{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Anscombe's quartet \n", "#### (https://matplotlib.org/stable/gallery/specialty_plots/anscombe.html)\n", "\n", "`Anscombe's quartet`_ is a group of datasets (x, y) that have the same mean,\n", "standard deviation, and regression line, but which are qualitatively different.\n", "\n", "It is often used to illustrate the importance of looking at a set of data\n", "graphically and not only relying on basic statistic properties.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]\n", "y1 = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]\n", "y2 = [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74]\n", "y3 = [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73]\n", "x4 = [8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8]\n", "y4 = [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89]\n", "\n", "datasets = {\n", " 'I': (x, y1),\n", " 'II': (x, y2),\n", " 'III': (x, y3),\n", " 'IV': (x4, y4)\n", "}\n", "\n", "fig, axs = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(10, 10),\n", " gridspec_kw={'wspace': 0.08, 'hspace': 0.08})\n", "axs[0, 0].set(xlim=(0, 20), ylim=(2, 14))\n", "axs[0, 0].set(xticks=(0, 10, 20), yticks=(4, 8, 12))\n", "\n", "for ax, (label, (x, y)) in zip(axs.flat, datasets.items()):\n", " ax.text(0.1, 0.9, label, fontsize=20, transform=ax.transAxes, va='top')\n", " ax.tick_params(direction='in', top=True, right=True)\n", " ax.plot(x, y, 'o')\n", "\n", " # linear regression\n", " p1, p0 = np.polyfit(x, y, deg=1)\n", " x_lin = np.array([np.min(x), np.max(x)])\n", " y_lin = p1 * x_lin + p0\n", " ax.plot(x_lin, y_lin, 'r-', lw=2)\n", "\n", " # add text box for the statistics\n", " stats = (f'$\\\\mu$ = {np.mean(y):.2f}\\n'\n", " f'$\\\\sigma$ = {np.std(y):.2f}\\n'\n", " f'$r$ = {np.corrcoef(x, y)[0][1]:.2f}')\n", " bbox = dict(boxstyle='round', fc='blanchedalmond', ec='orange', alpha=0.5)\n", " ax.text(0.95, 0.07, stats, fontsize=9, bbox=bbox,\n", " transform=ax.transAxes, horizontalalignment='right')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 4 }