellberg35Sweden187.96FC København636766.030278.0215757763.016345576753000000.7142860.6923080.6666670.8363640.5846150.6105260.8000.7411760.8611110.812500.500000.5503780.5681820.8965520.7692310.3759403.656876higher
161S. Distin37France193.04Bournemouth666463.030475.0237807978.0167956797801.0000000.8461540.7380950.7818180.5384620.6210530.7251.0000001.0000000.875000.968750.6070530.5909091.0000000.8461540.0000003.629529higher
182S. Diawara35Senegal187.96OGC Nice635972.026971.0214717172.016065571754000000.7142860.6923080.6666670.6909090.6769230.4368420.6250.7294120.7500000.625000.781250.5151130.5681820.7241380.7692310.5012533.428730higher
210Gilberto Silva36Brazil182.88Atlético Mineiro686351.033079.0229767974.0169561776900.8571430.5384620.7857140.7636360.3538460.7578950.8250.9058820.8888890.875000.843750.6272040.7045450.9310340.6153850.0000003.343581higher
235S. Cherundolo34United States172.72Hannover 96757181.032473.0224747377.019186973684000000.5714290.2307690.9523810.9090910.8153850.7263160.6750.8470590.8333330.687500.937500.9080600.8863640.7931030.5897440.5012533.941600higher
257M. Ambrosini36Italy182.88Fiorentina767692.037682.0226707878.0195771777300.8571430.5384620.9761901.0000000.9846151.0000000.9000.8705880.7222220.843750.968750.9571790.9318180.9310340.7179490.0000004.578755higher
294L. Cufré36Argentina175.26Leones Negros de la UdeG636182.028465.0206696968.0161256697300.8571430.3076920.6666670.7272730.8307690.5157890.4750.6352940.6944440.562500.656250.5226700.5909090.6551720.7179490.0000003.417657higher
309Lúcio35Brazil187.96Palmeiras656580.034075.0227757973.018075877754500000.7142860.6923080.7142860.8000000.8000000.8105260.7250.8823530.8611110.875000.812500.7682620.6363640.9310340.7692310.5639103.808516higher
321Borja Fernández37Spain187.96Real Valladolid CF666470.033770.0206707462.017586370753780001.0000000.6923080.7380950.7818180.6461540.7947370.6000.6352940.7222220.718750.468750.7065490.7500000.6896550.7692310.4736843.535298higher
327S. Riether35Germany175.26FC Schalke 04746571.031072.0220787369.018156673616210000.7142860.3076920.9285710.8000000.6615380.6526320.6500.8000000.9444440.687500.687500.7783380.8181820.7931030.4102560.7781953.450366higher
354K. Touré35Ivory Coast177.80Celtic706572.031572.0223727576.018166074797000000.7142860.3846150.8333330.8000000.6769230.6789470.6500.8352940.7777780.750000.906250.7795970.6818180.8275860.8717950.8771933.832051higher
362C. Schulz34Germany185.42SK Sturm Graz676572.028766.0196656764.016596066706670000.5714290.6153850.7619050.8000000.6769230.5315790.5000.5176470.5833330.500000.531250.5818640.6818180.5517240.6410260.8358403.379853higher
363A. Tymoshchuk35Ukraine180.34Zenit St. Petersburg727076.035980.0237778179.019656878723750000.7142860.4615380.8809520.8909090.7384620.9105260.8501.0000000.9166670.937501.000000.9672540.8636360.9655170.6923080.4699254.052631higher
393Junior Cesar31Brazil165.10Botafogo716682.033872.0210686676.019916668625750000.1428570.0000000.8571430.8181820.8307690.8000000.6500.6823530.6666670.468750.906251.0000000.8181820.6206900.4358970.7205513.591991higher
399Cris36Brazil182.88Vasco da Gama727073.032069.0201686865.0166463697100.8571430.5384620.8809520.8909090.6923080.7052630.5750.5764710.6666670.531250.562500.5881610.7500000.6551720.6666670.0000003.705836higher
404P. García36Uruguay185.42PAOK777665.034575.0214707371.0190074727800.8571430.6153851.0000001.0000000.5692310.8368420.7250.7294120.7222220.687500.750000.8853901.0000000.7586210.8461540.0000004.140385higher
424M. Gobbi37Italy182.88Parma717167.031375.0222657879.018676873655960001.0000000.5384620.8571430.9090910.6000000.6684210.7250.8235290.5833330.843751.000000.8438290.8636360.7931030.5128210.7468673.604054higher
425M. Cassani33Italy182.88Bari706770.030571.0203636872.018066467697100000.4285710.5384620.8333330.8363640.6461540.6263160.6250.6000000.5277780.531250.781250.7670030.7727270.5862070.6153850.8897243.556235higher
463C. Rodríguez36Argentina167.64Club Atlético Colón676287.031167.0199607069.018646564712500000.8571430.0769230.7619050.7454550.9076920.6578950.5250.5529410.4444440.593750.687500.8400500.7954550.4827590.6666670.3132833.606718higher
496D. Verón36Paraguay180.34Paraguay636480.030672.0215727469.0177156737300.8571430.4615380.6666670.7818180.8000000.6315790.6500.7411760.7777780.718750.687500.7229220.5909090.7931030.7179490.0000003.616434higher
516J. Thomsen37Denmark180.34Randers FC656281.028968.0196646567.017296364682250001.0000000.4615380.7142860.7454550.8153850.5421050.5500.5176470.5555560.437500.625000.6700250.7500000.4827590.5897440.2819553.414868higher
669Àngel Rangel36Spain177.80Queens Park Rangers686775.033472.0215697472.018536972674640000.8571430.3846150.7857140.8363640.7230770.7789470.6500.7411760.6944440.718750.781250.8261960.8863640.7586210.5641030.5814543.559257higher
680L. Perea35Colombia180.34Cruz Azul595290.028474.0227757676.017245175805500000.7142860.4615380.5714290.5636360.9538460.5157890.7000.8823530.8611110.781250.906250.6637280.4772730.8620690.8974360.6892233.686347higher
724D. Pratley35England185.42Charlton Athletic646187.033871.0197716264.018806267786330000.7142860.6153850.6904760.7272730.9076920.8000000.6250.5294120.7500000.343750.531250.8602020.7272730.5862070.8461540.7932333.796595higher
726G. Sardo37Italy190.50Chievo Verona606078.030371.0226757873.0180760757501.0000000.7692310.5952380.7090910.7692310.6157890.6250.8705880.8611110.843750.812500.7682620.6818180.8620690.7692310.0000003.467791higher
803D. Pérez34Uruguay177.80Bologna656674.034672.0208657271.018456270814000000.5714290.3846150.7142860.8181820.7076920.8421050.6500.6588240.5833330.656250.750000.8161210.7272730.6896550.9230770.5012533.813237higher
861Evaldo33Brazil182.88Moreirense FC676476.030766.0215687572.018396470703750000.4285710.5384620.7619050.7818180.7384620.6368420.5000.7411760.6666670.750000.781250.8085640.7727270.6896550.6410260.4699253.423210higher
925D. Moor36United States182.88Colorado Rapids686871.027366.0199686665.016145967693680000.8571430.5384620.7857140.8545450.6615380.4578950.5000.5529410.6666670.468750.562500.5251890.6590910.5862070.6153850.4611533.417183higher
1004J. Polák34Czech Republic180.341. FC Nürnberg686368.030473.0197656864.017356167692200000.5714290.4615380.7857140.7636360.6153850.6210530.6750.5294120.5833330.531250.531250.6775820.7045450.5862070.6153850.2756893.455120higher
1170Bolaño34Spain182.88Real Oviedo626175.030567.0199666766.018065967737150000.5714290.5384620.6428570.7272730.7230770.6263160.5250.5529410.6111110.500000.593750.7670030.6590910.5862070.7179490.8959903.336156higher
1414V. Elm34Sweden190.50Kalmar FF666381.031166.0191686459.017436166777310000.5714290.7692310.7380950.7636360.8153850.6578950.5000.4588240.6666670.406250.375000.6876570.7045450.5517240.8205130.9160403.637629higher
1465A. Raineau34France177.80La Berrichonne de Châteauroux666280.030364.0200686567.017656365715230000.5714290.3846150.7380950.7454550.8000000.6157890.4500.5647060.6666670.437500.625000.7153650.7500000.5172410.6666670.6553883.400216higher
1653N. Topor-Stanley35Australia190.50Newcastle Jets586082.024560.0178616057.015075360843150000.7142860.7692310.5476190.7090910.8307690.3105260.3500.3058820.4722220.281250.312500.3904280.5227270.3448281.0000000.3947373.437479higher
1664L. Broxham32Australia170.18Melbourne Victory615993.030361.0194616667.017485563847880000.2857140.1538460.6190480.6909091.0000000.6157890.3750.4941180.4722220.468750.625000.6939550.5681820.4482761.0000000.9874693.684957higher
1782B. Sigmund34New Zealand187.96Wellington Phoenix585592.023460.0183626160.014164461821300000.5714290.6923080.5476190.6181820.9846150.2526320.3500.3647060.5000000.312500.406250.2758190.3181820.3793100.9487180.1629073.449134higher
1984A. Collin34France187.96Philadelphia Union596382.028764.0181616060.015605262756000000.5714290.6923080.5714290.7636360.8307690.5315790.4500.3411760.4722220.281250.406250.4571790.5000000.4137930.7692310.7518803.385065higher
2298A. Meijers32Netherlands175.26Sparta Rotterdam656779.032564.0193636565.019156764725500000.2857140.3076920.7142860.8363640.7846150.7315790.4500.4823530.5277780.437500.562500.9042820.8409090.4827590.6923080.6892233.477572higher
2467Lombán33Spain185.42Málaga CF636971.029068.0197686564.016965967697800000.4285710.6153850.6666670.8727270.6615380.5473680.5500.5294120.6666670.437500.531250.6284630.6590910.5862070.6153850.9774443.366317higher
2531R. Austin34Jamaica182.88Esbjerg fB646280.030060.0186656457.016855963807310000.5714290.5384620.6904760.7454550.8000000.6000000.3500.4000000.5833330.406250.312500.6146100.6590910.4482760.8974360.9160403.483367higher
2711M. Inoha34Japan177.80Yokohama FC616592.027166.0184606361.016335562733830000.5714290.3846150.6190480.8000000.9846150.4473680.5000.3764710.4444440.375000.437500.5491180.5681820.4137930.7179490.4799503.621612higher
2881H. Mulder33Netherlands180.34RKC Waalwijk666373.031368.0187606463.017426263757350000.4285710.4615380.7380950.7636360.6923080.6684210.5500.4117650.4444440.406250.500000.6863980.7272730.4482760.7692310.9210533.513270higher
3084T. Makino33Japan182.88Urawa Red Diamonds666278.028961.0193616567.017276064767880000.4285710.5384620.7380950.7454550.7692310.5421050.3750.4823530.4722220.437500.625000.6675060.6818180.4827590.7948720.9874693.422652higher
3374Carlos Ruiz36Spain182.88CD Tenerife676183.028663.0187646360.017525764773900000.8571430.5384620.7619050.7272730.8461540.5263160.4250.4117650.5555560.375000.406250.6989920.6136360.4827590.8205130.4887223.580844higher
4267D. Kempe34Germany187.96SV Wehen Wiesbaden625876.029665.0196616669.017305864776740000.5714290.6923080.6428570.6727270.7384620.5789470.4750.5176470.4722220.468750.687500.6712850.6363640.4827590.8205130.8446123.349559higher
7770M. Čovilo34Bosnia Herzegovina193.04FC Lugano636286.029469.0192586965.016365865765150000.5714290.8461540.6666670.7454550.8923080.5684210.5750.4705880.3888890.562500.562500.5528970.6363640.5172410.7948720.6453633.674301higher
15408C. Caraza34Peru175.26Sport Huancayo687272.031564.0192656562.018526962707250000.5714290.3076920.7857140.9272730.6769230.6789470.4500.4705880.5833330.437500.468750.8249370.8863640.4137930.6410260.9085213.480936higher
15426V. Balta34Peru180.34Sport Huancayo605785.028067.0184616360.016525263756300000.5714290.4615380.5952380.6545450.8769230.4947370.5250.3764710.4722220.375000.406250.5730480.5000000.4482760.7692310.7894743.420937higher
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 437 - } - ] - }, - { - "cell_type": "code", - "source": [ - "import sklearn\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "from sklearn.preprocessing import StandardScaler" - ], - "metadata": { - "id": "xfuDQZ1xofGy" - }, - "execution_count": 438, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "#scaler = StandardScaler()\n", - "#scaler.fit_transform(defenders_filtered_stats2)" - ], - "metadata": { - "id": "LqoL-m8GpC4e" - }, - "execution_count": 439, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import seaborn as sns\n", - "#sns.heatmap(defenders_filtered_stats2.corr(),annot=True,xticklabel=defenders_filtered_stats2.corr().columns,yticklabel=defenders_filtered_stats2.corr().columns)\n", - "#plt.plot(defenders_filtered_stats['total stats'],defenders_filtered_stats['total_price'],'go')\n", - "#plt.show()" - ], - "metadata": { - "id": "n01-rxomqUZe" - }, - "execution_count": 440, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "'''fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(10, 10))\n", - "\n", - "# set the x-axis to a common column for all subplots\n", - "x_column = 'total_price'\n", - "\n", - "# iterate over the axes and plot each column against the common column\n", - "for i, ax in enumerate(axes.flatten()):\n", - " if i < len(defenders_filtered_stats2.columns) - 1:\n", - " y_column = defenders_filtered_stats2.columns[i+1]\n", - " ax.scatter(defenders_filtered_stats2[x_column], defenders_filtered_stats2[y_column])\n", - " ax.set_xlabel(x_column)\n", - " ax.set_ylabel(y_column)\n", - "\n", - "# adjust the layout of the subplots and show the figure\n", - "fig.tight_layout()\n", - "plt.show()\n", - "'''" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 71 - }, - "id": "m-GevxVHtF3a", - "outputId": "c01647a7-c5b9-494a-a92c-3e9939caa4d5" - }, - "execution_count": 441, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\"fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(10, 10))\\n\\n# set the x-axis to a common column for all subplots\\nx_column = 'total_price'\\n\\n# iterate over the axes and plot each column against the common column\\nfor i, ax in enumerate(axes.flatten()):\\n if i < len(defenders_filtered_stats2.columns) - 1:\\n y_column = defenders_filtered_stats2.columns[i+1]\\n ax.scatter(defenders_filtered_stats2[x_column], defenders_filtered_stats2[y_column])\\n ax.set_xlabel(x_column)\\n ax.set_ylabel(y_column)\\n\\n# adjust the layout of the subplots and show the figure\\nfig.tight_layout()\\nplt.show()\\n\"" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 441 - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(defenders_filtered_stats['quality_value'],defenders_filtered_stats['def'],c=defenders_filtered_stats['total_price'])\n", - "plt.colorbar()\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 430 - }, - "id": "0wTUgX50uWT0", - "outputId": "5aff5b61-3116-486b-b459-e3ff4632f017" - }, - "execution_count": 442, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "defenders_filtered_stats[defenders_filtered_stats['def']>72]" - ], - "metadata": { - "id": "LZeNZYomw3T3", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 838 - }, - "outputId": "a34d2fb3-8f5e-49e9-c30c-5e1c17ae9432" - }, - "execution_count": 443, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name age nationality height_cm club \\\n", - "10 P. Neville 35 England 180.34 Everton \n", - "24 A. Nesta 37 Italy 187.96 Montreal Impact \n", - "92 G. Heinze 35 Argentina 177.80 Newell's Old Boys \n", - "151 W. Samuel 37 Argentina 182.88 FC Basel 1893 \n", - "156 O. Mellberg 35 Sweden 187.96 FC København \n", - "161 S. Distin 37 France 193.04 Bournemouth \n", - "210 Gilberto Silva 36 Brazil 182.88 Atlético Mineiro \n", - "235 S. Cherundolo 34 United States 172.72 Hannover 96 \n", - "257 M. Ambrosini 36 Italy 182.88 Fiorentina \n", - "309 Lúcio 35 Brazil 187.96 Palmeiras \n", - "327 S. Riether 35 Germany 175.26 FC Schalke 04 \n", - "354 K. Touré 35 Ivory Coast 177.80 Celtic \n", - "363 A. Tymoshchuk 35 Ukraine 180.34 Zenit St. Petersburg \n", - "424 M. Gobbi 37 Italy 182.88 Parma \n", - "496 D. Verón 36 Paraguay 180.34 Paraguay \n", - "680 L. Perea 35 Colombia 180.34 Cruz Azul \n", - "726 G. Sardo 37 Italy 190.50 Chievo Verona \n", - "\n", - " short passing long passing jumping mentality interceptions \\\n", - "10 74 72 71.0 335 83.0 \n", - "24 65 62 74.0 277 76.0 \n", - "92 60 66 76.0 305 76.0 \n", - "151 66 67 75.0 293 86.0 \n", - "156 63 67 66.0 302 78.0 \n", - "161 66 64 63.0 304 75.0 \n", - "210 68 63 51.0 330 79.0 \n", - "235 75 71 81.0 324 73.0 \n", - "257 76 76 92.0 376 82.0 \n", - "309 65 65 80.0 340 75.0 \n", - "327 74 65 71.0 310 72.0 \n", - "354 70 65 72.0 315 72.0 \n", - "363 72 70 76.0 359 80.0 \n", - "424 71 71 67.0 313 75.0 \n", - "496 63 64 80.0 306 72.0 \n", - "680 59 52 90.0 284 74.0 \n", - "726 60 60 78.0 303 71.0 \n", - "\n", - " defending marking standing tackle sliding tackle total stats pas \\\n", - "10 224 77 75 72.0 1868 67 \n", - "24 221 74 77 70.0 1548 54 \n", - "92 227 76 76 75.0 1784 63 \n", - "151 220 74 74 72.0 1560 54 \n", - "156 215 75 77 63.0 1634 55 \n", - "161 237 80 79 78.0 1679 56 \n", - "210 229 76 79 74.0 1695 61 \n", - "235 224 74 73 77.0 1918 69 \n", - "257 226 70 78 78.0 1957 71 \n", - "309 227 75 79 73.0 1807 58 \n", - "327 220 78 73 69.0 1815 66 \n", - "354 223 72 75 76.0 1816 60 \n", - "363 237 77 81 79.0 1965 68 \n", - "424 222 65 78 79.0 1867 68 \n", - "496 215 72 74 69.0 1771 56 \n", - "680 227 75 76 76.0 1724 51 \n", - "726 226 75 78 73.0 1807 60 \n", - "\n", - " def phy total_price age_n height_cm_n short passing_n \\\n", - "10 76 72 120000 0.714286 0.461538 0.928571 \n", - "24 75 62 0 1.000000 0.692308 0.714286 \n", - "92 76 68 300000 0.714286 0.384615 0.595238 \n", - "151 76 66 0 1.000000 0.538462 0.738095 \n", - "156 76 75 300000 0.714286 0.692308 0.666667 \n", - "161 79 78 0 1.000000 0.846154 0.738095 \n", - "210 77 69 0 0.857143 0.538462 0.785714 \n", - "235 73 68 400000 0.571429 0.230769 0.952381 \n", - "257 77 73 0 0.857143 0.538462 0.976190 \n", - "309 77 75 450000 0.714286 0.692308 0.714286 \n", - "327 73 61 621000 0.714286 0.307692 0.928571 \n", - "354 74 79 700000 0.714286 0.384615 0.833333 \n", - "363 78 72 375000 0.714286 0.461538 0.880952 \n", - "424 73 65 596000 1.000000 0.538462 0.857143 \n", - "496 73 73 0 0.857143 0.461538 0.666667 \n", - "680 75 80 550000 0.714286 0.461538 0.571429 \n", - "726 75 75 0 1.000000 0.769231 0.595238 \n", - "\n", - " long passing_n jumping_n mentality_n interceptions_n defending_n \\\n", - "10 0.927273 0.661538 0.784211 0.925 0.847059 \n", - "24 0.745455 0.707692 0.478947 0.750 0.811765 \n", - "92 0.818182 0.738462 0.626316 0.750 0.882353 \n", - "151 0.836364 0.723077 0.563158 1.000 0.800000 \n", - "156 0.836364 0.584615 0.610526 0.800 0.741176 \n", - "161 0.781818 0.538462 0.621053 0.725 1.000000 \n", - "210 0.763636 0.353846 0.757895 0.825 0.905882 \n", - "235 0.909091 0.815385 0.726316 0.675 0.847059 \n", - "257 1.000000 0.984615 1.000000 0.900 0.870588 \n", - "309 0.800000 0.800000 0.810526 0.725 0.882353 \n", - "327 0.800000 0.661538 0.652632 0.650 0.800000 \n", - "354 0.800000 0.676923 0.678947 0.650 0.835294 \n", - "363 0.890909 0.738462 0.910526 0.850 1.000000 \n", - "424 0.909091 0.600000 0.668421 0.725 0.823529 \n", - "496 0.781818 0.800000 0.631579 0.650 0.741176 \n", - "680 0.563636 0.953846 0.515789 0.700 0.882353 \n", - "726 0.709091 0.769231 0.615789 0.625 0.870588 \n", - "\n", - " marking_n standing tackle_n sliding tackle_n total stats_n pas_n \\\n", - "10 0.916667 0.75000 0.78125 0.845088 0.840909 \n", - "24 0.833333 0.81250 0.71875 0.442065 0.545455 \n", - "92 0.888889 0.78125 0.87500 0.739295 0.750000 \n", - "151 0.833333 0.71875 0.78125 0.457179 0.545455 \n", - "156 0.861111 0.81250 0.50000 0.550378 0.568182 \n", - "161 1.000000 0.87500 0.96875 0.607053 0.590909 \n", - "210 0.888889 0.87500 0.84375 0.627204 0.704545 \n", - "235 0.833333 0.68750 0.93750 0.908060 0.886364 \n", - "257 0.722222 0.84375 0.96875 0.957179 0.931818 \n", - "309 0.861111 0.87500 0.81250 0.768262 0.636364 \n", - "327 0.944444 0.68750 0.68750 0.778338 0.818182 \n", - "354 0.777778 0.75000 0.90625 0.779597 0.681818 \n", - "363 0.916667 0.93750 1.00000 0.967254 0.863636 \n", - "424 0.583333 0.84375 1.00000 0.843829 0.863636 \n", - "496 0.777778 0.71875 0.68750 0.722922 0.590909 \n", - "680 0.861111 0.78125 0.90625 0.663728 0.477273 \n", - "726 0.861111 0.84375 0.81250 0.768262 0.681818 \n", - "\n", - " def_n phy_n total_price_n quality_value rank \n", - "10 0.896552 0.692308 0.150376 4.134690 higher \n", - "24 0.862069 0.435897 0.000000 3.353330 higher \n", - "92 0.896552 0.589744 0.375940 3.491625 higher \n", - "151 0.896552 0.538462 0.000000 3.835997 higher \n", - "156 0.896552 0.769231 0.375940 3.656876 higher \n", - "161 1.000000 0.846154 0.000000 3.629529 higher \n", - "210 0.931034 0.615385 0.000000 3.343581 higher \n", - "235 0.793103 0.589744 0.501253 3.941600 higher \n", - "257 0.931034 0.717949 0.000000 4.578755 higher \n", - "309 0.931034 0.769231 0.563910 3.808516 higher \n", - "327 0.793103 0.410256 0.778195 3.450366 higher \n", - "354 0.827586 0.871795 0.877193 3.832051 higher \n", - "363 0.965517 0.692308 0.469925 4.052631 higher \n", - "424 0.793103 0.512821 0.746867 3.604054 higher \n", - "496 0.793103 0.717949 0.000000 3.616434 higher \n", - "680 0.862069 0.897436 0.689223 3.686347 higher \n", - "726 0.862069 0.769231 0.000000 3.467791 higher " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_nquality_valuerank
10P. Neville35England180.34Everton747271.033583.0224777572.018686776721200000.7142860.4615380.9285710.9272730.6615380.7842110.9250.8470590.9166670.750000.781250.8450880.8409090.8965520.6923080.1503764.134690higher
24A. Nesta37Italy187.96Montreal Impact656274.027776.0221747770.0154854756201.0000000.6923080.7142860.7454550.7076920.4789470.7500.8117650.8333330.812500.718750.4420650.5454550.8620690.4358970.0000003.353330higher
92G. Heinze35Argentina177.80Newell's Old Boys606676.030576.0227767675.017846376683000000.7142860.3846150.5952380.8181820.7384620.6263160.7500.8823530.8888890.781250.875000.7392950.7500000.8965520.5897440.3759403.491625higher
151W. Samuel37Argentina182.88FC Basel 1893666775.029386.0220747472.0156054766601.0000000.5384620.7380950.8363640.7230770.5631581.0000.8000000.8333330.718750.781250.4571790.5454550.8965520.5384620.0000003.835997higher
156O. Mellberg35Sweden187.96FC København636766.030278.0215757763.016345576753000000.7142860.6923080.6666670.8363640.5846150.6105260.8000.7411760.8611110.812500.500000.5503780.5681820.8965520.7692310.3759403.656876higher
161S. Distin37France193.04Bournemouth666463.030475.0237807978.0167956797801.0000000.8461540.7380950.7818180.5384620.6210530.7251.0000001.0000000.875000.968750.6070530.5909091.0000000.8461540.0000003.629529higher
210Gilberto Silva36Brazil182.88Atlético Mineiro686351.033079.0229767974.0169561776900.8571430.5384620.7857140.7636360.3538460.7578950.8250.9058820.8888890.875000.843750.6272040.7045450.9310340.6153850.0000003.343581higher
235S. Cherundolo34United States172.72Hannover 96757181.032473.0224747377.019186973684000000.5714290.2307690.9523810.9090910.8153850.7263160.6750.8470590.8333330.687500.937500.9080600.8863640.7931030.5897440.5012533.941600higher
257M. Ambrosini36Italy182.88Fiorentina767692.037682.0226707878.0195771777300.8571430.5384620.9761901.0000000.9846151.0000000.9000.8705880.7222220.843750.968750.9571790.9318180.9310340.7179490.0000004.578755higher
309Lúcio35Brazil187.96Palmeiras656580.034075.0227757973.018075877754500000.7142860.6923080.7142860.8000000.8000000.8105260.7250.8823530.8611110.875000.812500.7682620.6363640.9310340.7692310.5639103.808516higher
327S. Riether35Germany175.26FC Schalke 04746571.031072.0220787369.018156673616210000.7142860.3076920.9285710.8000000.6615380.6526320.6500.8000000.9444440.687500.687500.7783380.8181820.7931030.4102560.7781953.450366higher
354K. Touré35Ivory Coast177.80Celtic706572.031572.0223727576.018166074797000000.7142860.3846150.8333330.8000000.6769230.6789470.6500.8352940.7777780.750000.906250.7795970.6818180.8275860.8717950.8771933.832051higher
363A. Tymoshchuk35Ukraine180.34Zenit St. Petersburg727076.035980.0237778179.019656878723750000.7142860.4615380.8809520.8909090.7384620.9105260.8501.0000000.9166670.937501.000000.9672540.8636360.9655170.6923080.4699254.052631higher
424M. Gobbi37Italy182.88Parma717167.031375.0222657879.018676873655960001.0000000.5384620.8571430.9090910.6000000.6684210.7250.8235290.5833330.843751.000000.8438290.8636360.7931030.5128210.7468673.604054higher
496D. Verón36Paraguay180.34Paraguay636480.030672.0215727469.0177156737300.8571430.4615380.6666670.7818180.8000000.6315790.6500.7411760.7777780.718750.687500.7229220.5909090.7931030.7179490.0000003.616434higher
680L. Perea35Colombia180.34Cruz Azul595290.028474.0227757676.017245175805500000.7142860.4615380.5714290.5636360.9538460.5157890.7000.8823530.8611110.781250.906250.6637280.4772730.8620690.8974360.6892233.686347higher
726G. Sardo37Italy190.50Chievo Verona606078.030371.0226757873.0180760757501.0000000.7692310.5952380.7090910.7692310.6157890.6250.8705880.8611110.843750.812500.7682620.6818180.8620690.7692310.0000003.467791higher
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 443 - } - ] - }, - { - "cell_type": "code", - "source": [ - "defenders_filtered_stats['ratio_n'] = defenders_filtered_stats['quality_value']/defenders_filtered_stats['total_price_n']\n", - "defenders_filtered_stats.sort_values(by='quality_value',ascending=False).shape\n", - "#list(defenders_filtered_stats['name'][:10])" - ], - "metadata": { - "id": "E_pP_xJjb9S2", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d0ca0658-bff9-4321-ea10-5e893c495815" - }, - "execution_count": 444, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " defenders_filtered_stats['ratio_n'] = defenders_filtered_stats['quality_value']/defenders_filtered_stats['total_price_n']\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(52, 38)" - ] - }, - "metadata": {}, - "execution_count": 444 - } - ] - }, - { - "cell_type": "code", - "source": [ - "defenders_filtered_stats = defenders_filtered_stats[defenders_filtered_stats['defending_n']>0.6]\n", - "best_defenders = defenders_filtered_stats.sort_values(by='quality_value',ascending=False)\n", - "best_defenders.head(10)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 514 - }, - "id": "UuWlAT_lL03x", - "outputId": "2cfe23fc-d6d8-4099-b016-050e6249d486" - }, - "execution_count": 451, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name age nationality height_cm club \\\n", - "257 M. Ambrosini 36 Italy 182.88 Fiorentina \n", - "404 P. García 36 Uruguay 185.42 PAOK \n", - "10 P. Neville 35 England 180.34 Everton \n", - "363 A. Tymoshchuk 35 Ukraine 180.34 Zenit St. Petersburg \n", - "235 S. Cherundolo 34 United States 172.72 Hannover 96 \n", - "151 W. Samuel 37 Argentina 182.88 FC Basel 1893 \n", - "354 K. Touré 35 Ivory Coast 177.80 Celtic \n", - "803 D. Pérez 34 Uruguay 177.80 Bologna \n", - "309 Lúcio 35 Brazil 187.96 Palmeiras \n", - "680 L. Perea 35 Colombia 180.34 Cruz Azul \n", - "\n", - " short passing long passing jumping mentality interceptions \\\n", - "257 76 76 92.0 376 82.0 \n", - "404 77 76 65.0 345 75.0 \n", - "10 74 72 71.0 335 83.0 \n", - "363 72 70 76.0 359 80.0 \n", - "235 75 71 81.0 324 73.0 \n", - "151 66 67 75.0 293 86.0 \n", - "354 70 65 72.0 315 72.0 \n", - "803 65 66 74.0 346 72.0 \n", - "309 65 65 80.0 340 75.0 \n", - "680 59 52 90.0 284 74.0 \n", - "\n", - " defending marking standing tackle sliding tackle total stats pas \\\n", - "257 226 70 78 78.0 1957 71 \n", - "404 214 70 73 71.0 1900 74 \n", - "10 224 77 75 72.0 1868 67 \n", - "363 237 77 81 79.0 1965 68 \n", - "235 224 74 73 77.0 1918 69 \n", - "151 220 74 74 72.0 1560 54 \n", - "354 223 72 75 76.0 1816 60 \n", - "803 208 65 72 71.0 1845 62 \n", - "309 227 75 79 73.0 1807 58 \n", - "680 227 75 76 76.0 1724 51 \n", - "\n", - " def phy total_price age_n height_cm_n short passing_n \\\n", - "257 77 73 0 0.857143 0.538462 0.976190 \n", - "404 72 78 0 0.857143 0.615385 1.000000 \n", - "10 76 72 120000 0.714286 0.461538 0.928571 \n", - "363 78 72 375000 0.714286 0.461538 0.880952 \n", - "235 73 68 400000 0.571429 0.230769 0.952381 \n", - "151 76 66 0 1.000000 0.538462 0.738095 \n", - "354 74 79 700000 0.714286 0.384615 0.833333 \n", - "803 70 81 400000 0.571429 0.384615 0.714286 \n", - "309 77 75 450000 0.714286 0.692308 0.714286 \n", - "680 75 80 550000 0.714286 0.461538 0.571429 \n", - "\n", - " long passing_n jumping_n mentality_n interceptions_n defending_n \\\n", - "257 1.000000 0.984615 1.000000 0.900 0.870588 \n", - "404 1.000000 0.569231 0.836842 0.725 0.729412 \n", - "10 0.927273 0.661538 0.784211 0.925 0.847059 \n", - "363 0.890909 0.738462 0.910526 0.850 1.000000 \n", - "235 0.909091 0.815385 0.726316 0.675 0.847059 \n", - "151 0.836364 0.723077 0.563158 1.000 0.800000 \n", - "354 0.800000 0.676923 0.678947 0.650 0.835294 \n", - "803 0.818182 0.707692 0.842105 0.650 0.658824 \n", - "309 0.800000 0.800000 0.810526 0.725 0.882353 \n", - "680 0.563636 0.953846 0.515789 0.700 0.882353 \n", - "\n", - " marking_n standing tackle_n sliding tackle_n total stats_n pas_n \\\n", - "257 0.722222 0.84375 0.96875 0.957179 0.931818 \n", - "404 0.722222 0.68750 0.75000 0.885390 1.000000 \n", - "10 0.916667 0.75000 0.78125 0.845088 0.840909 \n", - "363 0.916667 0.93750 1.00000 0.967254 0.863636 \n", - "235 0.833333 0.68750 0.93750 0.908060 0.886364 \n", - "151 0.833333 0.71875 0.78125 0.457179 0.545455 \n", - "354 0.777778 0.75000 0.90625 0.779597 0.681818 \n", - "803 0.583333 0.65625 0.75000 0.816121 0.727273 \n", - "309 0.861111 0.87500 0.81250 0.768262 0.636364 \n", - "680 0.861111 0.78125 0.90625 0.663728 0.477273 \n", - "\n", - " def_n phy_n total_price_n quality_value rank ratio_n \n", - "257 0.931034 0.717949 0.000000 4.578755 higher inf \n", - "404 0.758621 0.846154 0.000000 4.140385 higher inf \n", - "10 0.896552 0.692308 0.150376 4.134690 higher 27.495691 \n", - "363 0.965517 0.692308 0.469925 4.052631 higher 8.623998 \n", - "235 0.793103 0.589744 0.501253 3.941600 higher 7.863492 \n", - "151 0.896552 0.538462 0.000000 3.835997 higher inf \n", - "354 0.827586 0.871795 0.877193 3.832051 higher 4.368538 \n", - "803 0.689655 0.923077 0.501253 3.813237 higher 7.607407 \n", - "309 0.931034 0.769231 0.563910 3.808516 higher 6.753769 \n", - "680 0.862069 0.897436 0.689223 3.686347 higher 5.348554 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nameagenationalityheight_cmclubshort passinglong passingjumpingmentalityinterceptionsdefendingmarkingstanding tacklesliding tackletotal statspasdefphytotal_priceage_nheight_cm_nshort passing_nlong passing_njumping_nmentality_ninterceptions_ndefending_nmarking_nstanding tackle_nsliding tackle_ntotal stats_npas_ndef_nphy_ntotal_price_nquality_valuerankratio_n
257M. Ambrosini36Italy182.88Fiorentina767692.037682.0226707878.0195771777300.8571430.5384620.9761901.0000000.9846151.0000000.9000.8705880.7222220.843750.968750.9571790.9318180.9310340.7179490.0000004.578755higherinf
404P. García36Uruguay185.42PAOK777665.034575.0214707371.0190074727800.8571430.6153851.0000001.0000000.5692310.8368420.7250.7294120.7222220.687500.750000.8853901.0000000.7586210.8461540.0000004.140385higherinf
10P. Neville35England180.34Everton747271.033583.0224777572.018686776721200000.7142860.4615380.9285710.9272730.6615380.7842110.9250.8470590.9166670.750000.781250.8450880.8409090.8965520.6923080.1503764.134690higher27.495691
363A. Tymoshchuk35Ukraine180.34Zenit St. Petersburg727076.035980.0237778179.019656878723750000.7142860.4615380.8809520.8909090.7384620.9105260.8501.0000000.9166670.937501.000000.9672540.8636360.9655170.6923080.4699254.052631higher8.623998
235S. Cherundolo34United States172.72Hannover 96757181.032473.0224747377.019186973684000000.5714290.2307690.9523810.9090910.8153850.7263160.6750.8470590.8333330.687500.937500.9080600.8863640.7931030.5897440.5012533.941600higher7.863492
151W. Samuel37Argentina182.88FC Basel 1893666775.029386.0220747472.0156054766601.0000000.5384620.7380950.8363640.7230770.5631581.0000.8000000.8333330.718750.781250.4571790.5454550.8965520.5384620.0000003.835997higherinf
354K. Touré35Ivory Coast177.80Celtic706572.031572.0223727576.018166074797000000.7142860.3846150.8333330.8000000.6769230.6789470.6500.8352940.7777780.750000.906250.7795970.6818180.8275860.8717950.8771933.832051higher4.368538
803D. Pérez34Uruguay177.80Bologna656674.034672.0208657271.018456270814000000.5714290.3846150.7142860.8181820.7076920.8421050.6500.6588240.5833330.656250.750000.8161210.7272730.6896550.9230770.5012533.813237higher7.607407
309Lúcio35Brazil187.96Palmeiras656580.034075.0227757973.018075877754500000.7142860.6923080.7142860.8000000.8000000.8105260.7250.8823530.8611110.875000.812500.7682620.6363640.9310340.7692310.5639103.808516higher6.753769
680L. Perea35Colombia180.34Cruz Azul595290.028474.0227757676.017245175805500000.7142860.4615380.5714290.5636360.9538460.5157890.7000.8823530.8611110.781250.906250.6637280.4772730.8620690.8974360.6892233.686347higher5.348554
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 451 - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(defenders_filtered_stats['defending'],defenders_filtered_stats['quality_value'],c=defenders_filtered_stats['total_price'])\n", - "plt.colorbar()\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 435 - }, - "id": "fvdbwrE7HIqL", - "outputId": "53672dee-9908-4961-bea9-618bf36d04b9" - }, - "execution_count": 452, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "#list(defenders_filtered_stats['name'][:10])\n", - "list_best = list(best_defenders['name'])[:5]\n", - "list_best" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xNEUgpxmIga4", - "outputId": "d616d510-dd70-4fbf-f8f0-30d355c1e5ad" - }, - "execution_count": 453, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['M. Ambrosini', 'P. García', 'P. Neville', 'A. Tymoshchuk', 'S. Cherundolo']" - ] - }, - "metadata": {}, - "execution_count": 453 - } - ] - }, - { - "cell_type": "code", - "source": [ - "top_defenders = defenders[(defenders['name'].isin(list_best))]\n", - "top_defenders" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 496 - }, - "id": "-Jn_7gfhP-Co", - "outputId": "ad8cc927-47aa-43cd-a031-286405aef196" - }, - "execution_count": 454, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id name age ova nationality club \\\n", - "10 249 P. Neville 35 74 England Everton \n", - "235 51003 S. Cherundolo 34 73 United States Hannover 96 \n", - "257 53055 M. Ambrosini 36 74 Italy Fiorentina \n", - "363 121622 A. Tymoshchuk 35 75 Ukraine Zenit St. Petersburg \n", - "404 135616 P. García 36 73 Uruguay PAOK \n", - "\n", - " bov bp position player photo \\\n", - "10 75 CB CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", - "235 73 RB RB https://cdn.sofifa.com/players/051/003/14_120.png \n", - "257 78 CB NaN https://cdn.sofifa.com/players/053/055/14_120.png \n", - "363 76 CB NaN https://cdn.sofifa.com/players/121/622/15_120.png \n", - "404 75 CB NaN https://cdn.sofifa.com/players/135/616/14_120.png \n", - "\n", - " club logo \\\n", - "10 https://cdn.sofifa.com/teams/7/light_60.png \n", - "235 https://cdn.sofifa.com/teams/485/light_60.png \n", - "257 https://cdn.sofifa.com/teams/110374/light_60.png \n", - "363 https://cdn.sofifa.com/teams/100769/light_60.png \n", - "404 https://cdn.sofifa.com/teams/393/light_60.png \n", - "\n", - " flag photo pot \\\n", - "10 https://cdn.sofifa.com/flags/gb-eng.png 74 \n", - "235 https://cdn.sofifa.com/flags/us.png 73 \n", - "257 https://cdn.sofifa.com/flags/it.png 74 \n", - "363 https://cdn.sofifa.com/flags/ua.png 75 \n", - "404 https://cdn.sofifa.com/flags/uy.png 73 \n", - "\n", - " team & contract height weight foot growth \\\n", - "10 Everton 2005 ~ 2013 5'11\" 168lbs Right 0 \n", - "235 Hannover 96 1999 ~ 2014 5'8\" 152lbs Right 0 \n", - "257 Fiorentina 2013 ~ 2014 6'0\" 159lbs Right 0 \n", - "363 Zenit St. Petersburg 2013 ~ 2015 5'11\" 154lbs Right 0 \n", - "404 PAOK 2014 ~ 2014 6'1\" 161lbs Left 0 \n", - "\n", - " joined loan date end value wage release clause contract \\\n", - "10 Aug 1, 2005 NaN 120K €7K 0 2005 ~ 2013 \n", - "235 Jan 1, 1999 NaN 400K €25K 0 1999 ~ 2014 \n", - "257 Jul 4, 2013 NaN 0 €35K 0 2013 ~ 2014 \n", - "363 Jul 1, 2013 NaN 375K €45K 0 2013 ~ 2015 \n", - "404 Jan 12, 2014 NaN 0 €20K 0 2014 ~ 2014 \n", - "\n", - " attacking crossing finishing heading accuracy short passing volleys \\\n", - "10 315 73 36 69 74 63.0 \n", - "235 291 70 36 65 75 45.0 \n", - "257 342 66 56 84 76 60.0 \n", - "363 304 65 38 70 72 59.0 \n", - "404 302 74 53 72 77 26.0 \n", - "\n", - " skill dribbling curve fk accuracy long passing ball control \\\n", - "10 283 53 45.0 41 72 72 \n", - "235 347 68 67.0 68 71 73 \n", - "257 314 62 56.0 48 76 72 \n", - "363 325 58 56.0 73 70 68 \n", - "404 364 66 75.0 77 76 70 \n", - "\n", - " movement acceleration sprint speed agility reactions balance power \\\n", - "10 321 52 51 65.0 83 70.0 349 \n", - "235 353 67 69 68.0 74 75.0 333 \n", - "257 285 50 50 60.0 70 55.0 360 \n", - "363 318 44 61 66.0 79 68.0 372 \n", - "404 257 38 40 34.0 70 75.0 361 \n", - "\n", - " shot power jumping stamina strength long shots mentality \\\n", - "10 77 71.0 61 76 64 335 \n", - "235 65 81.0 71 64 52 324 \n", - "257 72 92.0 55 75 66 376 \n", - "363 84 76.0 70 69 73 359 \n", - "404 76 65.0 53 85 82 345 \n", - "\n", - " aggression interceptions positioning vision penalties composure \\\n", - "10 78 83.0 48.0 57.0 69 NaN \n", - "235 72 73.0 57.0 58.0 64 NaN \n", - "257 84 82.0 68.0 74.0 68 NaN \n", - "363 83 80.0 57.0 66.0 73 NaN \n", - "404 94 75.0 47.0 67.0 62 NaN \n", - "\n", - " defending marking standing tackle sliding tackle goalkeeping \\\n", - "10 224 77 75 72.0 41 \n", - "235 224 74 73 77.0 46 \n", - "257 226 70 78 78.0 54 \n", - "363 237 77 81 79.0 50 \n", - "404 214 70 73 71.0 57 \n", - "\n", - " gk diving gk handling gk kicking gk positioning gk reflexes \\\n", - "10 10 7 12 5 7 \n", - "235 11 14 9 6 6 \n", - "257 10 12 15 5 12 \n", - "363 5 15 10 8 12 \n", - "404 15 15 6 12 9 \n", - "\n", - " total stats base stats w/f sm a/w d/w ir pac sho pas \\\n", - "10 1868 381 4 ★ 2★ Medium High 2 ★ 51 53 67 \n", - "235 1918 396 3 ★ 2★ Medium Medium 2 ★ 68 48 69 \n", - "257 1957 399 3 ★ 2★ Low High 2 ★ 50 63 71 \n", - "363 1965 392 2 ★ 2★ Low High 3 ★ 53 58 68 \n", - "404 1900 390 3 ★ 2★ Medium Medium 2 ★ 39 62 74 \n", - "\n", - " dri def phy hits ls st rs lw lf cf rf rw lam \\\n", - "10 62 76 72 7 59+0 59+0 59+0 63+0 61+0 61+0 61+0 63+0 63+0 \n", - "235 70 73 68 5 59+0 59+0 59+0 67+0 62+0 62+0 62+0 67+0 65+0 \n", - "257 65 77 73 3 66+0 66+0 66+0 65+0 67+0 67+0 67+0 65+0 69+0 \n", - "363 63 78 72 3 61+1 61+1 61+1 63+1 64+1 64+1 64+1 63+1 65+1 \n", - "404 65 72 78 3 60+0 60+0 60+0 63+0 64+0 64+0 64+0 63+0 65+0 \n", - "\n", - " cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb \\\n", - "10 63+0 63+0 65+0 68+0 68+0 68+0 65+0 72+0 74+0 74+0 74+0 72+0 \n", - "235 65+0 65+0 69+0 68+0 68+0 68+0 69+0 73+0 71+0 71+0 71+0 73+0 \n", - "257 69+0 69+0 67+0 72+0 72+0 72+0 67+0 70+0 74+0 74+0 74+0 70+0 \n", - "363 65+1 65+1 65+1 70+1 70+1 70+1 65+1 73+1 74+1 74+1 74+1 73+1 \n", - "404 65+0 65+0 64+0 69+0 69+0 69+0 64+0 68+0 73+0 73+0 73+0 68+0 \n", - "\n", - " lb lcb cb rcb rb gk gender unit number market_value \\\n", - "10 73+0 75+-1 75+-1 75+-1 73+0 12+0 Male 1000 120.0 120000 \n", - "235 73+0 72+0 72+0 72+0 73+0 13+0 Male 1000 400.0 400000 \n", - "257 73+0 78+-4 78+-4 78+-4 73+0 14+0 Male 1 0.0 0 \n", - "363 75+0 76+-1 76+-1 76+-1 75+0 14+1 Male 1000 375.0 375000 \n", - "404 70+0 75+-2 75+-2 75+-2 70+0 16+0 Male 1 0.0 0 \n", - "\n", - " unit2 number2 release clause_total total_price \n", - "10 1 0.0 0 120000 \n", - "235 1 0.0 0 400000 \n", - "257 1 0.0 0 0 \n", - "363 1 0.0 0 375000 \n", - "404 1 0.0 0 0 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_price
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000
23551003S. Cherundolo3473United StatesHannover 9673RBRBhttps://cdn.sofifa.com/players/051/003/14_120.pnghttps://cdn.sofifa.com/teams/485/light_60.pnghttps://cdn.sofifa.com/flags/us.png73Hannover 96 1999 ~ 20145'8\"152lbsRight0Jan 1, 1999NaN400K€25K01999 ~ 20142917036657545.03476867.0687173353676968.07475.03336581.07164523247273.057.058.064NaN224747377.046111496619183963 ★2★MediumMedium2 ★684869707368559+059+059+067+062+062+062+067+065+065+065+069+068+068+068+069+073+071+071+071+073+073+072+072+072+073+013+0Male1000400.040000010.00400000
25753055M. Ambrosini3674ItalyFiorentina78CBNaNhttps://cdn.sofifa.com/players/053/055/14_120.pnghttps://cdn.sofifa.com/teams/110374/light_60.pnghttps://cdn.sofifa.com/flags/it.png74Fiorentina 2013 ~ 20146'0\"159lbsRight0Jul 4, 2013NaN0€35K02013 ~ 20143426656847660.03146256.0487672285505060.07055.03607292.05575663768482.068.074.068NaN226707878.05410121551219573993 ★2★LowHigh2 ★506371657773366+066+066+065+067+067+067+065+069+069+069+067+072+072+072+067+070+074+074+074+070+073+078+-478+-478+-473+014+0Male10.0010.000
363121622A. Tymoshchuk3575UkraineZenit St. Petersburg76CBNaNhttps://cdn.sofifa.com/players/121/622/15_120.pnghttps://cdn.sofifa.com/teams/100769/light_60.pnghttps://cdn.sofifa.com/flags/ua.png75Zenit St. Petersburg 2013 ~ 20155'11\"154lbsRight0Jul 1, 2013NaN375K€45K02013 ~ 20153046538707259.03255856.0737068318446166.07968.03728476.07069733598380.057.066.073NaN237778179.0505151081219653922 ★2★LowHigh3 ★535868637872361+161+161+163+164+164+164+163+165+165+165+165+170+170+170+165+173+174+174+174+173+175+076+-176+-176+-175+014+1Male1000375.037500010.00375000
404135616P. García3673UruguayPAOK75CBNaNhttps://cdn.sofifa.com/players/135/616/14_120.pnghttps://cdn.sofifa.com/teams/393/light_60.pnghttps://cdn.sofifa.com/flags/uy.png73PAOK 2014 ~ 20146'1\"161lbsLeft0Jan 12, 2014NaN0€20K02014 ~ 20143027453727726.03646675.0777670257384034.07075.03617665.05385823459475.047.067.062NaN214707371.0571515612919003903 ★2★MediumMedium2 ★396274657278360+060+060+063+064+064+064+063+065+065+065+064+069+069+069+064+068+073+073+073+068+070+075+-275+-275+-270+016+0Male10.0010.000
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 454 - } - ] - }, - { - "cell_type": "code", - "source": [ - "top_defenders = top_defenders[(defenders['nationality'].isin(['England','United States','Italy']))]\n", - "top_defenders" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 383 - }, - "id": "93W3xDY7QopC", - "outputId": "08ff386f-8752-4122-b83c-ad568cd31680" - }, - "execution_count": 455, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - ":1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", - " top_defenders = top_defenders[(defenders['nationality'].isin(['England','United States','Italy']))]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id name age ova nationality club bov bp \\\n", - "10 249 P. Neville 35 74 England Everton 75 CB \n", - "235 51003 S. Cherundolo 34 73 United States Hannover 96 73 RB \n", - "257 53055 M. Ambrosini 36 74 Italy Fiorentina 78 CB \n", - "\n", - " position player photo \\\n", - "10 CDM CM RB https://cdn.sofifa.com/players/000/249/13_120.png \n", - "235 RB https://cdn.sofifa.com/players/051/003/14_120.png \n", - "257 NaN https://cdn.sofifa.com/players/053/055/14_120.png \n", - "\n", - " club logo \\\n", - "10 https://cdn.sofifa.com/teams/7/light_60.png \n", - "235 https://cdn.sofifa.com/teams/485/light_60.png \n", - "257 https://cdn.sofifa.com/teams/110374/light_60.png \n", - "\n", - " flag photo pot team & contract \\\n", - "10 https://cdn.sofifa.com/flags/gb-eng.png 74 Everton 2005 ~ 2013 \n", - "235 https://cdn.sofifa.com/flags/us.png 73 Hannover 96 1999 ~ 2014 \n", - "257 https://cdn.sofifa.com/flags/it.png 74 Fiorentina 2013 ~ 2014 \n", - "\n", - " height weight foot growth joined loan date end value wage \\\n", - "10 5'11\" 168lbs Right 0 Aug 1, 2005 NaN 120K €7K \n", - "235 5'8\" 152lbs Right 0 Jan 1, 1999 NaN 400K €25K \n", - "257 6'0\" 159lbs Right 0 Jul 4, 2013 NaN 0 €35K \n", - "\n", - " release clause contract attacking crossing finishing \\\n", - "10 0 2005 ~ 2013 315 73 36 \n", - "235 0 1999 ~ 2014 291 70 36 \n", - "257 0 2013 ~ 2014 342 66 56 \n", - "\n", - " heading accuracy short passing volleys skill dribbling curve \\\n", - "10 69 74 63.0 283 53 45.0 \n", - "235 65 75 45.0 347 68 67.0 \n", - "257 84 76 60.0 314 62 56.0 \n", - "\n", - " fk accuracy long passing ball control movement acceleration \\\n", - "10 41 72 72 321 52 \n", - "235 68 71 73 353 67 \n", - "257 48 76 72 285 50 \n", - "\n", - " sprint speed agility reactions balance power shot power jumping \\\n", - "10 51 65.0 83 70.0 349 77 71.0 \n", - "235 69 68.0 74 75.0 333 65 81.0 \n", - "257 50 60.0 70 55.0 360 72 92.0 \n", - "\n", - " stamina strength long shots mentality aggression interceptions \\\n", - "10 61 76 64 335 78 83.0 \n", - "235 71 64 52 324 72 73.0 \n", - "257 55 75 66 376 84 82.0 \n", - "\n", - " positioning vision penalties composure defending marking \\\n", - "10 48.0 57.0 69 NaN 224 77 \n", - "235 57.0 58.0 64 NaN 224 74 \n", - "257 68.0 74.0 68 NaN 226 70 \n", - "\n", - " standing tackle sliding tackle goalkeeping gk diving gk handling \\\n", - "10 75 72.0 41 10 7 \n", - "235 73 77.0 46 11 14 \n", - "257 78 78.0 54 10 12 \n", - "\n", - " gk kicking gk positioning gk reflexes total stats base stats w/f \\\n", - "10 12 5 7 1868 381 4 ★ \n", - "235 9 6 6 1918 396 3 ★ \n", - "257 15 5 12 1957 399 3 ★ \n", - "\n", - " sm a/w d/w ir pac sho pas dri def phy hits ls st \\\n", - "10 2★ Medium High 2 ★ 51 53 67 62 76 72 7 59+0 59+0 \n", - "235 2★ Medium Medium 2 ★ 68 48 69 70 73 68 5 59+0 59+0 \n", - "257 2★ Low High 2 ★ 50 63 71 65 77 73 3 66+0 66+0 \n", - "\n", - " rs lw lf cf rf rw lam cam ram lm lcm cm \\\n", - "10 59+0 63+0 61+0 61+0 61+0 63+0 63+0 63+0 63+0 65+0 68+0 68+0 \n", - "235 59+0 67+0 62+0 62+0 62+0 67+0 65+0 65+0 65+0 69+0 68+0 68+0 \n", - "257 66+0 65+0 67+0 67+0 67+0 65+0 69+0 69+0 69+0 67+0 72+0 72+0 \n", - "\n", - " rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb \\\n", - "10 68+0 65+0 72+0 74+0 74+0 74+0 72+0 73+0 75+-1 75+-1 75+-1 \n", - "235 68+0 69+0 73+0 71+0 71+0 71+0 73+0 73+0 72+0 72+0 72+0 \n", - "257 72+0 67+0 70+0 74+0 74+0 74+0 70+0 73+0 78+-4 78+-4 78+-4 \n", - "\n", - " rb gk gender unit number market_value unit2 number2 \\\n", - "10 73+0 12+0 Male 1000 120.0 120000 1 0.0 \n", - "235 73+0 13+0 Male 1000 400.0 400000 1 0.0 \n", - "257 73+0 14+0 Male 1 0.0 0 1 0.0 \n", - "\n", - " release clause_total total_price \n", - "10 0 120000 \n", - "235 0 400000 \n", - "257 0 0 " - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idnameageovanationalityclubbovbppositionplayer photoclub logoflag photopotteam & contractheightweightfootgrowthjoinedloan date endvaluewagerelease clausecontractattackingcrossingfinishingheading accuracyshort passingvolleysskilldribblingcurvefk accuracylong passingball controlmovementaccelerationsprint speedagilityreactionsbalancepowershot powerjumpingstaminastrengthlong shotsmentalityaggressioninterceptionspositioningvisionpenaltiescomposuredefendingmarkingstanding tacklesliding tacklegoalkeepinggk divinggk handlinggk kickinggk positioninggk reflexestotal statsbase statsw/fsma/wd/wirpacshopasdridefphyhitslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrbgkgenderunitnumbermarket_valueunit2number2release clause_totaltotal_price
10249P. Neville3574EnglandEverton75CBCDM CM RBhttps://cdn.sofifa.com/players/000/249/13_120.pnghttps://cdn.sofifa.com/teams/7/light_60.pnghttps://cdn.sofifa.com/flags/gb-eng.png74Everton 2005 ~ 20135'11\"168lbsRight0Aug 1, 2005NaN120K€7K02005 ~ 20133157336697463.02835345.0417272321525165.08370.03497771.06176643357883.048.057.069NaN224777572.041107125718683814 ★2★MediumHigh2 ★515367627672759+059+059+063+061+061+061+063+063+063+063+065+068+068+068+065+072+074+074+074+072+073+075+-175+-175+-173+012+0Male1000120.012000010.00120000
23551003S. Cherundolo3473United StatesHannover 9673RBRBhttps://cdn.sofifa.com/players/051/003/14_120.pnghttps://cdn.sofifa.com/teams/485/light_60.pnghttps://cdn.sofifa.com/flags/us.png73Hannover 96 1999 ~ 20145'8\"152lbsRight0Jan 1, 1999NaN400K€25K01999 ~ 20142917036657545.03476867.0687173353676968.07475.03336581.07164523247273.057.058.064NaN224747377.046111496619183963 ★2★MediumMedium2 ★684869707368559+059+059+067+062+062+062+067+065+065+065+069+068+068+068+069+073+071+071+071+073+073+072+072+072+073+013+0Male1000400.040000010.00400000
25753055M. Ambrosini3674ItalyFiorentina78CBNaNhttps://cdn.sofifa.com/players/053/055/14_120.pnghttps://cdn.sofifa.com/teams/110374/light_60.pnghttps://cdn.sofifa.com/flags/it.png74Fiorentina 2013 ~ 20146'0\"159lbsRight0Jul 4, 2013NaN0€35K02013 ~ 20143426656847660.03146256.0487672285505060.07055.03607292.05575663768482.068.074.068NaN226707878.05410121551219573993 ★2★LowHigh2 ★506371657773366+066+066+065+067+067+067+065+069+069+069+067+072+072+072+067+070+074+074+074+070+073+078+-478+-478+-473+014+0Male10.0010.000
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 455 - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "etIBgo7YYt1X" - }, - "execution_count": 449, - "outputs": [] - } - ] -} \ No newline at end of file From f5151d80c71e9464e9a5d4310a311fa5ed7ea986 Mon Sep 17 00:00:00 2001 From: martaferreiro <125505098+martaferreiro@users.noreply.github.com> Date: Thu, 25 May 2023 16:22:21 +0200 Subject: [PATCH 3/3] MartaFernandez --- your-code/main.ipynb | 1881 ++++++++++++++++++++++++++++++------------ 1 file changed, 1359 insertions(+), 522 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 5759add..a2555bb 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -1,522 +1,1359 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Understanding Descriptive Statistics\n", - "\n", - "Import the necessary libraries here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Libraries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 1\n", - "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n", - "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Plot the results sorted by value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 2\n", - "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", - "\n", - "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", - "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 3\n", - "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", - "#### 1.- Sort the values and plot them. What do you see?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Now, calculate the frequency distribution.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 4\n", - "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n", - "\n", - "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 5\n", - "Now is the turn of `ages_population3.csv`.\n", - "\n", - "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bonus challenge\n", - "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ironhack-3.7", - "language": "python", - "name": "ironhack-3.7" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Descriptive Statistics\n", + "\n", + "Import the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "from scipy import stats\n", + "import random\n", + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 1\n", + "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n", + "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "\n", + "def dice_throw(throws=10, dice_faces = 6):\n", + " dice_list = []\n", + " \n", + " for i in range(throws):\n", + " dice_list.append(random.choice(np.arange(1,(dice_faces +1),1)))\n", + " \n", + " throws_df = pd.DataFrame(dice_list, columns=['Number'])\n", + " return throws_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Number
01
16
22
34
41
52
66
73
84
94
\n", + "
" + ], + "text/plain": [ + " Number\n", + "0 1\n", + "1 6\n", + "2 2\n", + "3 4\n", + "4 1\n", + "5 2\n", + "6 6\n", + "7 3\n", + "8 4\n", + "9 4" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dice = dice_throw()\n", + "dice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Number
01
41
22
52
73
34
84
94
16
66
\n", + "
" + ], + "text/plain": [ + " Number\n", + "0 1\n", + "4 1\n", + "2 2\n", + "5 2\n", + "7 3\n", + "3 4\n", + "8 4\n", + "9 4\n", + "1 6\n", + "6 6" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dice = dice.sort_values(by='Number')\n", + "dice" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "\n", + "plt.plot(dice['Number'],\n", + " dice.index,\n", + " color='purple',\n", + " marker='x')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "dice = dice.sort_index()\n", + "dice\n", + "\n", + "plt.hist(dice, bins=6,\n", + " range=(1,7),\n", + " color= 'purple',\n", + " rwidth = 2)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nyour comments here\\nthe first plot was unorganized and it wasn't clear the meaning of it.\\n\\nThe second plot makes sense because every dice number has a unique bar\\nregarding the quantity of the number of times it showed up.\\n\"" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "the first plot was unorganized and it wasn't clear the meaning of it.\n", + "\n", + "The second plot makes sense because every dice number has a unique bar\n", + "regarding the quantity of the number of times it showed up.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", + "\n", + "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(dice['Number'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.3" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "def mean(data):\n", + " sum_data = sum(data)\n", + " len_data = len(data)\n", + " return sum_data/len_data\n", + "mean(dice['Number'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 2, 6: 2, 2: 2, 4: 3, 3: 1}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "def freq_d (values):\n", + " freq_d = {}\n", + " for dice_n in values:\n", + " if dice_n in freq_d:\n", + " freq_d[dice_n] += 1\n", + " else:\n", + " freq_d[dice_n] = 1\n", + " \n", + " return freq_d\n", + "\n", + "freq_d (dice['Number'])" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 2, 2, 3, 4, 4, 4, 6, 6]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dice_sorted = list(dice['Number'].sort_values())\n", + "dice_sorted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", + "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.5" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "def median(data):\n", + " sorted_data = sorted(data)\n", + " len_data = len(sorted_data)\n", + " midle_index = (len_data) // 2\n", + " if len_data %2 == 0:\n", + " return int(((sorted_data[int(midle_index)]) + (sorted_data[(int(midle_index))-1])))/2\n", + " else:\n", + " return int(sorted_data[int(((len_data-1)/2))])\n", + " \n", + "\n", + "median(dice_sorted)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "dice_list = sorted(list(dice['Number']))" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 3.5, 4)" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def quantiles(value_list):\n", + " value_list = sorted(value_list)\n", + " \n", + " # 2q\n", + " second_q = median(value_list)\n", + "\n", + " if len(value_list) % 2 == 0:\n", + " # 1q\n", + " first_q = median(value_list[:int(len(value_list)/2)])\n", + " # 3q\n", + " third_q = median(value_list[int(len(value_list)/2):])\n", + "\n", + " else:\n", + " # 1q\n", + " first_q = median(value_list[:int(len(value_list)/2 -1)])\n", + " # 3q\n", + " third_q = median(value_list[int(len(value_list)/2-1):])\n", + " \n", + " return first_q, second_q, third_q\n", + "\n", + "quantiles(dice_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", + "#### 1.- Sort the values and plot them. What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
times
value
112
217
314
422
512
623
\n", + "
" + ], + "text/plain": [ + " times\n", + "value \n", + "1 12\n", + "2 17\n", + "3 14\n", + "4 22\n", + "5 12\n", + "6 23" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "\n", + "roll_dice = pd.read_csv('data/roll_the_dice_hundred.csv')\n", + "rolling_sorted = roll_dice.value_counts(['value'])\n", + "roll_dice1 = pd.DataFrame(rolling_sorted)\n", + "roll_dice1 = roll_dice1.sort_values('value',ascending=True)\n", + "roll_dice1 = roll_dice1.rename(columns={0:'times'})\n", + "roll_dice1 = roll_dice1.reset_index().set_index('value')\n", + "roll_dice1.plot(kind='bar')\n", + "plt.show\n", + "\n", + "roll_dice1" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16.666666666666668" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(roll_dice)/6" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n\\nThe values are around the mean of times that we expect. In this case, 16.6\\n'" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "The values are around the mean of times that we expect. In this case, 16.6\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "mean(roll_dice['value'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "freq_d (roll_dice['value'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "\n", + "plt.hist(roll_dice['value'],bins = 6, range=(1,7), rwidth = 0.8)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n\\nWe are expecting a mean of 3.5\\nAnd our mean is 3.74, meaning that we have more values on the right side of the graph.\\n'" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "We are expecting a mean of 3.5\n", + "And our mean is 3.74, meaning that we have more values on the right side of the graph.\n", + "And indeed, we can see in the histogram that we have many values on the 4 and the 6, which skew the data towards the right.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "roll_dice_thousand = pd.read_csv('data/roll_the_dice_thousand.csv')\n", + "roll_dice_thousand\n", + "\n", + "\n", + "plt.hist(roll_dice_thousand['value'],bins = 6, range=(1,7), rwidth = 0.8)\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "Now we have more data and the values are more close to each other, so there is less deviation, and it is less skewed.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 4\n", + "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n", + "\n", + "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. " + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGxCAYAAABIjE2TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAt1UlEQVR4nO3df1RVdb7/8dfhh0cgRJHwcBKJzG5TkDVaGjaJKTTmj8xpKpnKX92cSb1xzTF/3K6nprTlXbecsXSq8aJppDMrdCorxVFRxn5JelObzG7mrzBWiqBihyN8vn/M8nw7ggYI8jnH52OtvWh/9nvv/XkD5/hqn7M5DmOMEQAAgEXCWnsCAAAAZyKgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAF7FFixbJ4XBoy5YtrT2VFpOfn6+5c+fWu83hcMjj8VzQ+QBomIjWngAAtKT8/Hzt2LFDubm5dba9//776ty584WfFIAfRUABYIWqqipFR0df0HP27t37gp4PQMPxEg8QwoqLi9W/f3/FxsYqOjpaGRkZWrVqVZ268vJyjR49WvHx8YqJidGQIUP01VdfBdRs3bpVgwcPVmJiopxOp9xutwYNGqQDBw74a4wxmj9/vq6//npFRUWpQ4cOuvvuu+scKzMzU2lpadq4caMyMjIUHR2tMWPGaNiwYUpJSVFtbW2dOfbq1Us//elP/esvvviibr31ViUmJiomJkbp6emaM2eOfD5fwHlWrVqlvXv3yuFw+JfT6nuJZ8eOHbrzzjvVoUMHtW3bVtdff70WL14cULNhwwY5HA69/vrrmjFjhtxut9q1a6cBAwZo165d5/iJAGgoAgoQooqKinTbbbepoqJCCxcu1Ouvv67Y2FgNGTJEy5cvD6gdO3aswsLC/O/X+Oijj5SZmamjR49Kkk6cOKGsrCx9++23evHFF1VYWKi5c+eqS5cuOnbsmP8448aNU25urgYMGKCVK1dq/vz52rlzpzIyMvTtt98GnLO0tFT333+/cnJy9M477+iRRx7RmDFjtG/fPq1bty6g9vPPP9dHH32k0aNH+8f+7//+Tzk5OVqyZInefvttjR07Vv/1X/+lcePG+Wvmz5+vPn36yOVy6f333/cvZ7Nr1y5lZGRo586d+sMf/qCCggJdc801GjVqlObMmVOnfvr06dq7d6/+9Kc/6eWXX9bu3bs1ZMgQ1dTU/PgPCMC5GQAhqXfv3iYxMdEcO3bMP3bq1CmTlpZmOnfubGpra01eXp6RZO66666Aff/+978bSebpp582xhizZcsWI8msXLnyrOd7//33jSTz3//93wHj+/fvN1FRUWbKlCn+sb59+xpJ5m9/+1tArc/nM506dTI5OTkB41OmTDFt2rQx3333Xb3nrqmpMT6fz7z66qsmPDzcHDlyxL9t0KBBJiUlpd79JJmZM2f61++77z7jdDrNvn37AuoGDhxooqOjzdGjR40xxqxfv95IMnfccUdA3Z///Gcjybz//vv1ng9Aw3EFBQhBJ06c0Icffqi7775bl1xyiX88PDxcDzzwgA4cOBDwUsSvfvWrgP0zMjKUkpKi9evXS5KuvPJKdejQQY8//rj++Mc/6rPPPqtzzrffflsOh0P333+/Tp065V9cLpe6d++uDRs2BNR36NBBt912W8BYRESE7r//fhUUFKiiokKSVFNToyVLlujOO+9Ux44d/bVbt27V0KFD1bFjR4WHhysyMlIPPvigampq9MUXXzTp+7Zu3Tr1799fycnJAeOjRo1SVVVVnasvQ4cODVi/7rrrJEl79+5t0vkB/H8EFCAElZeXyxijpKSkOtvcbrck6fDhw/4xl8tVp87lcvlr4uLiVFRUpOuvv17Tp0/XtddeK7fbrZkzZ/rf8/Htt9/KGKNOnTopMjIyYPnggw/03XffBRy/vrlJ0pgxY/T9999r2bJlkqTVq1ertLQ04OWdffv26Wc/+5kOHjyo3//+99q0aZM+/vhjvfjii5KkkydPNvh79UOHDx9u8PdMUkBgkiSn03le5wfw/3EXDxCCOnTooLCwMJWWltbZ9s0330iSEhIStHv3bknSoUOH6tQdOnRIV155pX89PT1dy5YtkzFGn376qRYtWqSnnnpKUVFRmjp1qhISEuRwOLRp0yb/P9Q/dObYD9+s+kPXXHONbrrpJuXl5WncuHHKy8uT2+1Wdna2v2blypU6ceKECgoKlJKS4h/ftm3bOb4rP65jx44/+j0DcGFwBQUIQTExMerVq5cKCgoC/m++trZWS5cuVefOnXXVVVf5x1977bWA/Tdv3qy9e/cqMzOzzrEdDoe6d++u559/Xu3bt9cnn3wiSRo8eLCMMTp48KB69uxZZ0lPT2/w/EePHq0PP/xQxcXFeuuttzRy5EiFh4cHzEEKDD3GGL3yyit1juV0Oht8RaN///5at26dP5Cc9uqrryo6OprbkoELiCsoQIiaPXu2srKy1K9fP02ePFlt2rTR/PnztWPHDr3++usBVzC2bNmihx56SL/85S+1f/9+zZgxQ5dddpkeeeQRSf98f8n8+fM1bNgwXXHFFTLGqKCgQEePHlVWVpYkqU+fPnr44Yc1evRobdmyRbfeeqtiYmJUWlqq4uJipaen6ze/+U2D5j5ixAhNmjRJI0aMkNfr1ahRowK2Z2VlqU2bNhoxYoSmTJmi77//XgsWLFB5eXmdY6Wnp6ugoEALFixQjx49FBYWpp49e9Z73pkzZ+rtt99Wv3799J//+Z+Kj4/Xa6+9plWrVmnOnDmKi4tr0PwBNIPWfIcugJa1adMmc9ttt5mYmBgTFRVlevfubd566y3/9tN38axZs8Y88MADpn379iYqKsrccccdZvfu3f66zz//3IwYMcJ07drVREVFmbi4OHPTTTeZRYsW1Tnn//zP/5hevXr5z9m1a1fz4IMPmi1btvhr+vbta6699tpzzj0nJ8dIMn369Kl3+1tvvWW6d+9u2rZtay677DLz29/+1rz77rtGklm/fr2/7siRI+buu+827du3Nw6Hw/zwaU9n3MVjjDHbt283Q4YMMXFxcaZNmzame/fuJi8vL6Dm9F08f/nLXwLG9+zZYyTVqQfQeA5jjGnFfAQAAFAH70EBAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALBOUP6httraWn3zzTeKjY0965/LBgAAdjHG6NixY3K73QoLO/c1kqAMKN98802dTxsFAADBYf/+/ercufM5a4IyoMTGxkr6Z4Pt2rVr0jF8Pp/WrFmj7OxsRUZGNuf0rEB/wSuUe5PoL5iFcm8S/V0IlZWVSk5O9v87fi5BGVBOv6zTrl278woo0dHRateuXcj+ItJfcArl3iT6C2ah3JtEfxdSQ96ewZtkAQCAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKwT0doTANCyLp+6qrWnEMAZbjTnJinNs1remvo/cv3rZwdd4FkBsA1XUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOo0KKLNnz9aNN96o2NhYJSYmatiwYdq1a1dAzahRo+RwOAKW3r17B9R4vV5NnDhRCQkJiomJ0dChQ3XgwIHz7wYAAISERgWUoqIijR8/Xh988IEKCwt16tQpZWdn68SJEwF1P//5z1VaWupf3nnnnYDtubm5WrFihZYtW6bi4mIdP35cgwcPVk1Nzfl3BAAAgl6jPizwvffeC1jPy8tTYmKiSkpKdOutt/rHnU6nXC5XvceoqKjQwoULtWTJEg0YMECStHTpUiUnJ2vt2rW6/fbbG9sDAAAIMef1acYVFRWSpPj4+IDxDRs2KDExUe3bt1ffvn31zDPPKDExUZJUUlIin8+n7Oxsf73b7VZaWpo2b95cb0Dxer3yer3+9crKSkmSz+eTz+dr0txP79fU/W1Hf8GruXtzhptmOU5zcYaZgK/1CeafK7+bwYv+LtwcGsJhjGnSs5cxRnfeeafKy8u1adMm//jy5ct1ySWXKCUlRXv27NETTzyhU6dOqaSkRE6nU/n5+Ro9enRA4JCk7Oxspaam6qWXXqpzLo/HoyeffLLOeH5+vqKjo5syfQAAcIFVVVUpJydHFRUVateu3Tlrm3wFZcKECfr0009VXFwcMH7vvff6/zstLU09e/ZUSkqKVq1apeHDh5/1eMYYORyOerdNmzZNkyZN8q9XVlYqOTlZ2dnZP9rg2fh8PhUWFiorK0uRkZFNOobN6C94NXdvaZ7VzTCr5uMMM/pdz1o9sSVM3tr6H/M7PMH7Ui+/m8GL/lre6VdAGqJJAWXixIl68803tXHjRnXu3PmctUlJSUpJSdHu3bslSS6XS9XV1SovL1eHDh38dWVlZcrIyKj3GE6nU06ns854ZGTkeX+Tm+MYNqO/4NVcvXlr6g8Brc1b6zjr3ELhZ8rvZvCiv5Y9d0M16i4eY4wmTJiggoICrVu3TqmpqT+6z+HDh7V//34lJSVJknr06KHIyEgVFhb6a0pLS7Vjx46zBhQAAHBxadQVlPHjxys/P19//etfFRsbq0OHDkmS4uLiFBUVpePHj8vj8egXv/iFkpKS9PXXX2v69OlKSEjQXXfd5a8dO3asHnvsMXXs2FHx8fGaPHmy0tPT/Xf1AACAi1ujAsqCBQskSZmZmQHjeXl5GjVqlMLDw7V9+3a9+uqrOnr0qJKSktSvXz8tX75csbGx/vrnn39eERERuueee3Ty5En1799fixYtUnh4+Pl3BAAAgl6jAsqP3fATFRWl1at//A15bdu21bx58zRv3rzGnB4AAFwk+CweAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1Ilp7AkAwuXzqqhY/hzPcaM5NUppntbw1jhY/HwDYiCsoAADAOo0KKLNnz9aNN96o2NhYJSYmatiwYdq1a1dAjTFGHo9HbrdbUVFRyszM1M6dOwNqvF6vJk6cqISEBMXExGjo0KE6cODA+XcDAABCQqMCSlFRkcaPH68PPvhAhYWFOnXqlLKzs3XixAl/zZw5c/Tcc8/phRde0McffyyXy6WsrCwdO3bMX5Obm6sVK1Zo2bJlKi4u1vHjxzV48GDV1NQ0X2cAACBoNeo9KO+9917Ael5enhITE1VSUqJbb71VxhjNnTtXM2bM0PDhwyVJixcvVqdOnZSfn69x48apoqJCCxcu1JIlSzRgwABJ0tKlS5WcnKy1a9fq9ttvb6bWAABAsDqvN8lWVFRIkuLj4yVJe/bs0aFDh5Sdne2vcTqd6tu3rzZv3qxx48appKREPp8voMbtdistLU2bN2+uN6B4vV55vV7/emVlpSTJ5/PJ5/M1ae6n92vq/rajv5bhDDctf44wE/A11DSkv2D+vQ3lx14o9ybR34WcQ0M4jDFNehY0xujOO+9UeXm5Nm3aJEnavHmz+vTpo4MHD8rtdvtrH374Ye3du1erV69Wfn6+Ro8eHRA4JCk7O1upqal66aWX6pzL4/HoySefrDOen5+v6OjopkwfAABcYFVVVcrJyVFFRYXatWt3ztomX0GZMGGCPv30UxUXF9fZ5nAE3hppjKkzdqZz1UybNk2TJk3yr1dWVio5OVnZ2dk/2uDZ+Hw+FRYWKisrS5GRkU06hs3or2WkeVa3+DmcYUa/61mrJ7aEyVsbercZN6S/HZ7gfak3lB97odybRH8XwulXQBqiSQFl4sSJevPNN7Vx40Z17tzZP+5yuSRJhw4dUlJSkn+8rKxMnTp18tdUV1ervLxcHTp0CKjJyMio93xOp1NOp7POeGRk5Hl/k5vjGDajv+Z1If8uibfWEdJ/B+Vc/YXC72woP/ZCuTeJ/lr63A3VqLt4jDGaMGGCCgoKtG7dOqWmpgZsT01NlcvlUmFhoX+surpaRUVF/vDRo0cPRUZGBtSUlpZqx44dZw0oAADg4tKoKyjjx49Xfn6+/vrXvyo2NlaHDh2SJMXFxSkqKkoOh0O5ubmaNWuWunXrpm7dumnWrFmKjo5WTk6Ov3bs2LF67LHH1LFjR8XHx2vy5MlKT0/339UDAAAubo0KKAsWLJAkZWZmBozn5eVp1KhRkqQpU6bo5MmTeuSRR1ReXq5evXppzZo1io2N9dc///zzioiI0D333KOTJ0+qf//+WrRokcLDw8+vGwAAEBIaFVAacsOPw+GQx+ORx+M5a03btm01b948zZs3rzGnBwAAFwk+iwcAAFiHTzMGYJ0L8anRze3rZwe19hSAkMIVFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6zQ6oGzcuFFDhgyR2+2Ww+HQypUrA7aPGjVKDocjYOndu3dAjdfr1cSJE5WQkKCYmBgNHTpUBw4cOK9GAABA6Gh0QDlx4oS6d++uF1544aw1P//5z1VaWupf3nnnnYDtubm5WrFihZYtW6bi4mIdP35cgwcPVk1NTeM7AAAAISeisTsMHDhQAwcOPGeN0+mUy+Wqd1tFRYUWLlyoJUuWaMCAAZKkpUuXKjk5WWvXrtXtt9/e2CkBAIAQ0+iA0hAbNmxQYmKi2rdvr759++qZZ55RYmKiJKmkpEQ+n0/Z2dn+erfbrbS0NG3evLnegOL1euX1ev3rlZWVkiSfzyefz9ekOZ7er6n7247+WoYz3LT8OcJMwNdQE6r9nfk7GYqPvVDuTaK/CzmHhnAYY5r8LOFwOLRixQoNGzbMP7Z8+XJdcsklSklJ0Z49e/TEE0/o1KlTKikpkdPpVH5+vkaPHh0QOCQpOztbqampeumll+qcx+Px6Mknn6wznp+fr+jo6KZOHwAAXEBVVVXKyclRRUWF2rVrd87aZr+Ccu+99/r/Oy0tTT179lRKSopWrVql4cOHn3U/Y4wcDke926ZNm6ZJkyb51ysrK5WcnKzs7OwfbfBsfD6fCgsLlZWVpcjIyCYdw2b01zLSPKtb/BzOMKPf9azVE1vC5K2t/zERzEK1vx2ef179DeXHXij3JtHfhXD6FZCGaJGXeH4oKSlJKSkp2r17tyTJ5XKpurpa5eXl6tChg7+urKxMGRkZ9R7D6XTK6XTWGY+MjDzvb3JzHMNm9Ne8vDUX7h9Ub63jgp7vQgu1/s78PQzlx14o9ybRX0ufu6Fa/O+gHD58WPv371dSUpIkqUePHoqMjFRhYaG/prS0VDt27DhrQAEAABeXRl9BOX78uL788kv/+p49e7Rt2zbFx8crPj5eHo9Hv/jFL5SUlKSvv/5a06dPV0JCgu666y5JUlxcnMaOHavHHntMHTt2VHx8vCZPnqz09HT/XT0AAODi1uiAsmXLFvXr18+/fvq9ISNHjtSCBQu0fft2vfrqqzp69KiSkpLUr18/LV++XLGxsf59nn/+eUVEROiee+7RyZMn1b9/fy1atEjh4eHN0BIAAAh2jQ4omZmZOteNP6tX//ibCNu2bat58+Zp3rx5jT09AAC4CPBZPAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1Ilp7Arh4XT51VZP3dYYbzblJSvOslrfG0YyzAgDYgCsoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUaHVA2btyoIUOGyO12y+FwaOXKlQHbjTHyeDxyu92KiopSZmamdu7cGVDj9Xo1ceJEJSQkKCYmRkOHDtWBAwfOqxEAABA6Gh1QTpw4oe7du+uFF16od/ucOXP03HPP6YUXXtDHH38sl8ulrKwsHTt2zF+Tm5urFStWaNmyZSouLtbx48c1ePBg1dTUNL0TAAAQMiIau8PAgQM1cODAercZYzR37lzNmDFDw4cPlyQtXrxYnTp1Un5+vsaNG6eKigotXLhQS5Ys0YABAyRJS5cuVXJystauXavbb7+9znG9Xq+8Xq9/vbKyUpLk8/nk8/ka24J/3x9+DTXB0J8z3DR93zAT8DWUhHJvUuj2d+ZjzubHXlOFcm8S/V3IOTSEwxjT5GcJh8OhFStWaNiwYZKkr776Sl27dtUnn3yiG264wV935513qn379lq8eLHWrVun/v3768iRI+rQoYO/pnv37ho2bJiefPLJOufxeDz1jufn5ys6Orqp0wcAABdQVVWVcnJyVFFRoXbt2p2zttFXUM7l0KFDkqROnToFjHfq1El79+7117Rp0yYgnJyuOb3/maZNm6ZJkyb51ysrK5WcnKzs7OwfbfBsfD6fCgsLlZWVpcjIyCYdw2bB0F+aZ3WT93WGGf2uZ62e2BImb62jGWfV+kK5Nyl0+9vh+efV32B47DVVKPcm0d+FcPoVkIZo1oBymsMR+KRjjKkzdqZz1TidTjmdzjrjkZGR5/1Nbo5j2Mzm/rw15/+Pk7fW0SzHsVEo9yaFXn/dnlgj6Z8vXc65SbrhmXVB0d/Xzw5q9D42P680B/pr2XM3VLPeZuxyuSSpzpWQsrIy/1UVl8ul6upqlZeXn7UGAABc3Jo1oKSmpsrlcqmwsNA/Vl1draKiImVkZEiSevToocjIyICa0tJS7dixw18DAAAubo1+ief48eP68ssv/et79uzRtm3bFB8fry5duig3N1ezZs1St27d1K1bN82aNUvR0dHKycmRJMXFxWns2LF67LHH1LFjR8XHx2vy5MlKT0/339UDAAAubo0OKFu2bFG/fv3866ffvDpy5EgtWrRIU6ZM0cmTJ/XII4+ovLxcvXr10po1axQbG+vf5/nnn1dERITuuecenTx5Uv3799eiRYsUHh7eDC0BAIBg1+iAkpmZqXPdmexwOOTxeOTxeM5a07ZtW82bN0/z5s1r7OkBAMBFgM/iAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArBPR2hNA87h86qqAdWe40ZybpDTPanlrHK00KwAAmoYrKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6zR7QPF4PHI4HAGLy+XybzfGyOPxyO12KyoqSpmZmdq5c2dzTwMAAASxFrmCcu2116q0tNS/bN++3b9tzpw5eu655/TCCy/o448/lsvlUlZWlo4dO9YSUwEAAEGoRQJKRESEXC6Xf7n00ksl/fPqydy5czVjxgwNHz5caWlpWrx4saqqqpSfn98SUwEAAEGoRf7U/e7du+V2u+V0OtWrVy/NmjVLV1xxhfbs2aNDhw4pOzvbX+t0OtW3b19t3rxZ48aNq/d4Xq9XXq/Xv15ZWSlJ8vl88vl8TZrj6f2aur9tnOEmcD3MBHwNNaHcXyj3JtGfbRrzHBhqz5tnor8LN4eGcBhjmvVR9O6776qqqkpXXXWVvv32Wz399NP6/PPPtXPnTu3atUt9+vTRwYMH5Xa7/fs8/PDD2rt3r1avXl3vMT0ej5588sk64/n5+YqOjm7O6QMAgBZSVVWlnJwcVVRUqF27duesbfaAcqYTJ06oa9eumjJlinr37q0+ffrom2++UVJSkr/mX//1X7V//36999579R6jvisoycnJ+u677360wbPx+XwqLCxUVlaWIiMjm3QMm6R5AsOdM8zodz1r9cSWMHlrQ+/DAkO5v1DuTaI/2+zw3N7g2lB73jwT/bW8yspKJSQkNCigtPinGcfExCg9PV27d+/WsGHDJEmHDh0KCChlZWXq1KnTWY/hdDrldDrrjEdGRp73N7k5jmGDs31isbfWEdKfZhzK/YVybxL92aIpz3+h8rx5NvTXsuduqBb/Oyher1f/+Mc/lJSUpNTUVLlcLhUWFvq3V1dXq6ioSBkZGS09FQAAECSa/QrK5MmTNWTIEHXp0kVlZWV6+umnVVlZqZEjR8rhcCg3N1ezZs1St27d1K1bN82aNUvR0dHKyclp7qkAAIAg1ewB5cCBAxoxYoS+++47XXrpperdu7c++OADpaSkSJKmTJmikydP6pFHHlF5ebl69eqlNWvWKDY2trmnAgAAglSzB5Rly5adc7vD4ZDH45HH42nuUwMAgBDBZ/EAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYJ1m/7BAAEDwuHzqqgbXOsON5twkpXlWy1vjaMFZndvXzw5qtXPjwuEKCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYJ2I1p6AjS6fuqq1pwAAwEWNKygAAMA6BBQAAGAdAgoAALAO70EBAASVlnqfoDPcaM5NUppntbw1jmY99tfPDmrW410MuIICAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYJ1WDSjz589Xamqq2rZtqx49emjTpk2tOR0AAGCJVgsoy5cvV25urmbMmKGtW7fqZz/7mQYOHKh9+/a11pQAAIAlWi2gPPfccxo7dqweeugh/eQnP9HcuXOVnJysBQsWtNaUAACAJSJa46TV1dUqKSnR1KlTA8azs7O1efPmOvVer1der9e/XlFRIUk6cuSIfD5fk+bg8/lUVVWlw4cPKzIyMmBbxKkTTTqmTSJqjaqqahXhC1NNraO1p9PsQrm/UO5Nor9gFsq9SS3b35WT/9ysx2sKZ5jRf9xQq+tnFMjbgP4+nNa/2edw7NgxSZIx5seLTSs4ePCgkWT+/ve/B4w/88wz5qqrrqpTP3PmTCOJhYWFhYWFJQSW/fv3/2hWaJUrKKc5HIEJzhhTZ0ySpk2bpkmTJvnXa2trdeTIEXXs2LHe+oaorKxUcnKy9u/fr3bt2jXpGDajv+AVyr1J9BfMQrk3if4uBGOMjh07Jrfb/aO1rRJQEhISFB4erkOHDgWMl5WVqVOnTnXqnU6nnE5nwFj79u2bZS7t2rULyV/E0+gveIVybxL9BbNQ7k2iv5YWFxfXoLpWeZNsmzZt1KNHDxUWFgaMFxYWKiMjozWmBAAALNJqL/FMmjRJDzzwgHr27Kmbb75ZL7/8svbt26df//rXrTUlAABgiVYLKPfee68OHz6sp556SqWlpUpLS9M777yjlJSUC3J+p9OpmTNn1nnpKFTQX/AK5d4k+gtmodybRH+2cRjTkHt9AAAALhw+iwcAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUu2oAyf/58paamqm3bturRo4c2bdrU2lNqko0bN2rIkCFyu91yOBxauXJlwHZjjDwej9xut6KiopSZmamdO3e2zmQbafbs2brxxhsVGxurxMREDRs2TLt27QqoCeb+FixYoOuuu87/Vx1vvvlmvfvuu/7twdzbmWbPni2Hw6Hc3Fz/WDD35/F45HA4AhaXy+XfHsy9SdLBgwd1//33q2PHjoqOjtb111+vkpIS//Zg7u/yyy+v87NzOBwaP368pODuTZJOnTql//iP/1BqaqqioqJ0xRVX6KmnnlJtba2/Jmh6PK9P/QtSy5YtM5GRkeaVV14xn332mXn00UdNTEyM2bt3b2tPrdHeeecdM2PGDPPGG28YSWbFihUB25999lkTGxtr3njjDbN9+3Zz7733mqSkJFNZWdk6E26E22+/3eTl5ZkdO3aYbdu2mUGDBpkuXbqY48eP+2uCub8333zTrFq1yuzatcvs2rXLTJ8+3URGRpodO3YYY4K7tx/66KOPzOWXX26uu+468+ijj/rHg7m/mTNnmmuvvdaUlpb6l7KyMv/2YO7tyJEjJiUlxYwaNcp8+OGHZs+ePWbt2rXmyy+/9NcEc39lZWUBP7fCwkIjyaxfv94YE9y9GWPM008/bTp27Gjefvtts2fPHvOXv/zFXHLJJWbu3Ln+mmDp8aIMKDfddJP59a9/HTB29dVXm6lTp7bSjJrHmQGltrbWuFwu8+yzz/rHvv/+exMXF2f++Mc/tsIMz09ZWZmRZIqKiowxodefMcZ06NDB/OlPfwqZ3o4dO2a6detmCgsLTd++ff0BJdj7mzlzpunevXu924K9t8cff9zccsstZ90e7P2d6dFHHzVdu3Y1tbW1IdHboEGDzJgxYwLGhg8fbu6//35jTHD9/C66l3iqq6tVUlKi7OzsgPHs7Gxt3ry5lWbVMvbs2aNDhw4F9Op0OtW3b9+g7LWiokKSFB8fLym0+qupqdGyZct04sQJ3XzzzSHT2/jx4zVo0CANGDAgYDwU+tu9e7fcbrdSU1N133336auvvpIU/L29+eab6tmzp375y18qMTFRN9xwg1555RX/9mDv74eqq6u1dOlSjRkzRg6HIyR6u+WWW/S3v/1NX3zxhSTpf//3f1VcXKw77rhDUnD9/FrtT923lu+++041NTV1PjW5U6dOdT5dOdid7qe+Xvfu3dsaU2oyY4wmTZqkW265RWlpaZJCo7/t27fr5ptv1vfff69LLrlEK1as0DXXXON/ogjm3pYtW6ZPPvlEH3/8cZ1twf6z69Wrl1599VVdddVV+vbbb/X0008rIyNDO3fuDPrevvrqKy1YsECTJk3S9OnT9dFHH+nf/u3f5HQ69eCDDwZ9fz+0cuVKHT16VKNGjZIU/L+XkvT444+roqJCV199tcLDw1VTU6NnnnlGI0aMkBRcPV50AeU0h8MRsG6MqTMWKkKh1wkTJujTTz9VcXFxnW3B3N+//Mu/aNu2bTp69KjeeOMNjRw5UkVFRf7twdrb/v379eijj2rNmjVq27btWeuCtb+BAwf6/zs9PV0333yzunbtqsWLF6t3796Sgre32tpa9ezZU7NmzZIk3XDDDdq5c6cWLFigBx980F8XrP390MKFCzVw4EC53e6A8WDubfny5Vq6dKny8/N17bXXatu2bcrNzZXb7dbIkSP9dcHQ40X3Ek9CQoLCw8PrXC0pKyurkyiD3em7CoK914kTJ+rNN9/U+vXr1blzZ/94KPTXpk0bXXnllerZs6dmz56t7t276/e//33Q91ZSUqKysjL16NFDERERioiIUFFRkf7whz8oIiLC30Ow9nemmJgYpaena/fu3UH/s0tKStI111wTMPaTn/xE+/btkxQajztJ2rt3r9auXauHHnrIPxYKvf32t7/V1KlTdd999yk9PV0PPPCA/v3f/12zZ8+WFFw9XnQBpU2bNurRo4cKCwsDxgsLC5WRkdFKs2oZqampcrlcAb1WV1erqKgoKHo1xmjChAkqKCjQunXrlJqaGrA92PurjzFGXq836Hvr37+/tm/frm3btvmXnj176le/+pW2bdumK664Iqj7O5PX69U//vEPJSUlBf3Prk+fPnVu5//iiy/8nzQf7P2dlpeXp8TERA0aNMg/Fgq9VVVVKSws8J/28PBw/23GQdVj67w3t3Wdvs144cKF5rPPPjO5ubkmJibGfP311609tUY7duyY2bp1q9m6dauRZJ577jmzdetW/y3Tzz77rImLizMFBQVm+/btZsSIEVbeTlaf3/zmNyYuLs5s2LAh4LbAqqoqf00w9zdt2jSzceNGs2fPHvPpp5+a6dOnm7CwMLNmzRpjTHD3Vp8f3sVjTHD399hjj5kNGzaYr776ynzwwQdm8ODBJjY21v8cEsy9ffTRRyYiIsI888wzZvfu3ea1114z0dHRZunSpf6aYO7PGGNqampMly5dzOOPP15nW7D3NnLkSHPZZZf5bzMuKCgwCQkJZsqUKf6aYOnxogwoxhjz4osvmpSUFNOmTRvz05/+1H/rarBZv369kVRnGTlypDHmn7eUzZw507hcLuN0Os2tt95qtm/f3rqTbqD6+pJk8vLy/DXB3N+YMWP8v4OXXnqp6d+/vz+cGBPcvdXnzIASzP2d/rsRkZGRxu12m+HDh5udO3f6twdzb8YY89Zbb5m0tDTjdDrN1VdfbV5++eWA7cHe3+rVq40ks2vXrjrbgr23yspK8+ijj5ouXbqYtm3bmiuuuMLMmDHDeL1ef02w9OgwxphWuXQDAABwFhfde1AAAID9CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYJ3/BzR//bCIMaWGAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "age = pd.read_csv('data/ages_population.csv')\n", + "age.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nThe mean would be around 38, and the standard deviation to be 15\\n'" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "The mean could be around 38, and the standard deviation could be around 15\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36.56\n" + ] + }, + { + "data": { + "text/plain": [ + "12.816499625976762" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "print(mean(age['observation']))\n", + "age['observation'].std()" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "They were approximate, actually the standard deviation was a little bit below what we expected.So most data is compiled in a way smaller range than what we thought<.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]], dtype=object)" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "age2 = pd.read_csv('data/ages_population2.csv')\n", + "age2.hist()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\nThe mean could be around 27\\nThe data is less disperse, so the standard deviation probably has a fewer value.\\nAnd the range is shorted\\n'" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "The data is less disperse, so the standard deviation probably has a fewer value.\n", + "The mean could be around 27\n", + "And the range is shorter\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27.155\n", + "2.969813932689186\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "print(age2['observation'].mean())\n", + "print(age2['observation'].std())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "Indeed, the mean was around 27 and the standard deviation is way less than the first csv.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 5\n", + "Now is the turn of `ages_population3.csv`.\n", + "\n", + "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]], dtype=object)" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGxCAYAAABBZ+3pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxqklEQVR4nO3de3SU1aH+8WeSTIYEEm4xJFNDBC/HahCVFBCsBDHRiFGk3khVEG+t1iMHLaIcfgxWLouueikIVUuDCBHqEjgqWgjKteCFKEeCFaNGQAmyQEiA6DAk+/dHV+Y4JKhJZsjOO9/PWrPsvO9+9+wnw0yevjOTcRljjAAAACwW09oLAAAA+DEUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAehQWIIrNmzdPLpdLmzdvbu2lRExxcbGefPLJRve5XC75fL6Tuh4AzRPX2gsAgEgqLi5WWVmZxowZ02Dfpk2bdOqpp578RQFoMgoLACvU1NQoMTHxpN5m//79T+rtAWg+XhICHGzDhg0aMmSIkpKSlJiYqAEDBmj58uUNxh04cEC33XabunTpovbt26ugoECff/55yJgPPvhAV111lVJTU+XxeOT1ejV06FB9+eWXwTHGGM2ePVvnn3++EhIS1LlzZ1133XUN5srJyVFWVpbWrVunAQMGKDExUaNHj9awYcOUmZmpurq6Bmvs16+fLrzwwuD1p59+WpdccolSU1PVvn179erVSzNmzFAgEAi5neXLl2vHjh1yuVzBS73GXhIqKyvTNddco86dO6tdu3Y6//zz9fzzz4eMWbNmjVwul1588UVNmDBBXq9XycnJuuyyy7R9+/YfuEcANBeFBXCotWvX6tJLL1VVVZXmzp2rF198UUlJSSooKNDixYtDxt5+++2KiYkJvt/j3XffVU5Ojg4ePChJOnLkiHJzc/X111/r6aefVklJiZ588kl1795dhw4dCs5z9913a8yYMbrsssu0bNkyzZ49W9u2bdOAAQP09ddfh9xmZWWlbr75ZhUWFur111/XPffco9GjR2vnzp166623QsZ+/PHHevfdd3XbbbcFt3322WcqLCzUCy+8oNdee0233367/vjHP+ruu+8Ojpk9e7YGDhyotLQ0bdq0KXg5ke3bt2vAgAHatm2b/vznP2vJkiU655xzNGrUKM2YMaPB+EceeUQ7duzQX//6Vz377LMqLy9XQUGBamtrf/wOAtA0BoAj9e/f36SmpppDhw4Ftx07dsxkZWWZU0891dTV1ZmioiIjyVx77bUhx/7zn/80ksxjjz1mjDFm8+bNRpJZtmzZCW9v06ZNRpL505/+FLJ9165dJiEhwYwbNy64bdCgQUaSefPNN0PGBgIB061bN1NYWBiyfdy4cSY+Pt7s27ev0duura01gUDAzJ8/38TGxppvvvkmuG/o0KEmMzOz0eMkmUmTJgWv33TTTcbj8ZidO3eGjMvPzzeJiYnm4MGDxhhjVq9ebSSZK6+8MmTc3//+dyPJbNq0qdHbA9B8nGEBHOjIkSN65513dN1116lDhw7B7bGxsbrlllv05Zdfhrx08etf/zrk+AEDBigzM1OrV6+WJJ1xxhnq3LmzHnroIf3lL3/RRx991OA2X3vtNblcLt188806duxY8JKWlqbevXtrzZo1IeM7d+6sSy+9NGRbXFycbr75Zi1ZskRVVVWSpNraWr3wwgu65ppr1LVr1+DYDz74QFdffbW6du2q2NhYud1u3XrrraqtrdUnn3zSrJ/bW2+9pSFDhigjIyNk+6hRo1RTU9Pg7MzVV18dcv28886TJO3YsaNZtw/gxCgsgAMdOHBAxhilp6c32Of1eiVJ+/fvD25LS0trMC4tLS04pmPHjlq7dq3OP/98PfLIIzr33HPl9Xo1adKk4HtGvv76axlj1K1bN7nd7pDL22+/rX379oXM39jaJGn06NH67rvvtGjRIknSihUrVFlZGfJy0M6dO/XLX/5SX331lZ566imtX79e7733np5++mlJ0rfffvuTf1bft3///p/8M5MUUqAkyePxtOj2AZwYnxICHKhz586KiYlRZWVlg327d++WJKWkpKi8vFyStGfPngbj9uzZozPOOCN4vVevXlq0aJGMMfrwww81b948Pfroo0pISND48eOVkpIil8ul9evXB39xf9/x277/5tfvO+ecc9S3b18VFRXp7rvvVlFRkbxer/Ly8oJjli1bpiNHjmjJkiXKzMwMbt+yZcsP/FR+XNeuXX/0ZwagdXCGBXCg9u3bq1+/flqyZEnI/9uvq6vTggULdOqpp+qss84Kbl+4cGHI8Rs3btSOHTuUk5PTYG6Xy6XevXvriSeeUKdOnfT+++9Lkq666ioZY/TVV18pOzu7waVXr14/ef233Xab3nnnHW3YsEGvvvqqRo4cqdjY2JA1SKElyBij5557rsFcHo/nJ5/xGDJkiN56661gQak3f/58JSYm8jFooBVxhgVwqGnTpik3N1eDBw/Wgw8+qPj4eM2ePVtlZWV68cUXQ85wbN68WXfccYeuv/567dq1SxMmTNDPfvYz3XPPPZL+/f6U2bNna9iwYerZs6eMMVqyZIkOHjyo3NxcSdLAgQN111136bbbbtPmzZt1ySWXqH379qqsrNSGDRvUq1cv/fa3v/1Jax8xYoTGjh2rESNGyO/3a9SoUSH7c3NzFR8frxEjRmjcuHH67rvvNGfOHB04cKDBXL169dKSJUs0Z84c9enTRzExMcrOzm70didNmqTXXntNgwcP1v/7f/9PXbp00cKFC7V8+XLNmDFDHTt2/EnrBxABrfmOXwCRtX79enPppZea9u3bm4SEBNO/f3/z6quvBvfXf0po5cqV5pZbbjGdOnUyCQkJ5sorrzTl5eXBcR9//LEZMWKEOf30001CQoLp2LGj6du3r5k3b16D2/zb3/5m+vXrF7zN008/3dx6661m8+bNwTGDBg0y55577g+uvbCw0EgyAwcObHT/q6++anr37m3atWtnfvazn5nf//735o033jCSzOrVq4PjvvnmG3PdddeZTp06GZfLZb7/tKfjPiVkjDFbt241BQUFpmPHjiY+Pt707t3bFBUVhYyp/5TQSy+9FLK9oqLCSGowHkDLuYwxphX7EgAAwI/iPSwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANZrk384rq6uTrt371ZSUtIJ/7w3AACwizFGhw4dktfrVUxM086ZtMnCsnv37gbfpgoAANqGXbt26dRTT23SMW2ysCQlJUn6d+Dk5ORmzREIBLRy5Url5eXJ7XaHc3nWiaasUnTlJatzRVNesjrX8Xmrq6uVkZER/D3eFG2ysNS/DJScnNyiwpKYmKjk5GTH/6OJpqxSdOUlq3NFU16yOteJ8jbn7Ry86RYAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAenGtvQAAkXXa+OWtvYQm+2L60NZeAgDLcIYFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArNfkwrJu3ToVFBTI6/XK5XJp2bJlIftdLlejlz/+8Y/BMTk5OQ3233TTTS0OAwAAnKnJheXIkSPq3bu3Zs2a1ej+ysrKkMvf/vY3uVwu/epXvwoZd+edd4aMe+aZZ5qXAAAAOF5cUw/Iz89Xfn7+CfenpaWFXP+f//kfDR48WD179gzZnpiY2GAsAABAY5pcWJri66+/1vLly/X888832Ldw4UItWLBA3bp1U35+viZNmqSkpKRG5/H7/fL7/cHr1dXVkqRAIKBAINCstdUf19zj25JoyipFV96fktUTa07WcsKmsTzRdL9K0ZWXrM51fN6W5HYZY5r9bOZyubR06VINGzas0f0zZszQ9OnTtXv3brVr1y64/bnnnlOPHj2UlpamsrIyPfzwwzrjjDNUUlLS6Dw+n0+TJ09usL24uFiJiYnNXT4AADiJampqVFhYqKqqKiUnJzfp2IgWlrPPPlu5ubmaOXPmD85TWlqq7OxslZaW6sILL2ywv7EzLBkZGdq3b1+TA9cLBAIqKSlRbm6u3G53s+ZoK6IpqxRdeX9K1izfipO8qpYr813eYFs03a9SdOUlq3Mdn7e6ulopKSnNKiwRe0lo/fr12r59uxYvXvyjYy+88EK53W6Vl5c3Wlg8Ho88Hk+D7W63u8V3eDjmaCuiKasUXXl/KKu/1nWSV9NyZ05c2WCbJ9ZoRl/pgilvWZnpi+lDIzIv/46dKZqySv+XtyWZI/Z3WObOnas+ffqod+/ePzp227ZtCgQCSk9Pj9RyAABAG9bkMyyHDx/Wp59+GrxeUVGhLVu2qEuXLurevbukf79k89JLL+lPf/pTg+M/++wzLVy4UFdeeaVSUlL00Ucf6YEHHtAFF1yggQMHtiAKAABwqiYXls2bN2vw4MHB62PHjpUkjRw5UvPmzZMkLVq0SMYYjRgxosHx8fHxevPNN/XUU0/p8OHDysjI0NChQzVp0iTFxsY2MwYAAHCyJheWnJwc/dj7dO+66y7dddddje7LyMjQ2rVrm3qzAAAgivFdQgAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHpxrb0AoC05bfzy1l5CCE+s0Yy+UpZvhfy1rtZeDgBEDGdYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6TS4s69atU0FBgbxer1wul5YtWxayf9SoUXK5XCGX/v37h4zx+/267777lJKSovbt2+vqq6/Wl19+2aIgAADAuZpcWI4cOaLevXtr1qxZJxxzxRVXqLKyMnh5/fXXQ/aPGTNGS5cu1aJFi7RhwwYdPnxYV111lWpra5ueAAAAOF6Tv/wwPz9f+fn5PzjG4/EoLS2t0X1VVVWaO3euXnjhBV122WWSpAULFigjI0OrVq3S5Zdf3tQlAQAAh4vItzWvWbNGqamp6tSpkwYNGqQpU6YoNTVVklRaWqpAIKC8vLzgeK/Xq6ysLG3cuLHRwuL3++X3+4PXq6urJUmBQECBQKBZa6w/rrnHtyXRlFWKbF5PrAn7nC3hiTEh/3Uy27OG+99bND1uyepcx+dtSW6XMabZj36Xy6WlS5dq2LBhwW2LFy9Whw4dlJmZqYqKCk2cOFHHjh1TaWmpPB6PiouLddttt4UUEEnKy8tTjx499MwzzzS4HZ/Pp8mTJzfYXlxcrMTExOYuHwAAnEQ1NTUqLCxUVVWVkpOTm3Rs2M+w3HjjjcH/nZWVpezsbGVmZmr58uUaPnz4CY8zxsjlcjW67+GHH9bYsWOD16urq5WRkaG8vLwmB64XCARUUlKi3Nxcud3uZs3RVkRTVimyebN8K8I6X0t5Yoz+kF2niZtj5K9r/PHjFLZnLfOF9+XsaHrcktW5js9b/wpJc0TkJaHvS09PV2ZmpsrLyyVJaWlpOnr0qA4cOKDOnTsHx+3du1cDBgxodA6PxyOPx9Ngu9vtbvEdHo452opoyipFJq+/1r5flJLkr3NZu7ZwszVrpB5b0fS4Jatz1edtSeaI/x2W/fv3a9euXUpPT5ck9enTR263WyUlJcExlZWVKisrO2FhAQAA0a3JZ1gOHz6sTz/9NHi9oqJCW7ZsUZcuXdSlSxf5fD796le/Unp6ur744gs98sgjSklJ0bXXXitJ6tixo26//XY98MAD6tq1q7p06aIHH3xQvXr1Cn5qCAAA4PuaXFg2b96swYMHB6/Xv7dk5MiRmjNnjrZu3ar58+fr4MGDSk9P1+DBg7V48WIlJSUFj3niiScUFxenG264Qd9++62GDBmiefPmKTY2NgyRAACA0zS5sOTk5OiHPli0YsWPvymxXbt2mjlzpmbOnNnUmwcAAFGI7xICAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWi2vtBQCAE5w2fnlY5/PEGs3oK2X5Vshf6wrr3N/3xfShEZsbCCfOsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9ZpcWNatW6eCggJ5vV65XC4tW7YsuC8QCOihhx5Sr1691L59e3m9Xt16663avXt3yBw5OTlyuVwhl5tuuqnFYQAAgDM1ubAcOXJEvXv31qxZsxrsq6mp0fvvv6+JEyfq/fff15IlS/TJJ5/o6quvbjD2zjvvVGVlZfDyzDPPNC8BAABwvCZ/+WF+fr7y8/Mb3dexY0eVlJSEbJs5c6b69u2rnTt3qnv37sHtiYmJSktLa+rNAwCAKBTxb2uuqqqSy+VSp06dQrYvXLhQCxYsULdu3ZSfn69JkyYpKSmp0Tn8fr/8fn/wenV1taR/vwQVCASata7645p7fFsSTVmlyOb1xJqwz9kSnhgT8l8ni6as0snLa8PzQjQ9R0VTVqlh3pbkdhljmv1ocLlcWrp0qYYNG9bo/u+++04XX3yxzj77bC1YsCC4/bnnnlOPHj2UlpamsrIyPfzwwzrjjDManJ2p5/P5NHny5Abbi4uLlZiY2NzlAwCAk6impkaFhYWqqqpScnJyk46NWGEJBAK6/vrrtXPnTq1Zs+YHF1ZaWqrs7GyVlpbqwgsvbLC/sTMsGRkZ2rdvX5MDf399JSUlys3NldvtbtYcbUU0ZZUimzfLtyKs87WUJ8boD9l1mrg5Rv46V2svJ6KiKat08vKW+S6P2Nw/VTQ9R0VTVqlh3urqaqWkpDSrsETkJaFAIKAbbrhBFRUVeuutt350URdeeKHcbrfKy8sbLSwej0cej6fBdrfb3eI7PBxztBXRlFWKTF5/rZ2/KP11LmvXFm7RlFWKfF6bnhOi6TkqmrJK/5e3JZnDXljqy0p5eblWr16trl27/ugx27ZtUyAQUHp6eriXAwAAHKDJheXw4cP69NNPg9crKiq0ZcsWdenSRV6vV9ddd53ef/99vfbaa6qtrdWePXskSV26dFF8fLw+++wzLVy4UFdeeaVSUlL00Ucf6YEHHtAFF1yggQMHhi8ZAABwjCYXls2bN2vw4MHB62PHjpUkjRw5Uj6fT6+88ook6fzzzw85bvXq1crJyVF8fLzefPNNPfXUUzp8+LAyMjI0dOhQTZo0SbGxsS2IAgAAnKrJhSUnJ0c/9D7dH3sPb0ZGhtauXdvUmwUAAFGM7xICAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKwXkS8/BAAgUk4bv7y1l9BkX0wf2tpLaPM4wwIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOs1ubCsW7dOBQUF8nq9crlcWrZsWch+Y4x8Pp+8Xq8SEhKUk5Ojbdu2hYzx+/267777lJKSovbt2+vqq6/Wl19+2aIgAADAuZpcWI4cOaLevXtr1qxZje6fMWOGHn/8cc2aNUvvvfee0tLSlJubq0OHDgXHjBkzRkuXLtWiRYu0YcMGHT58WFdddZVqa2ubnwQAADhWXFMPyM/PV35+fqP7jDF68sknNWHCBA0fPlyS9Pzzz6tbt24qLi7W3XffraqqKs2dO1cvvPCCLrvsMknSggULlJGRoVWrVunyyy9vQRwAAOBETS4sP6SiokJ79uxRXl5ecJvH49GgQYO0ceNG3X333SotLVUgEAgZ4/V6lZWVpY0bNzZaWPx+v/x+f/B6dXW1JCkQCCgQCDRrrfXHNff4tiSaskqRzeuJNWGfsyU8MSbkv04WTVmlk5fXhueFpj5mbXsc/hTHZ7Th534yhDN3WAvLnj17JEndunUL2d6tWzft2LEjOCY+Pl6dO3duMKb++ONNmzZNkydPbrB95cqVSkxMbNGaS0pKWnR8WxJNWaXI5J3RN+xThsUfsutaewknTTRllSKf9/XXX4/o/E3xUx+ztj4Of8jxP+dofT6uqalp9hxhLSz1XC5XyHVjTINtx/uhMQ8//LDGjh0bvF5dXa2MjAzl5eUpOTm5WWsMBAIqKSlRbm6u3G53s+ZoK6IpqxTZvFm+FWGdr6U8MUZ/yK7TxM0x8tf98GOsrYumrNLJy1vma/2X4Zv6mLXtcfhT1P+co/35uP4VkuYIa2FJS0uT9O+zKOnp6cHte/fuDZ51SUtL09GjR3XgwIGQsyx79+7VgAEDGp3X4/HI4/E02O52u1t8h4djjrYimrJKkcnrr7XzF6W/zmXt2sItmrJKkc9r03PCT33MtsX7//hc0fp83JLMYf07LD169FBaWlrIqa6jR49q7dq1wTLSp08fud3ukDGVlZUqKys7YWEBAADRrclnWA4fPqxPP/00eL2iokJbtmxRly5d1L17d40ZM0ZTp07VmWeeqTPPPFNTp05VYmKiCgsLJUkdO3bU7bffrgceeEBdu3ZVly5d9OCDD6pXr17BTw0BAAB8X5MLy+bNmzV48ODg9fr3lowcOVLz5s3TuHHj9O233+qee+7RgQMH1K9fP61cuVJJSUnBY5544gnFxcXphhtu0LfffqshQ4Zo3rx5io2NDUMkAADgNE0uLDk5OTLmxB8pc7lc8vl88vl8JxzTrl07zZw5UzNnzmzqzQMAgCjEdwkBAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPXiWnsBiF6njV8ekXk9sUYz+kpZvhXy17oichsAgJOLMywAAMB6YS8sp512mlwuV4PLvffeK0kaNWpUg339+/cP9zIAAICDhP0loffee0+1tbXB62VlZcrNzdX1118f3HbFFVeoqKgoeD0+Pj7cywAAAA4S9sJyyimnhFyfPn26Tj/9dA0aNCi4zePxKC0tLdw3DQAAHCqib7o9evSoFixYoLFjx8rl+r83P65Zs0apqanq1KmTBg0apClTpig1NfWE8/j9fvn9/uD16upqSVIgEFAgEGjW2uqPa+7xbYmtWT2xJjLzxpiQ/zoZWZ3rZOW14Xmhqc9RkXruiKTjM9rwcz8ZwpnbZYyJ2D3/97//XYWFhdq5c6e8Xq8kafHixerQoYMyMzNVUVGhiRMn6tixYyotLZXH42l0Hp/Pp8mTJzfYXlxcrMTExEgtHwAAhFFNTY0KCwtVVVWl5OTkJh0b0cJy+eWXKz4+Xq+++uoJx1RWViozM1OLFi3S8OHDGx3T2BmWjIwM7du3r8mB6wUCAZWUlCg3N1dut7tZc7QVtmbN8q2IyLyeGKM/ZNdp4uYY+euc/bFmsjrXycpb5rs8YnP/VE19jorUc0ck1f+cbX0+jpTj81ZXVyslJaVZhSViLwnt2LFDq1at0pIlS35wXHp6ujIzM1VeXn7CMR6Pp9GzL263u8V3eDjmaCtsyxrpv5Hir3NFzd9hIatzRTqvTc8JP/U5qi3e/8fnsu35ONLq87Ykc8T+DktRUZFSU1M1dOjQHxy3f/9+7dq1S+np6ZFaCgAAaOMiUljq6upUVFSkkSNHKi7u/07iHD58WA8++KA2bdqkL774QmvWrFFBQYFSUlJ07bXXRmIpAADAASLyktCqVau0c+dOjR49OmR7bGystm7dqvnz5+vgwYNKT0/X4MGDtXjxYiUlJUViKQAAwAEiUljy8vLU2Ht5ExIStGJF23uzFAAAaF18lxAAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKwX19oLAAC0ntPGL2/tJcgTazSjr5TlWyF/rau1lwNLcYYFAABYj8ICAACsR2EBAADW4z0sAABEWP17hdrS+3W+mD60tZcQgjMsAADAehQWAABgPQoLAACwHoUFAABYL+yFxefzyeVyhVzS0tKC+40x8vl88nq9SkhIUE5OjrZt2xbuZQAAAAeJyBmWc889V5WVlcHL1q1bg/tmzJihxx9/XLNmzdJ7772ntLQ05ebm6tChQ5FYCgAAcICIFJa4uDilpaUFL6eccoqkf59defLJJzVhwgQNHz5cWVlZev7551VTU6Pi4uJILAUAADhARP4OS3l5ubxerzwej/r166epU6eqZ8+eqqio0J49e5SXlxcc6/F4NGjQIG3cuFF33313o/P5/X75/f7g9erqaklSIBBQIBBo1hrrj2vu8W2JrVk9sSYy88aYkP86GVmdK5ryktVO4fidcfzvn5bM6TLGhPWn9sYbb6impkZnnXWWvv76az322GP6+OOPtW3bNm3fvl0DBw7UV199Ja/XGzzmrrvu0o4dO7RixYpG5/T5fJo8eXKD7cXFxUpMTAzn8gEAQITU1NSosLBQVVVVSk5ObtKxYS8sxzty5IhOP/10jRs3Tv3799fAgQO1e/dupaenB8fceeed2rVrl/7xj380OkdjZ1gyMjK0b9++JgeuFwgEVFJSotzcXLnd7mbN0VbYmjXL13hBbSlPjNEfsus0cXOM/HV2/yXJliKrc0VTXrLaqcx3eYvnOP73T3V1tVJSUppVWCL+p/nbt2+vXr16qby8XMOGDZMk7dmzJ6Sw7N27V926dTvhHB6PRx6Pp8F2t9vd4l/A4ZijrbAta6T/LLW/zmX9n74OF7I6VzTlJatdwvn7ov73T0vmjPjfYfH7/frXv/6l9PR09ejRQ2lpaSopKQnuP3r0qNauXasBAwZEeikAAKCNCvsZlgcffFAFBQXq3r279u7dq8cee0zV1dUaOXKkXC6XxowZo6lTp+rMM8/UmWeeqalTpyoxMVGFhYXhXgoAAHCIsBeWL7/8UiNGjNC+fft0yimnqH///nr77beVmZkpSRo3bpy+/fZb3XPPPTpw4ID69eunlStXKikpKdxLAQAADhH2wrJo0aIf3O9yueTz+eTz+cJ90wAAwKH4LiEAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGC9uNZeAMLjtPHLT7jPE2s0o6+U5Vshf63rJK4KAIDw4AwLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFgv7IVl2rRp+sUvfqGkpCSlpqZq2LBh2r59e8iYUaNGyeVyhVz69+8f7qUAAACHCHthWbt2re699169/fbbKikp0bFjx5SXl6cjR46EjLviiitUWVkZvLz++uvhXgoAAHCIsH/54T/+8Y+Q60VFRUpNTVVpaakuueSS4HaPx6O0tLRw3zwAAHCgiH9bc1VVlSSpS5cuIdvXrFmj1NRUderUSYMGDdKUKVOUmpra6Bx+v19+vz94vbq6WpIUCAQUCASata7645p7vG08sebE+2JMyH+dLpryktW5oikvWe0Ujt+Px/+ubcmcLmNMxH5qxhhdc801OnDggNavXx/cvnjxYnXo0EGZmZmqqKjQxIkTdezYMZWWlsrj8TSYx+fzafLkyQ22FxcXKzExMVLLBwAAYVRTU6PCwkJVVVUpOTm5ScdGtLDce++9Wr58uTZs2KBTTz31hOMqKyuVmZmpRYsWafjw4Q32N3aGJSMjQ/v27Wty4HqBQEAlJSXKzc2V2+1u1hw2yfKtOOE+T4zRH7LrNHFzjPx1rpO4qtYRTXnJ6lzRlJesdirzXd7iOY7/XVtdXa2UlJRmFZaIvSR033336ZVXXtG6det+sKxIUnp6ujIzM1VeXt7ofo/H0+iZF7fb3eKyEY45bOCv/fF/+P46108a5xTRlJeszhVNeclql3D+bqz/XduSOcNeWIwxuu+++7R06VKtWbNGPXr0+NFj9u/fr127dik9PT3cywEAAA4Q9o8133vvvVqwYIGKi4uVlJSkPXv2aM+ePfr2228lSYcPH9aDDz6oTZs26YsvvtCaNWtUUFCglJQUXXvtteFeDgAAcICwn2GZM2eOJCknJydke1FRkUaNGqXY2Fht3bpV8+fP18GDB5Wenq7Bgwdr8eLFSkpKCvdyAACAA0TkJaEfkpCQoBUrTvwGUQAAgOPxXUIAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPUoLAAAwHoUFgAAYD0KCwAAsB6FBQAAWI/CAgAArEdhAQAA1qOwAAAA61FYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6ca29ABudNn55ay8BAAB8D2dYAACA9SgsAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsR2EBAADWo7AAAADrUVgAAID1KCwAAMB6rVpYZs+erR49eqhdu3bq06eP1q9f35rLAQAAlmq1wrJ48WKNGTNGEyZM0AcffKBf/vKXys/P186dO1trSQAAwFKtVlgef/xx3X777brjjjv085//XE8++aQyMjI0Z86c1loSAACwVKt8W/PRo0dVWlqq8ePHh2zPy8vTxo0bG4z3+/3y+/3B61VVVZKkb775RoFAoFlrCAQCqqmp0f79++V2u0P2xR070qw5bRVXZ1RTU6e4QIxq61ytvZyIi6a8ZHWuaMpLVjvt37+/xXMc/7v20KFDkiRjTNMnM63gq6++MpLMP//5z5DtU6ZMMWeddVaD8ZMmTTKSuHDhwoULFy4OuOzatavJ3aFVzrDUc7lC26UxpsE2SXr44Yc1duzY4PW6ujp988036tq1a6Pjf4rq6mplZGRo165dSk5ObtYcbUU0ZZWiKy9ZnSua8pLVuY7Pa4zRoUOH5PV6mzxXqxSWlJQUxcbGas+ePSHb9+7dq27dujUY7/F45PF4QrZ16tQpLGtJTk6Oin80UnRllaIrL1mdK5ryktW5vp+3Y8eOzZqjVd50Gx8frz59+qikpCRke0lJiQYMGNAaSwIAABZrtZeExo4dq1tuuUXZ2dm66KKL9Oyzz2rnzp36zW9+01pLAgAAlmq1wnLjjTdq//79evTRR1VZWamsrCy9/vrryszMPCm37/F4NGnSpAYvNTlRNGWVoisvWZ0rmvKS1bnCmddlTHM+WwQAAHDy8F1CAADAehQWAABgPQoLAACwHoUFAABYj8ICAACsF5WFZfbs2erRo4fatWunPn36aP369a29pLBYt26dCgoK5PV65XK5tGzZspD9xhj5fD55vV4lJCQoJydH27Zta53FttC0adP0i1/8QklJSUpNTdWwYcO0ffv2kDFOyTtnzhydd955wb8UedFFF+mNN94I7ndKzsZMmzZNLpdLY8aMCW5zUl6fzyeXyxVySUtLC+53UlZJ+uqrr3TzzTera9euSkxM1Pnnn6/S0tLgfiflPe200xrcty6XS/fee68kZ2U9duyY/vu//1s9evRQQkKCevbsqUcffVR1dXXBMWHJ2+RvH2rjFi1aZNxut3nuuefMRx99ZO6//37Tvn17s2PHjtZeWou9/vrrZsKECebll182kszSpUtD9k+fPt0kJSWZl19+2WzdutXceOONJj093VRXV7fOglvg8ssvN0VFRaasrMxs2bLFDB061HTv3t0cPnw4OMYpeV955RWzfPlys337drN9+3bzyCOPGLfbbcrKyowxzsl5vHfffdecdtpp5rzzzjP3339/cLuT8k6aNMmce+65prKyMnjZu3dvcL+Tsn7zzTcmMzPTjBo1yrzzzjumoqLCrFq1ynz66afBMU7Ku3fv3pD7taSkxEgyq1evNsY4K+tjjz1munbtal577TVTUVFhXnrpJdOhQwfz5JNPBseEI2/UFZa+ffua3/zmNyHbzj77bDN+/PhWWlFkHF9Y6urqTFpampk+fXpw23fffWc6duxo/vKXv7TCCsNr7969RpJZu3atMcb5eTt37mz++te/OjbnoUOHzJlnnmlKSkrMoEGDgoXFaXknTZpkevfu3eg+p2V96KGHzMUXX3zC/U7Le7z777/fnH766aaurs5xWYcOHWpGjx4dsm348OHm5ptvNsaE776NqpeEjh49qtLSUuXl5YVsz8vL08aNG1tpVSdHRUWF9uzZE5Ld4/Fo0KBBjsheVVUlSerSpYsk5+atra3VokWLdOTIEV100UWOzXnvvfdq6NChuuyyy0K2OzFveXm5vF6vevTooZtuukmff/65JOdlfeWVV5Sdna3rr79eqampuuCCC/Tcc88F9zst7/cdPXpUCxYs0OjRo+VyuRyX9eKLL9abb76pTz75RJL0v//7v9qwYYOuvPJKSeG7b1vtT/O3hn379qm2trbBN0J369atwTdHO019vsay79ixozWWFDbGGI0dO1YXX3yxsrKyJDkv79atW3XRRRfpu+++U4cOHbR06VKdc845wQe7U3JK0qJFi/T+++/rvffea7DPafdrv379NH/+fJ111ln6+uuv9dhjj2nAgAHatm2b47J+/vnnmjNnjsaOHatHHnlE7777rv7zP/9THo9Ht956q+Pyft+yZct08OBBjRo1SpLz/h0/9NBDqqqq0tlnn63Y2FjV1tZqypQpGjFihKTw5Y2qwlLP5XKFXDfGNNjmVE7M/rvf/U4ffvihNmzY0GCfU/L+x3/8h7Zs2aKDBw/q5Zdf1siRI7V27drgfqfk3LVrl+6//36tXLlS7dq1O+E4p+TNz88P/u9evXrpoosu0umnn67nn39e/fv3l+ScrHV1dcrOztbUqVMlSRdccIG2bdumOXPm6NZbbw2Oc0re75s7d67y8/Pl9XpDtjsl6+LFi7VgwQIVFxfr3HPP1ZYtWzRmzBh5vV6NHDkyOK6leaPqJaGUlBTFxsY2OJuyd+/eBs3Paeo/eeC07Pfdd59eeeUVrV69Wqeeempwu9PyxsfH64wzzlB2dramTZum3r1766mnnnJcztLSUu3du1d9+vRRXFyc4uLitHbtWv35z39WXFxcMJNT8h6vffv26tWrl8rLyx1336anp+ucc84J2fbzn/9cO3fulOS8x2y9HTt2aNWqVbrjjjuC25yW9fe//73Gjx+vm266Sb169dItt9yi//qv/9K0adMkhS9vVBWW+Ph49enTRyUlJSHbS0pKNGDAgFZa1cnRo0cPpaWlhWQ/evSo1q5d2yazG2P0u9/9TkuWLNFbb72lHj16hOx3Wt7jGWPk9/sdl3PIkCHaunWrtmzZErxkZ2fr17/+tbZs2aKePXs6Ku/x/H6//vWvfyk9Pd1x9+3AgQMb/OmBTz75RJmZmZKc+5gtKipSamqqhg4dGtzmtKw1NTWKiQmtE7GxscGPNYctb/PfF9w21X+see7cueajjz4yY8aMMe3btzdffPFFay+txQ4dOmQ++OAD88EHHxhJ5vHHHzcffPBB8CPb06dPNx07djRLliwxW7duNSNGjGizH6P77W9/azp27GjWrFkT8tHBmpqa4Bin5H344YfNunXrTEVFhfnwww/NI488YmJiYszKlSuNMc7JeSLf/5SQMc7K+8ADD5g1a9aYzz//3Lz99tvmqquuMklJScHnIydlfffdd01cXJyZMmWKKS8vNwsXLjSJiYlmwYIFwTFOymuMMbW1taZ79+7moYcearDPSVlHjhxpfvaznwU/1rxkyRKTkpJixo0bFxwTjrxRV1iMMebpp582mZmZJj4+3lx44YXBj8K2datXrzaSGlxGjhxpjPn3R8smTZpk0tLSjMfjMZdcconZunVr6y66mRrLKckUFRUFxzgl7+jRo4P/Xk855RQzZMiQYFkxxjk5T+T4wuKkvPV/i8Ltdhuv12uGDx9utm3bFtzvpKzGGPPqq6+arKws4/F4zNlnn22effbZkP1Oy7tixQojyWzfvr3BPidlra6uNvfff7/p3r27adeunenZs6eZMGGC8fv9wTHhyOsyxpjmngYCAAA4GaLqPSwAAKBtorAAAADrUVgAAID1KCwAAMB6FBYAAGA9CgsAALAehQUAAFiPwgIAAKxHYQEAANajsAAAAOtRWAAAgPX+P65KiekyvPgzAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "age3 = pd.read_csv('data/ages_population3.csv')\n", + "age3.hist()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41.989\n", + "16.144705959865934\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(age3['observation'].mean())\n", + "print(age3['observation'].std())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\nThe mean is a little bit higher than 40, because we have a skewed graph.\\n'" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "The mean is a little bit higher than 40, because we have a skewed graph.\n", + "And the data is a little more disperse, so the standard deviation is a little bit higher.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 1.0\n", + "Name: 0.0, dtype: float64\n", + "observation 30.0\n", + "Name: 0.25, dtype: float64\n", + "observation 40.0\n", + "Name: 0.5, dtype: float64\n", + "observation 53.0\n", + "Name: 0.75, dtype: float64\n", + "observation 77.0\n", + "Name: 1.0, dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "print(age3.quantile(0))\n", + "print(age3.quantile(0.25))\n", + "print(age3.quantile(0.5))\n", + "print(age3.quantile(0.75))\n", + "print(age3.quantile(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n\\nSo our range is between 1 and 77.\\nThe mean was 41 and the median is 40, so the data is very balanced.\\n'" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "So our range is between 1 and 77.\n", + "The mean was 41 and the median is 40, so the data is very balanced.\n", + "The the percentil 75 is a little bit more distant from the median than the 25 percentil to the median.\n", + "It means that we have more disperse data on the right side of the graph.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 36.0\n", + "Name: 0.4, dtype: float64\n", + "observation 45.0\n", + "Name: 0.6, dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "print(age3.quantile(0.4))\n", + "print(age3.quantile(0.6))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "As we saw earlier, on the right side we have a more disperse data\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus challenge\n", + "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}