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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Plot Interaction of Categorical Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will vizualize the interaction between categorical factors. First, we will create some categorical data are initialized. Then plotted using the interaction_plot function which internally recodes the x-factor categories to ingegers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "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": null,
   "metadata": {
    "collapsed": false
   },
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   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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)"
   ]
  }
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