From accbe8be8e117d424a339071898b311978fa8a9a Mon Sep 17 00:00:00 2001 From: leticiademarchiferreira <127995157+leticiademarchiferreira@users.noreply.github.com> Date: Mon, 22 May 2023 17:01:45 +0200 Subject: [PATCH] https://www.bing.com/search?q=ortomedic+marca+de+aparelho+autoligado+brasil&qs=n&form=QBRE&sp=-1&lq=0&pq=ortomedic+marca+de+aparelho+autoligado+brasil&sc=8-45&sk=&cvid=848C0A78910A40818759D2D17916D846&ghsh=0&ghacc=0&ghpl= --- your-code/1.-Data-Cleaning.ipynb | 1328 +++++++++++++++++- your-code/2.-Exploratory-Data-Analysis.ipynb | 792 ++++++++++- your-code/3.-Inferential-Analysis.ipynb | 728 +++++++++- your-code/wnba.csv | 144 ++ your-code/wnba_clean.csv | 144 ++ 5 files changed, 3031 insertions(+), 105 deletions(-) create mode 100644 your-code/wnba.csv create mode 100644 your-code/wnba_clean.csv diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..f6ab44b 100644 --- a/your-code/1.-Data-Cleaning.ipynb +++ b/your-code/1.-Data-Cleaning.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -28,15 +29,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "import pandas as pd\n", "pd.set_option('display.max_columns', 100)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -47,14 +53,286 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF18371.021.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F18573.021.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG17069.023.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F18584.024.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING17578.025.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \n", + "0 Aerial Powers DAL F 183 71.0 21.200991 US \\\n", + "1 Alana Beard LA G/F 185 73.0 21.329438 US \n", + "2 Alex Bentley CON G 170 69.0 23.875433 US \n", + "3 Alex Montgomery SAN G/F 185 84.0 24.543462 US \n", + "4 Alexis Jones MIN G 175 78.0 25.469388 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \\\n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "2 October 27, 1990 26 Penn State 4 26 617 82 \n", + "3 December 11, 1988 28 Georgia Tech 6 31 721 75 \n", + "4 August 5, 1994 23 Baylor R 24 137 16 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \\\n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "2 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 22 3 \n", + "3 195 38.5 21 68 30.9 17 21 81.0 35 134 169 65 20 10 \n", + "4 50 32.0 7 20 35.0 11 12 91.7 3 9 12 12 7 0 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 \n", + "3 38 188 2 0 \n", + "4 14 50 0 0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba = pd.read_csv(\"wnba.csv\") \n", + "wnba.head()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -64,14 +342,40 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns with NaN values:\n", + "Weight 1\n", + "BMI 1\n", + "dtype: int64\n", + "\n", + "Number of rows with NaN values in each column:\n", + "Weight 1\n", + "BMI 1\n", + "dtype: int64\n" + ] + } + ], "source": [ - "#your code here" + "nan_values = wnba.isna().sum()\n", + "\n", + "columns_with_nan = nan_values[nan_values > 0]\n", + "\n", + "rows_with_nan = wnba[columns_with_nan.index].isna().sum()\n", + "\n", + "print(\"Columns with NaN values:\")\n", + "print(columns_with_nan)\n", + "print(\"\\nNumber of rows with NaN values in each column:\")\n", + "print(rows_with_nan)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -80,14 +384,31 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Team Pos Height Weight BMI Birth_Place Birthdate Age \n", + "91 Makayla Epps CHI G 178 NaN NaN US June 6, 1995 22 \\\n", + "\n", + " College Experience Games Played MIN FGM FGA FG% 3PM 3PA 3P% \n", + "91 Kentucky R 14 52 2 14 14.3 0 5 0.0 \\\n", + "\n", + " FTM FTA FT% OREB DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "91 2 5 40.0 2 0 2 4 1 0 4 6 0 0 \n" + ] + } + ], "source": [ - "#your code here" + "rows_with_nan = wnba[wnba['Weight'].isna() | wnba['BMI'].isna()]\n", + "print(rows_with_nan)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -96,14 +417,26 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of values to be removed: 0.04%\n" + ] + } + ], "source": [ - "#your code here" + "total_values = wnba.size\n", + "nan_count = wnba.isna().sum().sum()\n", + "percentage_removed = (nan_count / total_values) * 100\n", + "print(\"Percentage of values to be removed: {:.2f}%\".format(percentage_removed))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -114,14 +447,56 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name 0\n", + "Team 0\n", + "Pos 0\n", + "Height 0\n", + "Weight 0\n", + "BMI 0\n", + "Birth_Place 0\n", + "Birthdate 0\n", + "Age 0\n", + "College 0\n", + "Experience 0\n", + "Games Played 0\n", + "MIN 0\n", + "FGM 0\n", + "FGA 0\n", + "FG% 0\n", + "3PM 0\n", + "3PA 0\n", + "3P% 0\n", + "FTM 0\n", + "FTA 0\n", + "FT% 0\n", + "OREB 0\n", + "DREB 0\n", + "REB 0\n", + "AST 0\n", + "STL 0\n", + "BLK 0\n", + "TO 0\n", + "PTS 0\n", + "DD2 0\n", + "TD3 0\n", + "dtype: int64\n" + ] + } + ], "source": [ - "#your code here" + "wnba_cleaned = wnba.dropna()\n", + "print(wnba_cleaned.isna().sum())" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -130,14 +505,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#N pq a porcentagem é muito baixa." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -147,14 +523,58 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name object\n", + "Team object\n", + "Pos object\n", + "Height int64\n", + "Weight float64\n", + "BMI float64\n", + "Birth_Place object\n", + "Birthdate object\n", + "Age int64\n", + "College object\n", + "Experience object\n", + "Games Played int64\n", + "MIN int64\n", + "FGM int64\n", + "FGA int64\n", + "FG% float64\n", + "3PM int64\n", + "3PA int64\n", + "3P% float64\n", + "FTM int64\n", + "FTA int64\n", + "FT% float64\n", + "OREB int64\n", + "DREB int64\n", + "REB int64\n", + "AST int64\n", + "STL int64\n", + "BLK int64\n", + "TO int64\n", + "PTS int64\n", + "DD2 int64\n", + "TD3 int64\n", + "dtype: object" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.dtypes" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -162,6 +582,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -170,14 +591,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "wnba['Weight'] = wnba['Weight'].fillna(0).astype(int)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -186,14 +608,349 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
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mean184.56643478.42657323.09121427.07692324.356643496.97202873.895105167.62237842.90139914.72727343.42657324.80349739.27272749.11188875.57832221.92307761.16083983.08391644.23076917.6083929.71328732.090909201.7902101.1328670.006993
std8.68506812.7938642.0736913.6791707.104259290.77732056.110895117.46709510.11149817.35591946.10619918.51218336.74774744.24485418.71219421.50927649.76191968.30219741.48301713.43897812.52019321.502017153.3815482.9003100.083624
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25%176.50000071.00000021.78587624.00000022.000000240.00000026.00000066.00000036.9500000.0000003.0000000.00000012.00000016.50000071.1500007.00000025.50000034.00000011.0000007.0000002.00000013.50000075.0000000.0000000.000000
50%185.00000079.00000022.87331427.00000027.000000504.00000069.000000152.00000042.00000010.00000032.00000030.30000029.00000035.00000080.00000013.00000050.00000062.00000033.00000015.0000005.00000028.000000177.0000000.0000000.000000
75%191.00000086.00000024.18071530.00000029.000000750.000000105.000000244.50000048.55000022.00000065.00000036.15000052.50000066.00000085.85000031.00000084.000000116.00000066.50000027.00000012.00000048.000000277.5000001.0000000.000000
max206.000000113.00000031.55588036.00000032.0000001018.000000227.000000509.000000100.00000088.000000225.000000100.000000168.000000186.000000100.000000113.000000226.000000334.000000206.00000063.00000064.00000087.000000584.00000017.0000001.000000
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
...................................................................................................
138Tiffany HayesATLG1787022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.52889117693785046700
139Tiffany JacksonLAF1918423.025685USApril 26, 198532Texas922127122548.0010.04666.75182331382800
140Tiffany MitchellINDG1756922.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.2167086393154027700
141Tina CharlesNYF/C1938422.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.55621226875212271582110
142Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \n", + "0 Aerial Powers DAL F 183 71 21.200991 US \\\n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "2 Alex Bentley CON G 170 69 23.875433 US \n", + "3 Alex Montgomery SAN G/F 185 84 24.543462 US \n", + "4 Alexis Jones MIN G 175 78 25.469388 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \\\n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "2 October 27, 1990 26 Penn State 4 26 617 82 \n", + "3 December 11, 1988 28 Georgia Tech 6 31 721 75 \n", + "4 August 5, 1994 23 Baylor R 24 137 16 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \\\n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "2 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 22 3 \n", + "3 195 38.5 21 68 30.9 17 21 81.0 35 134 169 65 20 10 \n", + "4 50 32.0 7 20 35.0 11 12 91.7 3 9 12 12 7 0 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 \n", + "3 38 188 2 0 \n", + "4 14 50 0 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba = pd.read_csv('wnba_clean.csv')\n", + "wnba.head()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -52,14 +326,349 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count143.000000143.000000142.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000
mean184.56643478.42657323.09121427.07692324.356643496.97202873.895105167.62237842.90139914.72727343.42657324.80349739.27272749.11188875.57832221.92307761.16083983.08391644.23076917.6083929.71328732.090909201.7902101.1328670.006993
std8.68506812.7938642.0736913.6791707.104259290.77732056.110895117.46709510.11149817.35591946.10619918.51218336.74774744.24485418.71219421.50927649.76191968.30219741.48301713.43897812.52019321.502017153.3815482.9003100.083624
min165.0000000.00000018.39067521.0000002.00000012.0000001.0000003.00000014.3000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%176.50000071.00000021.78587624.00000022.000000240.00000026.00000066.00000036.9500000.0000003.0000000.00000012.00000016.50000071.1500007.00000025.50000034.00000011.0000007.0000002.00000013.50000075.0000000.0000000.000000
50%185.00000079.00000022.87331427.00000027.000000504.00000069.000000152.00000042.00000010.00000032.00000030.30000029.00000035.00000080.00000013.00000050.00000062.00000033.00000015.0000005.00000028.000000177.0000000.0000000.000000
75%191.00000086.00000024.18071530.00000029.000000750.000000105.000000244.50000048.55000022.00000065.00000036.15000052.50000066.00000085.85000031.00000084.000000116.00000066.50000027.00000012.00000048.000000277.5000001.0000000.000000
max206.000000113.00000031.55588036.00000032.0000001018.000000227.000000509.000000100.00000088.000000225.000000100.000000168.000000186.000000100.000000113.000000226.000000334.000000206.00000063.00000064.00000087.000000584.00000017.0000001.000000
\n", + "
" + ], + "text/plain": [ + " Height Weight BMI Age Games Played \n", + "count 143.000000 143.000000 142.000000 143.000000 143.000000 \\\n", + "mean 184.566434 78.426573 23.091214 27.076923 24.356643 \n", + "std 8.685068 12.793864 2.073691 3.679170 7.104259 \n", + "min 165.000000 0.000000 18.390675 21.000000 2.000000 \n", + "25% 176.500000 71.000000 21.785876 24.000000 22.000000 \n", + "50% 185.000000 79.000000 22.873314 27.000000 27.000000 \n", + "75% 191.000000 86.000000 24.180715 30.000000 29.000000 \n", + "max 206.000000 113.000000 31.555880 36.000000 32.000000 \n", + "\n", + " MIN FGM FGA FG% 3PM \n", + "count 143.000000 143.000000 143.000000 143.000000 143.000000 \\\n", + "mean 496.972028 73.895105 167.622378 42.901399 14.727273 \n", + "std 290.777320 56.110895 117.467095 10.111498 17.355919 \n", + "min 12.000000 1.000000 3.000000 14.300000 0.000000 \n", + "25% 240.000000 26.000000 66.000000 36.950000 0.000000 \n", + "50% 504.000000 69.000000 152.000000 42.000000 10.000000 \n", + "75% 750.000000 105.000000 244.500000 48.550000 22.000000 \n", + "max 1018.000000 227.000000 509.000000 100.000000 88.000000 \n", + "\n", + " 3PA 3P% FTM FTA FT% OREB \n", + "count 143.000000 143.000000 143.000000 143.000000 143.000000 143.000000 \\\n", + "mean 43.426573 24.803497 39.272727 49.111888 75.578322 21.923077 \n", + "std 46.106199 18.512183 36.747747 44.244854 18.712194 21.509276 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 3.000000 0.000000 12.000000 16.500000 71.150000 7.000000 \n", + "50% 32.000000 30.300000 29.000000 35.000000 80.000000 13.000000 \n", + "75% 65.000000 36.150000 52.500000 66.000000 85.850000 31.000000 \n", + "max 225.000000 100.000000 168.000000 186.000000 100.000000 113.000000 \n", + "\n", + " DREB REB AST STL BLK TO \n", + "count 143.000000 143.000000 143.000000 143.000000 143.000000 143.000000 \\\n", + "mean 61.160839 83.083916 44.230769 17.608392 9.713287 32.090909 \n", + "std 49.761919 68.302197 41.483017 13.438978 12.520193 21.502017 \n", + "min 0.000000 2.000000 0.000000 0.000000 0.000000 2.000000 \n", + "25% 25.500000 34.000000 11.000000 7.000000 2.000000 13.500000 \n", + "50% 50.000000 62.000000 33.000000 15.000000 5.000000 28.000000 \n", + "75% 84.000000 116.000000 66.500000 27.000000 12.000000 48.000000 \n", + "max 226.000000 334.000000 206.000000 63.000000 64.000000 87.000000 \n", + "\n", + " PTS DD2 TD3 \n", + "count 143.000000 143.000000 143.000000 \n", + "mean 201.790210 1.132867 0.006993 \n", + "std 153.381548 2.900310 0.083624 \n", + "min 2.000000 0.000000 0.000000 \n", + "25% 75.000000 0.000000 0.000000 \n", + "50% 177.000000 0.000000 0.000000 \n", + "75% 277.500000 1.000000 0.000000 \n", + "max 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.describe()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -72,12 +681,44 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Players with highest ages:\n", + " Name Age\n", + "127 Sue Bird 36\n", + "67 Jia Perkins 35\n", + "110 Rebekkah Brunson 35\n", + "106 Plenette Pierson 35\n", + "1 Alana Beard 35\n", + "Players with highest weights:\n", + " Name Weight\n", + "36 Courtney Paris 113\n", + "12 Amanda Zahui B. 113\n", + "41 Danielle Adams 108\n", + "23 Brionna Jones 104\n", + "89 Lynetta Kizer 104\n" + ] + } + ], "source": [ - "#your code here" + "age_sorted = wnba.sort_values('Age', ascending=False)\n", + "\n", + "highest_ages = age_sorted.head()\n", + "print(\"Players with highest ages:\")\n", + "print(highest_ages[['Name', 'Age']])\n", + "\n", + "weight_sorted = wnba.sort_values('Weight', ascending=False)\n", + "\n", + "highest_weights = weight_sorted.head()\n", + "print(\"Players with highest weights:\")\n", + "print(highest_weights[['Name', 'Weight']])" ] }, { 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(wnba['Height'], bins=20, edgecolor='black')\n", + "plt.xlabel('Height')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Distribution of Height')\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(wnba['Weight'], bins=20, edgecolor='black')\n", + "plt.xlabel('Weight')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Distribution of Weight')\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(wnba['Age'], bins=20, edgecolor='black')\n", + "plt.xlabel('Age')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Distribution of Age')\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(wnba['BMI'], bins=20, edgecolor='black')\n", + "plt.xlabel('BMI')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Distribution of BMI')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -109,10 +791,11 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#Altura, peso, imc e idade, ajudam a entender as variaveis caracteristicas das jogadoras..." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -124,6 +807,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -134,14 +818,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "columns = ['REB', 'AST', 'STL', 'PTS', 'BLK']\n", + "\n", + "# Plotar as distribuições\n", + "fig, axs = plt.subplots(1, len(columns), figsize=(15, 4))\n", + "\n", + "for i, col in enumerate(columns):\n", + " axs[i].hist(wnba[col], bins=20)\n", + " axs[i].set_title(col)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -150,14 +856,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# mostra a variacao do desempenho das jogadoras\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -165,6 +872,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -173,14 +881,55 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "wnba['REB_PER_MIN'] = wnba['REB'] / wnba['MIN']\n", + "wnba['AST_PER_MIN'] = wnba['AST'] / wnba['MIN']\n", + "wnba['STL_PER_MIN'] = wnba['STL'] / wnba['MIN']\n", + "wnba['PTS_PER_MIN'] = wnba['PTS'] / wnba['MIN']\n", + "wnba['BLK_PER_MIN'] = wnba['BLK'] / wnba['MIN']\n", + "\n", + "# Plot the distributions\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(2, 3, 1)\n", + "plt.hist(wnba['REB_PER_MIN'], bins=30)\n", + "plt.xlabel('REB_PER_MIN')\n", + "plt.ylabel('Frequency')\n", + "plt.subplot(2, 3, 2)\n", + "plt.hist(wnba['AST_PER_MIN'], bins=30)\n", + "plt.xlabel('AST_PER_MIN')\n", + "plt.ylabel('Frequency')\n", + "plt.subplot(2, 3, 3)\n", + "plt.hist(wnba['STL_PER_MIN'], bins=30)\n", + "plt.xlabel('STL_PER_MIN')\n", + "plt.ylabel('Frequency')\n", + "plt.subplot(2, 3, 4)\n", + "plt.hist(wnba['PTS_PER_MIN'], bins=30)\n", + "plt.xlabel('PTS_PER_MIN')\n", + "plt.ylabel('Frequency')\n", + "plt.subplot(2, 3, 5)\n", + "plt.hist(wnba['BLK_PER_MIN'], bins=30)\n", + "plt.xlabel('BLK_PER_MIN')\n", + "plt.ylabel('Frequency')\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -197,6 +946,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -242,7 +992,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..02c2f61 100644 --- a/your-code/3.-Inferential-Analysis.ipynb +++ b/your-code/3.-Inferential-Analysis.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -21,12 +22,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Libraries\n", "import math\n", + "import statistics as st\n", "import pandas as pd\n", "import numpy as np\n", "from scipy import stats\n", @@ -36,6 +38,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -46,14 +49,523 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" - ] - }, - { + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
...................................................................................................
138Tiffany HayesATLG1787022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.52889117693785046700
139Tiffany JacksonLAF1918423.025685USApril 26, 198532Texas922127122548.0010.04666.75182331382800
140Tiffany MitchellINDG1756922.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.2167086393154027700
141Tina CharlesNYF/C1938422.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.55621226875212271582110
142Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
\n", + "

143 rows × 32 columns

\n", + "
" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \n", + "0 Aerial Powers DAL F 183 71 21.200991 US \\\n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "2 Alex Bentley CON G 170 69 23.875433 US \n", + "3 Alex Montgomery SAN G/F 185 84 24.543462 US \n", + "4 Alexis Jones MIN G 175 78 25.469388 US \n", + ".. ... ... ... ... ... ... ... \n", + "138 Tiffany Hayes ATL G 178 70 22.093170 US \n", + "139 Tiffany Jackson LA F 191 84 23.025685 US \n", + "140 Tiffany Mitchell IND G 175 69 22.530612 US \n", + "141 Tina Charles NY F/C 193 84 22.550941 US \n", + "142 Yvonne Turner PHO G 175 59 19.265306 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN \n", + "0 January 17, 1994 23 Michigan State 2 8 173 \\\n", + "1 May 14, 1982 35 Duke 12 30 947 \n", + "2 October 27, 1990 26 Penn State 4 26 617 \n", + "3 December 11, 1988 28 Georgia Tech 6 31 721 \n", + "4 August 5, 1994 23 Baylor R 24 137 \n", + ".. ... ... ... ... ... ... \n", + "138 September 20, 1989 27 Connecticut 6 29 861 \n", + "139 April 26, 1985 32 Texas 9 22 127 \n", + "140 September 23, 1984 32 South Carolina 2 27 671 \n", + "141 May 12, 1988 29 Connecticut 8 29 952 \n", + "142 October 13, 1987 29 Nebraska 2 30 356 \n", + "\n", + " FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST \n", + "0 30 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 \\\n", + "1 90 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 \n", + "2 82 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 \n", + "3 75 195 38.5 21 68 30.9 17 21 81.0 35 134 169 65 \n", + "4 16 50 32.0 7 20 35.0 11 12 91.7 3 9 12 12 \n", + ".. ... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "138 144 331 43.5 43 112 38.4 136 161 84.5 28 89 117 69 \n", + "139 12 25 48.0 0 1 0.0 4 6 66.7 5 18 23 3 \n", + "140 83 238 34.9 17 69 24.6 94 102 92.2 16 70 86 39 \n", + "141 227 509 44.6 18 56 32.1 110 135 81.5 56 212 268 75 \n", + "142 59 140 42.1 11 47 23.4 22 28 78.6 11 13 24 30 \n", + "\n", + " STL BLK TO PTS DD2 TD3 \n", + "0 3 6 12 93 0 0 \n", + "1 63 13 40 217 0 0 \n", + "2 22 3 24 218 0 0 \n", + "3 20 10 38 188 2 0 \n", + "4 7 0 14 50 0 0 \n", + ".. ... ... .. ... ... ... \n", + "138 37 8 50 467 0 0 \n", + "139 1 3 8 28 0 0 \n", + "140 31 5 40 277 0 0 \n", + "141 21 22 71 582 11 0 \n", + "142 18 1 32 151 0 0 \n", + "\n", + "[143 rows x 32 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba = pd.read_csv('wnba_clean.csv')\n", + "wnba" + ] + }, + { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -70,14 +582,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "sample_mean_weight = wnba['Weight'].mean()\n", + "\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -86,14 +600,33 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample Mean Weight: 78.42657342657343\n", + "Confidence Interval: (76.31162952730683, 80.54151732584002)\n" + ] + } + ], + "source": [ + "confidence_level = 0.95\n", + "\n", + "standard_error = wnba['Weight'].std() / np.sqrt(len(wnba))\n", + "\n", + "margin_of_error = stats.t.ppf((1 + confidence_level) / 2, df=len(wnba)-1) * standard_error\n", + "\n", + "confidence_interval = (sample_mean_weight - margin_of_error, sample_mean_weight + margin_of_error)\n", + "\n", + "print(\"Sample Mean Weight:\", sample_mean_weight)\n", + "print(\"Confidence Interval:\", confidence_interval)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -102,14 +635,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#Sample Mean Weight: 78.42657342657343\n", + "#Confidence Interval: (76.31162952730683, 80.54151732584002)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -118,14 +653,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#67 kg está um peso abaixo da media... e do intervalo de confianca...." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -134,14 +670,50 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_mean = 78.42657342657343\n", + "sample_std = 10.75181363846344\n", + "sample_size = 100\n", + "confidence_level = 0.95\n", + "\n", + "alpha = 1 - confidence_level\n", + "critical_value = stats.norm.ppf(1 - alpha/2)\n", + "\n", + "x = np.linspace(sample_mean - 4*sample_std/np.sqrt(sample_size), sample_mean + 4*sample_std/np.sqrt(sample_size), 100)\n", + "\n", + "distribution = stats.norm.pdf(x, loc=sample_mean, scale=sample_std/np.sqrt(sample_size))\n", + "\n", + "plt.plot(x, distribution, label='Distribution')\n", + "\n", + "x_critical = np.linspace(sample_mean - critical_value*sample_std/np.sqrt(sample_size), sample_mean + critical_value*sample_std/np.sqrt(sample_size), 100)\n", + "y_critical = stats.norm.pdf(x_critical, loc=sample_mean, scale=sample_std/np.sqrt(sample_size))\n", + "plt.fill_between(x_critical, y_critical, color='red', alpha=0.3, label='Critical Region')\n", + "\n", + "plt.xlabel('Weight')\n", + "plt.ylabel('Probability Density')\n", + "plt.title('Probability Distribution of Average Weight')\n", + "\n", + "plt.legend()\n", + "\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -154,14 +726,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# your answer here" - ] + "source": [] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -170,14 +741,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# your code here" - ] + "source": [] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -186,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -194,6 +764,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -205,11 +776,10 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "#your code here" - ] + "source": [] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -217,6 +787,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -227,14 +798,46 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for +: 'int' and 'ellipsis'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[43], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mscipy\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mstats\u001b[39;00m \u001b[39mimport\u001b[39;00m ttest_1samp\n\u001b[1;32m----> 4\u001b[0m t_statistic, p_value \u001b[39m=\u001b[39m ttest_1samp(wnba_assists, combined_assists_mean)\n\u001b[0;32m 6\u001b[0m alpha \u001b[39m=\u001b[39m \u001b[39m0.05\u001b[39m\n\u001b[0;32m 8\u001b[0m \u001b[39mif\u001b[39;00m p_value \u001b[39m<\u001b[39m alpha:\n", + "File \u001b[1;32mc:\\Users\\Leticia Demarchi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:502\u001b[0m, in \u001b[0;36m_axis_nan_policy_factory..axis_nan_policy_decorator..axis_nan_policy_wrapper\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 500\u001b[0m \u001b[39mif\u001b[39;00m sentinel:\n\u001b[0;32m 501\u001b[0m samples \u001b[39m=\u001b[39m _remove_sentinel(samples, paired, sentinel)\n\u001b[1;32m--> 502\u001b[0m res \u001b[39m=\u001b[39m hypotest_fun_out(\u001b[39m*\u001b[39;49msamples, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m 503\u001b[0m res \u001b[39m=\u001b[39m result_to_tuple(res)\n\u001b[0;32m 504\u001b[0m res \u001b[39m=\u001b[39m _add_reduced_axes(res, reduced_axes, keepdims)\n", + "File \u001b[1;32mc:\\Users\\Leticia Demarchi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\scipy\\stats\\_stats_py.py:6263\u001b[0m, in \u001b[0;36mttest_1samp\u001b[1;34m(a, popmean, axis, nan_policy, alternative)\u001b[0m\n\u001b[0;32m 6260\u001b[0m n \u001b[39m=\u001b[39m a\u001b[39m.\u001b[39mshape[axis]\n\u001b[0;32m 6261\u001b[0m df \u001b[39m=\u001b[39m n \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m-> 6263\u001b[0m mean \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mmean(a, axis)\n\u001b[0;32m 6264\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 6265\u001b[0m popmean \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msqueeze(popmean, axis\u001b[39m=\u001b[39maxis)\n", + "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mmean\u001b[1;34m(*args, **kwargs)\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\core\\fromnumeric.py:3464\u001b[0m, in \u001b[0;36mmean\u001b[1;34m(a, axis, dtype, out, keepdims, where)\u001b[0m\n\u001b[0;32m 3461\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 3462\u001b[0m \u001b[39mreturn\u001b[39;00m mean(axis\u001b[39m=\u001b[39maxis, dtype\u001b[39m=\u001b[39mdtype, out\u001b[39m=\u001b[39mout, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m-> 3464\u001b[0m \u001b[39mreturn\u001b[39;00m _methods\u001b[39m.\u001b[39;49m_mean(a, axis\u001b[39m=\u001b[39;49maxis, dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[0;32m 3465\u001b[0m out\u001b[39m=\u001b[39;49mout, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\core\\_methods.py:181\u001b[0m, in \u001b[0;36m_mean\u001b[1;34m(a, axis, dtype, out, keepdims, where)\u001b[0m\n\u001b[0;32m 178\u001b[0m dtype \u001b[39m=\u001b[39m mu\u001b[39m.\u001b[39mdtype(\u001b[39m'\u001b[39m\u001b[39mf4\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 179\u001b[0m is_float16_result \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m--> 181\u001b[0m ret \u001b[39m=\u001b[39m umr_sum(arr, axis, dtype, out, keepdims, where\u001b[39m=\u001b[39;49mwhere)\n\u001b[0;32m 182\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(ret, mu\u001b[39m.\u001b[39mndarray):\n\u001b[0;32m 183\u001b[0m \u001b[39mwith\u001b[39;00m _no_nep50_warning():\n", + "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'ellipsis'" + ] + } + ], "source": [ - "#your-answer-here" + "import numpy as np\n", + "from scipy.stats import ttest_1samp\n", + "\n", + "t_statistic, p_value = ttest_1samp(wnba_assists, combined_assists_mean)\n", + "\n", + "alpha = 0.05\n", + "\n", + "if p_value < alpha:\n", + " print(\"The average number of assists for WNBA players is significantly higher than the combined average.\")\n", + "else:\n", + " print(\"There is not enough evidence to conclude that the average number of assists for WNBA players is higher.\")\n", + "\n", + "print(\"t-statistic:\", t_statistic)\n", + "print(\"p-value:\", p_value)\n", + "\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -243,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -252,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -260,6 +863,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -268,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -276,6 +880,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -292,6 +897,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -308,16 +914,31 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" - ] + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'<' not supported between instances of 'ellipsis' and 'int'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[42], line 8\u001b[0m\n\u001b[0;32m 5\u001b[0m wnba_weights \u001b[39m=\u001b[39m [\u001b[39m60\u001b[39m, \u001b[39m65\u001b[39m, \u001b[39m70\u001b[39m, \u001b[39m75\u001b[39m, \u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m] \u001b[39m# Replace with actual WNBA player weights\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[39m# Perform Kolmogorov-Smirnov test\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m statistic, p_value \u001b[39m=\u001b[39m kstest(wnba_weights, norm\u001b[39m.\u001b[39;49mcdf)\n\u001b[0;32m 10\u001b[0m \u001b[39m# Define significance level\u001b[39;00m\n\u001b[0;32m 11\u001b[0m alpha \u001b[39m=\u001b[39m \u001b[39m0.05\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\Leticia Demarchi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\scipy\\_lib\\_util.py:700\u001b[0m, in \u001b[0;36m_rename_parameter..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 698\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(message)\n\u001b[0;32m 699\u001b[0m kwargs[new_name] \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(old_name)\n\u001b[1;32m--> 700\u001b[0m \u001b[39mreturn\u001b[39;00m fun(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Leticia Demarchi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\scipy\\stats\\_stats_py.py:8376\u001b[0m, in \u001b[0;36mkstest\u001b[1;34m(rvs, cdf, args, N, alternative, method)\u001b[0m\n\u001b[0;32m 8374\u001b[0m \u001b[39mif\u001b[39;00m alternative \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mtwo-sided\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mgreater\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mless\u001b[39m\u001b[39m'\u001b[39m]:\n\u001b[0;32m 8375\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mUnexpected alternative \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m alternative)\n\u001b[1;32m-> 8376\u001b[0m xvals, yvals, cdf \u001b[39m=\u001b[39m _parse_kstest_args(rvs, cdf, args, N)\n\u001b[0;32m 8377\u001b[0m \u001b[39mif\u001b[39;00m cdf:\n\u001b[0;32m 8378\u001b[0m \u001b[39mreturn\u001b[39;00m ks_1samp(xvals, cdf, args\u001b[39m=\u001b[39margs, alternative\u001b[39m=\u001b[39malternative,\n\u001b[0;32m 8379\u001b[0m method\u001b[39m=\u001b[39mmethod)\n", + "File \u001b[1;32mc:\\Users\\Leticia Demarchi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\scipy\\stats\\_stats_py.py:8202\u001b[0m, in \u001b[0;36m_parse_kstest_args\u001b[1;34m(data1, data2, args, N)\u001b[0m\n\u001b[0;32m 8199\u001b[0m cdf \u001b[39m=\u001b[39m data2\n\u001b[0;32m 8200\u001b[0m data2 \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m-> 8202\u001b[0m data1 \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49msort(rvsfunc(\u001b[39m*\u001b[39;49margs, size\u001b[39m=\u001b[39;49mN) \u001b[39mif\u001b[39;49;00m rvsfunc \u001b[39melse\u001b[39;49;00m data1)\n\u001b[0;32m 8203\u001b[0m \u001b[39mreturn\u001b[39;00m data1, data2, cdf\n", + "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36msort\u001b[1;34m(*args, **kwargs)\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\core\\fromnumeric.py:1030\u001b[0m, in \u001b[0;36msort\u001b[1;34m(a, axis, kind, order)\u001b[0m\n\u001b[0;32m 1028\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 1029\u001b[0m a \u001b[39m=\u001b[39m asanyarray(a)\u001b[39m.\u001b[39mcopy(order\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mK\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m-> 1030\u001b[0m a\u001b[39m.\u001b[39;49msort(axis\u001b[39m=\u001b[39;49maxis, kind\u001b[39m=\u001b[39;49mkind, order\u001b[39m=\u001b[39;49morder)\n\u001b[0;32m 1031\u001b[0m \u001b[39mreturn\u001b[39;00m a\n", + "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'ellipsis' and 'int'" + ] + } + ], + "source": [] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -325,6 +946,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -333,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -357,7 +979,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/your-code/wnba.csv b/your-code/wnba.csv new file mode 100644 index 0000000..bb13374 --- /dev/null +++ b/your-code/wnba.csv @@ -0,0 +1,144 @@ +Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 +Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 +Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 +Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 +Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 +Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 +Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100,3,13,16,11,5,0,11,26,0,0 +Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100,1,14,15,5,4,3,3,24,0,0 +Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 +Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 +Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100,3,7,10,10,5,0,2,36,0,0 +Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 +Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 +Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 +Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 +Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 +Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100,7,7,100,16,42,58,8,1,11,16,58,0,0 +Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 +Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 +Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 +Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 +Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 +Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 +Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 +Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 +Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 +Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 +Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 +Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 +Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 +Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 +Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 +Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 +Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 +Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 +Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 +Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100,0,0,0.0,3,7,10,7,1,1,5,17,0,0 +Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 +Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 +Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 +Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 +Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 +Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100,6,4,10,4,4,4,7,49,0,0 +Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 +Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 +Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 +Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 +Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 +Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 +Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 +Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 +Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 +Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 +Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 +Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 +Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 +Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 +Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 +Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 +Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 +Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 +Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 +Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 +Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 +Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 +Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 +Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 +Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 +Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 +Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 +Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 +Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 +Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 +Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 +Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100,4,10,14,6,1,1,13,65,0,0 +Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 +Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 +Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 +Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 +Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 +Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 +Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 +Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 +Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 +Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 +Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 +Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 +Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 +Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 +Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 +Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 +Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 +Makayla Epps,CHI,G,178,,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 +Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 +Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 +Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 +Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 +Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 +Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 +Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 +Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 +Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 +Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 +Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 +Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 +Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 +Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 +Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 +Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 +Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 +Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 +Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 +Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 +Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 +Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 +Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 +Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 +Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 +Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 +Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 +Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 +Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 +Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 +Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 +Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 +Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 +Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 +Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 +Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 +Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 +Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 +Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 +Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 +Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 +Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 +Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 +Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 +Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 +Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 +Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 +Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 +Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 +Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 +Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0 \ No newline at end of file diff --git a/your-code/wnba_clean.csv b/your-code/wnba_clean.csv new file mode 100644 index 0000000..7442ec4 --- /dev/null +++ b/your-code/wnba_clean.csv @@ -0,0 +1,144 @@ +Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 +Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 +Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 +Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 +Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 +Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 +Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100.0,3,13,16,11,5,0,11,26,0,0 +Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100.0,1,14,15,5,4,3,3,24,0,0 +Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 +Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 +Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100.0,3,7,10,10,5,0,2,36,0,0 +Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 +Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 +Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 +Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 +Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100.0,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 +Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100.0,7,7,100.0,16,42,58,8,1,11,16,58,0,0 +Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 +Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 +Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 +Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 +Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 +Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 +Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 +Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 +Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 +Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 +Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 +Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 +Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 +Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 +Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 +Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 +Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 +Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 +Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 +Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100.0,0,0,0.0,3,7,10,7,1,1,5,17,0,0 +Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 +Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 +Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 +Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 +Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 +Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100.0,6,4,10,4,4,4,7,49,0,0 +Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 +Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 +Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 +Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 +Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 +Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 +Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 +Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 +Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 +Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 +Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 +Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 +Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 +Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 +Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 +Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 +Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 +Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 +Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 +Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 +Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 +Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 +Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 +Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 +Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 +Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 +Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 +Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 +Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 +Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 +Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 +Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100.0,4,10,14,6,1,1,13,65,0,0 +Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 +Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 +Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 +Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 +Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 +Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 +Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 +Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 +Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 +Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 +Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 +Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 +Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 +Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 +Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 +Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 +Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 +Makayla Epps,CHI,G,178,0,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 +Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 +Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 +Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 +Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 +Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 +Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 +Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 +Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 +Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 +Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 +Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 +Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 +Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 +Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 +Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 +Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 +Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 +Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 +Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 +Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 +Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 +Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 +Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 +Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 +Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 +Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 +Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 +Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 +Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 +Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 +Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 +Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 +Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 +Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 +Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 +Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 +Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 +Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 +Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 +Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 +Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 +Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 +Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 +Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 +Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 +Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 +Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 +Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 +Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 +Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 +Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0