{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Anscombe's quartet\n", "\n", "[further info here](https://en.wikipedia.org/wiki/Anscombe%27s_quartet)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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x1y1x2y2x3y3x4y4
0108.04109.14107.4686.58
186.9588.1486.7785.76
2137.58138.741312.7487.71
398.8198.7797.1188.84
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5149.96148.10148.8487.04
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81210.84129.13128.1585.56
974.8277.2676.4287.91
1055.6854.7455.7386.89
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" ], "text/plain": [ " x1 y1 x2 y2 x3 y3 x4 y4\n", "0 10 8.04 10 9.14 10 7.46 8 6.58\n", "1 8 6.95 8 8.14 8 6.77 8 5.76\n", "2 13 7.58 13 8.74 13 12.74 8 7.71\n", "3 9 8.81 9 8.77 9 7.11 8 8.84\n", "4 11 8.33 11 9.26 11 7.81 8 8.47\n", "5 14 9.96 14 8.10 14 8.84 8 7.04\n", "6 6 7.24 6 6.13 6 6.08 8 5.25\n", "7 4 4.26 4 3.10 4 5.39 19 12.50\n", "8 12 10.84 12 9.13 12 8.15 8 5.56\n", "9 7 4.82 7 7.26 7 6.42 8 7.91\n", "10 5 5.68 5 4.74 5 5.73 8 6.89" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "#matplotlib inline\n", "\n", "df = pd.read_csv('../data/anscombe.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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x1y1x2y2x3y3x4y4
count11.00000011.00000011.00000011.00000011.00000011.00000011.00000011.000000
mean9.0000007.5009099.0000007.5009099.0000007.5000009.0000007.500909
std3.3166252.0315683.3166252.0316573.3166252.0304243.3166252.030579
min4.0000004.2600004.0000003.1000004.0000005.3900008.0000005.250000
25%6.5000006.3150006.5000006.6950006.5000006.2500008.0000006.170000
50%9.0000007.5800009.0000008.1400009.0000007.1100008.0000007.040000
75%11.5000008.57000011.5000008.95000011.5000007.9800008.0000008.190000
max14.00000010.84000014.0000009.26000014.00000012.74000019.00000012.500000
\n", "
" ], "text/plain": [ " x1 y1 x2 y2 x3 y3 \\\n", "count 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 \n", "mean 9.000000 7.500909 9.000000 7.500909 9.000000 7.500000 \n", "std 3.316625 2.031568 3.316625 2.031657 3.316625 2.030424 \n", "min 4.000000 4.260000 4.000000 3.100000 4.000000 5.390000 \n", "25% 6.500000 6.315000 6.500000 6.695000 6.500000 6.250000 \n", "50% 9.000000 7.580000 9.000000 8.140000 9.000000 7.110000 \n", "75% 11.500000 8.570000 11.500000 8.950000 11.500000 7.980000 \n", "max 14.000000 10.840000 14.000000 9.260000 14.000000 12.740000 \n", "\n", " x4 y4 \n", "count 11.000000 11.000000 \n", "mean 9.000000 7.500909 \n", "std 3.316625 2.030579 \n", "min 8.000000 5.250000 \n", "25% 8.000000 6.170000 \n", "50% 8.000000 7.040000 \n", "75% 8.000000 8.190000 \n", "max 19.000000 12.500000 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x1, y1: 0.81642051634484\n", "x2, y2: 0.8162365060002428\n", "x3, y3: 0.8162867394895984\n", "x4, y4: 0.8165214368885028\n" ] } ], "source": [ "for i in range(1, 5):\n", " correlation = df[f'x{i}'].corr(df[f'y{i}'])\n", " print(f'x{i}, y{i}: {correlation}')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# call plt.figure to create a new active plot object. otherweise, all points are drawn into the same plot\n", "for i in range(1, 5):\n", " plt.figure()\n", " sns.scatterplot(f'x{i}', f'y{i}', data=df)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }