boxplots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as scs\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = scs.norm(0, 1).rvs(50)\n",
    "b = scs.norm(0, 3).rvs(50)\n",
    "c = scs.norm(0, 5).rvs(50)\n",
    "data = [a, b, c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 7))\n",
    "ax = fig.add_subplot(111)\n",
    "bp = ax.boxplot(data, patch_artist=True)\n",
    "for box in bp['boxes']:\n",
    "    box.set(color='#ff33cc', linewidth=1)\n",
    "    box.set(facecolor='#dddddd', alpha=0.5)\n",
    "for whisker in bp['whiskers']:\n",
    "    whisker.set(color='blue', linewidth=1, linestyle='--')\n",
    "for cap in bp['caps']:\n",
    "    cap.set(color='blue', linewidth=1)\n",
    "for median in bp['medians']:\n",
    "    median.set(color='green', linewidth=1, linestyle='--')\n",
    "for flier in bp['fliers']:\n",
    "    flier.set(color='black', marker='*', alpha=0.7)\n",
    "ax.set_title('Boxplots in Action')\n",
    "ax.set_xticklabels(['alpha', 'beta', 'gamma'])\n",
    "ax.set_xlabel('x-axis')\n",
    "ax.set_ylabel('y-axis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}