{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Interaction of Categorical Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will visualize the interaction between categorical factors. First, we will create some categorical data. Then, we will plot it using the interaction_plot function, which internally re-codes the x-factor categories to integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from statsmodels.graphics.factorplots import interaction_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)\n",
    "weight = pd.Series(np.repeat(['low', 'hi', 'low', 'hi'], 15), name='weight')\n",
    "nutrition = pd.Series(np.repeat(['lo_carb', 'hi_carb'], 30), name='nutrition')\n",
    "days = np.log(np.random.randint(1, 30, size=60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "fig = interaction_plot(x=weight, trace=nutrition, response=days, \n",
    "                       colors=['red', 'blue'], markers=['D', '^'], ms=10, ax=ax)"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
