From 6ed675680da076266c28aff45a047eacab189636 Mon Sep 17 00:00:00 2001 From: rafaelmello82 <64922281+rafaelmello82@users.noreply.github.com> Date: Sun, 2 Aug 2020 09:07:04 +0100 Subject: [PATCH] lab_ finished_Rafael_mello --- {data => your-code}/ages_population.csv | 0 {data => your-code}/ages_population2.csv | 0 {data => your-code}/ages_population3.csv | 0 your-code/main.ipynb | 1654 ++++++++++++++++- {data => your-code}/roll_the_dice_hundred.csv | 0 .../roll_the_dice_thousand.csv | 0 6 files changed, 1567 insertions(+), 87 deletions(-) rename {data => your-code}/ages_population.csv (100%) rename {data => your-code}/ages_population2.csv (100%) rename {data => your-code}/ages_population3.csv (100%) rename {data => your-code}/roll_the_dice_hundred.csv (100%) rename {data => your-code}/roll_the_dice_thousand.csv (100%) diff --git a/data/ages_population.csv b/your-code/ages_population.csv similarity index 100% rename from data/ages_population.csv rename to your-code/ages_population.csv diff --git a/data/ages_population2.csv b/your-code/ages_population2.csv similarity index 100% rename from data/ages_population2.csv rename to your-code/ages_population2.csv diff --git a/data/ages_population3.csv b/your-code/ages_population3.csv similarity index 100% rename from data/ages_population3.csv rename to your-code/ages_population3.csv diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a0a5b66..2d05c94 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# Libraries" + "# Libraries\n", + "import pandas as pd \n", + "import random\n", + "import matplotlib.pyplot as plt \n", + "import numpy as np" ] }, { @@ -29,11 +33,120 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "result_list=[] \n", + "\n", + "def dice (x):\n", + " for i in range(x):\n", + " result = random.randint(1, 6)\n", + " result_list.append(result)\n", + "\n", + "result_list \n", + "\n", + "dice(10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "df.sort_values(by=[0]).reset_index()[0].plot(kind = 'bar')\n" ] }, { @@ -61,21 +201,88 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3 3\n", + "5 2\n", + "4 2\n", + "6 1\n", + "2 1\n", + "1 1\n", + "Name: 0, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "pd.value_counts(df[0])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1., 0., 1., 0., 3., 0., 2., 0., 2., 1.]),\n", + " array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ]),\n", + " )" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 1\n", + "47 47 47 1\n", + "56 56 56 1\n", + "9 9 9 1\n", + "73 73 73 1\n", + ".. ... ... ...\n", + "17 17 17 6\n", + "11 11 11 6\n", + "24 24 24 6\n", + "21 21 21 6\n", + "99 99 99 6\n", + "\n", + "[100 rows x 3 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "data = pd.read_csv('roll_the_dice_hundred.csv')\n", + "data=data.sort_values(by='value')\n", + "data\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data['value'].hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n\\n'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", + "\n", "\"\"\"" ] }, @@ -185,11 +746,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "mean(data['value'])" ] }, { @@ -201,11 +775,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "6 23\n", + "4 22\n", + "2 17\n", + "3 14\n", + "5 12\n", + "1 12\n", + "Name: value, dtype: int64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "data['value'].value_counts()\n", + "\n" ] }, { @@ -217,21 +811,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([12., 0., 17., 0., 14., 0., 22., 0., 12., 23.]),\n", + " array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ]),\n", + " )" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "plt.hist(data['value'])\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\nthe mean is between 3 , 4\\n'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", + "the mean is between 3 , 4\n", "\"\"\"" ] }, @@ -244,21 +879,128 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.447" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "data = pd.read_csv('roll_the_dice_thousand.csv')\n", + "data\n", + "\n", + "data=data.sort_values(by='value')\n", + "\n", + "data_hist=data.groupby('value')['value'].count()\n", + "plt.bar(data_hist.index,data_hist.values)\n", + "\n", + "mean(data['value'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3 175\n", + "1 175\n", + "4 168\n", + "2 167\n", + "6 166\n", + "5 149\n", + "Name: value, dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['value'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([175., 0., 167., 0., 175., 0., 168., 0., 149., 166.]),\n", + " array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ]),\n", + " )" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data['value'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n\\nnow, when we increase the number of samples, we can see the each value tend to the same distribution\\n\\n'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", + "\n", + "now, when we increase the number of samples, we can see the each value tend to the same distribution\n", + "\n", "\"\"\"" ] }, @@ -274,11 +1016,175 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "0 21.0\n", + "1 21.0\n", + "2 24.0\n", + "3 31.0\n", + "4 54.0\n", + ".. ...\n", + "995 16.0\n", + "996 55.0\n", + "997 30.0\n", + "998 35.0\n", + "999 43.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "data3 = pd.read_csv('ages_population3.csv')\n", + "\n", + "data3" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32.0 37\n", + "35.0 31\n", + "37.0 31\n", + "39.0 29\n", + "36.0 26\n", + " ..\n", + "76.0 1\n", + "9.0 1\n", + "1.0 1\n", + "5.0 1\n", + "7.0 1\n", + "Name: observation, Length: 75, dtype: int64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data3['observation'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 8., 33., 78., 158., 187., 174., 133., 57., 117., 55.]),\n", + " array([ 1. , 8.6, 16.2, 23.8, 31.4, 39. , 46.6, 54.2, 61.8, 69.4, 77. ]),\n", + " )" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist([data3['observation']])" ] }, { @@ -397,21 +1740,68 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "observation 41.989\n", + "dtype: float64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "data3.mean()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "observation 16.144706\n", + "dtype: float64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data3.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\nnow the values are more spread out. also we can see values around 70. this will affect the mean \\n'" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", + "now the values are more spread out. also we can see values around 70. this will affect the mean \n", "\"\"\"" ] }, @@ -424,21 +1814,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 30.0\n", + "Name: 0.25, dtype: float64 observation 40.0\n", + "Name: 0.5, dtype: float64 observation 53.0\n", + "Name: 0.75, dtype: float64 observation 41.989\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "data3.quantile()\n", + "\n", + "\n", + "q1=data3.quantile(q=0.25)\n", + "median=data3.quantile(q=0.5)\n", + "q3=data3.quantile(q=0.75)\n", + "mean=data3.mean()\n", + "\n", + "print(q1,median,q3,mean)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n\\nthe difference is 13 (53-40). it shows the means has been affected \\n'" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", + "\n", + "the difference is 13 (53-40). it shows the means has been affected \n", "\"\"\"" ] }, @@ -451,21 +1876,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 67.0\n", + "Name: 0.9, dtype: float64 observation 57.0\n", + "Name: 0.8, dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "\n", + "q9 =data3.quantile(q=0.9)\n", + "\n", + "q8 = data3.quantile(q=0.8)\n", + "\n", + "\n", + "print( q9, q8)\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\nabout 10% of people are 67 \\n'" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", + "about 10% of people are 67 \n", "\"\"\"" ] }, @@ -479,7 +1935,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -488,9 +1944,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", @@ -500,9 +1967,9 @@ ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "Python 3", "language": "python", - "name": "ironhack-3.7" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -514,7 +1981,20 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.6" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/data/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv similarity index 100% rename from data/roll_the_dice_hundred.csv rename to your-code/roll_the_dice_hundred.csv diff --git a/data/roll_the_dice_thousand.csv b/your-code/roll_the_dice_thousand.csv similarity index 100% rename from data/roll_the_dice_thousand.csv rename to your-code/roll_the_dice_thousand.csv