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 "cells": [
  {
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   "source": [
    "[![Open in Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/justmarkham/scikit-learn-tips/master?filepath=notebooks%2F27_impute_categorical_features.ipynb)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/justmarkham/scikit-learn-tips/blob/master/notebooks/27_impute_categorical_features.ipynb)\n",
    "\n",
    "# 🤖⚡ scikit-learn tip #27 ([video](https://www.youtube.com/watch?v=k3KrhjvaCq0&list=PL5-da3qGB5ID7YYAqireYEew2mWVvgmj6&index=27))\n",
    "\n",
    "Need to impute missing values for a categorical feature?\n",
    "\n",
    "Two options:\n",
    "\n",
    "1. Impute the most frequent value\n",
    "2. Impute the value \"missing\", which treats it as a separate category\n",
    "\n",
    "See example 👇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "X = pd.DataFrame({'Shape':['square', 'square', 'oval', 'circle', np.nan]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>square</td>\n",
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       "      <td>oval</td>\n",
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       "      <th>3</th>\n",
       "      <td>circle</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    Shape\n",
       "0  square\n",
       "1  square\n",
       "2    oval\n",
       "3  circle\n",
       "4     NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['square'],\n",
       "       ['square'],\n",
       "       ['oval'],\n",
       "       ['circle'],\n",
       "       ['square']], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "imputer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['square'],\n",
       "       ['square'],\n",
       "       ['oval'],\n",
       "       ['circle'],\n",
       "       ['missing']], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = SimpleImputer(strategy='constant', fill_value='missing')\n",
    "imputer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Want more tips? [View all tips on GitHub](https://github.com/justmarkham/scikit-learn-tips) or [Sign up to receive 2 tips by email every week](https://scikit-learn.tips) 💌\n",
    "\n",
    "© 2020 [Data School](https://www.dataschool.io). All rights reserved."
   ]
  }
 ],
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