{
 "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": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": 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": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": 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": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "fig = interaction_plot(\n",
    "    x=weight,\n",
    "    trace=nutrition,\n",
    "    response=days,\n",
    "    colors=[\"red\", \"blue\"],\n",
    "    markers=[\"D\", \"^\"],\n",
    "    ms=10,\n",
    "    ax=ax,\n",
    ")"
   ]
  }
 ],
 "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": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
