ch01-missing-data-imputation/Recipe-03-Imputing-categorical-variables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical variable imputation\n",
    "\n",
    "Missing data in categorical variables are normally replaced by the most frequent category or by an arbitrary string.\n",
    "\n",
    "In this recipe, we will replace missing values in categorical variables utilizing pandas, Scikit-learn and Feature-engine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# to split the data sets:\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# to impute missing data with sklearn:\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# to impute missing data with Feature-engine:\n",
    "from feature_engine.imputation import CategoricalImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8   A9  A10  A11 A12 A13    A14  A15  \\\n",
       "0  b  30.83  0.000  u  g  w  v  1.25    t    t    1   f   g  202.0    0   \n",
       "1  a  58.67  4.460  u  g  q  h  3.04    t    t    6   f   g   43.0  560   \n",
       "2  a  24.50    NaN  u  g  q  h   NaN  NaN  NaN    0   f   g  280.0  824   \n",
       "3  b  27.83  1.540  u  g  w  v  3.75    t    t    5   t   g  100.0    3   \n",
       "4  b  20.17  5.625  u  g  w  v  1.71    t    f    0   f   s  120.0    0   \n",
       "\n",
       "   target  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"credit_approval_uci.csv\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data in train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((483, 15), (207, 15))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(\"target\", axis=1),\n",
    "    data[\"target\"],\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A1', 'A4', 'A5', 'A6', 'A7', 'A9', 'A10', 'A12', 'A13']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_vars = X_train.select_dtypes(include=\"O\").columns.to_list()\n",
    "\n",
    "categorical_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0.017391\n",
       "A4     0.008696\n",
       "A5     0.008696\n",
       "A6     0.013043\n",
       "A7     0.013043\n",
       "A9     0.133333\n",
       "A10    0.133333\n",
       "A12    0.000000\n",
       "A13    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's inspect the proportion of missing\n",
    "# values per variable:\n",
    "\n",
    "data[categorical_vars].isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas\n",
    "\n",
    "[pd.fillna](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html)\n",
    "\n",
    "### Frequent category imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A1': 'b',\n",
       " 'A4': 'u',\n",
       " 'A5': 'g',\n",
       " 'A6': 'c',\n",
       " 'A7': 'v',\n",
       " 'A9': 't',\n",
       " 'A10': 'f',\n",
       " 'A12': 'f',\n",
       " 'A13': 'g'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Learn the variables' most frequent values:\n",
    "\n",
    "frequent_values = X_train[categorical_vars].mode().iloc[0].to_dict()\n",
    "\n",
    "frequent_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing data by the frequent category:\n",
    "\n",
    "X_train = X_train.fillna(value=frequent_values)\n",
    "X_test = X_test.fillna(value=frequent_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A9     0\n",
       "A10    0\n",
       "A12    0\n",
       "A13    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corroborate absence of missing values:\n",
    "\n",
    "X_train[categorical_vars].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A9     0\n",
       "A10    0\n",
       "A12    0\n",
       "A13    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corroborate absence of missing values:\n",
    "\n",
    "X_test[categorical_vars].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputation with a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test sets:\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(\"target\", axis=1),\n",
    "    data[\"target\"],\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A1': 'no_data',\n",
       " 'A4': 'no_data',\n",
       " 'A5': 'no_data',\n",
       " 'A6': 'no_data',\n",
       " 'A7': 'no_data',\n",
       " 'A9': 'no_data',\n",
       " 'A10': 'no_data',\n",
       " 'A12': 'no_data',\n",
       " 'A13': 'no_data'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputation_dict = {var: \"no_data\" for var in categorical_vars}\n",
    "\n",
    "imputation_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing data with a specific string:\n",
    "\n",
    "X_train.fillna(value=imputation_dict, inplace=True)\n",
    "X_test.fillna(value=imputation_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b          335\n",
       "a          144\n",
       "no_data      4\n",
       "Name: A1, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the values of an imputed variable:\n",
    "\n",
    "X_train[\"A1\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn\n",
    "\n",
    "[SimpleImputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html)\n",
    "\n",
    "### Frequent category imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test set:\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(\"target\", axis=1),\n",
    "    data[\"target\"],\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A2', 'A3', 'A8', 'A11', 'A14', 'A15']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a list with the numerical variables:\n",
    "\n",
    "remaining_vars = [var for var in X_train.columns if var not in categorical_vars]\n",
    "\n",
    "remaining_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "                  transformers=[(&#x27;imputer&#x27;,\n",
       "                                 SimpleImputer(strategy=&#x27;most_frequent&#x27;),\n",
       "                                 [&#x27;A1&#x27;, &#x27;A4&#x27;, &#x27;A5&#x27;, &#x27;A6&#x27;, &#x27;A7&#x27;, &#x27;A9&#x27;, &#x27;A10&#x27;,\n",
       "                                  &#x27;A12&#x27;, &#x27;A13&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;imputer&#x27;,\n",
       "                                 SimpleImputer(strategy=&#x27;most_frequent&#x27;),\n",
       "                                 [&#x27;A1&#x27;, &#x27;A4&#x27;, &#x27;A5&#x27;, &#x27;A6&#x27;, &#x27;A7&#x27;, &#x27;A9&#x27;, &#x27;A10&#x27;,\n",
       "                                  &#x27;A12&#x27;, &#x27;A13&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">imputer</label><div class=\"sk-toggleable__content\"><pre>[&#x27;A1&#x27;, &#x27;A4&#x27;, &#x27;A5&#x27;, &#x27;A6&#x27;, &#x27;A7&#x27;, &#x27;A9&#x27;, &#x27;A10&#x27;, &#x27;A12&#x27;, &#x27;A13&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;A2&#x27;, &#x27;A3&#x27;, &#x27;A8&#x27;, &#x27;A11&#x27;, &#x27;A14&#x27;, &#x27;A15&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(remainder='passthrough',\n",
       "                  transformers=[('imputer',\n",
       "                                 SimpleImputer(strategy='most_frequent'),\n",
       "                                 ['A1', 'A4', 'A5', 'A6', 'A7', 'A9', 'A10',\n",
       "                                  'A12', 'A13'])])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up the imputer to replace missing data with\n",
    "# the most frequent category:\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"most_frequent\")\n",
    "\n",
    "# Indicate which variables to impute:\n",
    "ct = ColumnTransformer(\n",
    "    [(\"imputer\", imputer, categorical_vars)], remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "# Find the most frequent value per variable:\n",
    "ct.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['b', 'u', 'g', 'c', 'v', 't', 'f', 'f', 'g'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the most frequent categories:\n",
    "\n",
    "ct.named_transformers_.imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['a', 'u', 'g', ..., 8.0, 396.0, 4159.0],\n",
       "       ['a', 'u', 'g', ..., 0.0, 120.0, 0.0],\n",
       "       ['b', 'y', 'p', ..., 1.0, 50.0, 1187.0],\n",
       "       ...,\n",
       "       ['a', 'u', 'g', ..., 0.0, 220.0, 5.0],\n",
       "       ['a', 'u', 'g', ..., 7.0, 140.0, 2384.0],\n",
       "       ['b', 'u', 'g', ..., 0.0, 400.0, 0.0]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace missing data:\n",
    "\n",
    "X_train = ct.transform(X_train)\n",
    "X_test = ct.transform(X_test)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>A1</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A8</th>\n",
       "      <th>A11</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>46.08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.375</td>\n",
       "      <td>8.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>4159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>v</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>15.92</td>\n",
       "      <td>2.875</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>36.33</td>\n",
       "      <td>2.125</td>\n",
       "      <td>0.085</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>22.17</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>m</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>57.83</td>\n",
       "      <td>7.04</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1332.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1 A4 A5  A6  A7 A9 A10 A12 A13     A2     A3     A8  A11    A14     A15\n",
       "0  a  u  g   c   v  t   t   t   g  46.08    3.0  2.375  8.0  396.0  4159.0\n",
       "1  a  u  g   q   v  f   f   f   g  15.92  2.875  0.085  0.0  120.0     0.0\n",
       "2  b  y  p   w   v  t   t   f   g  36.33  2.125  0.085  1.0   50.0  1187.0\n",
       "3  b  y  p  ff  ff  f   f   f   g  22.17  0.585    0.0  0.0  100.0     0.0\n",
       "4  b  u  g   m   v  t   t   t   g  57.83   7.04   14.0  6.0  360.0  1332.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert array to a pandas dataframe:\n",
    "\n",
    "X_train = pd.DataFrame(\n",
    "    X_train,\n",
    "    columns=categorical_vars + remaining_vars,\n",
    ")\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A9     0\n",
       "A10    0\n",
       "A12    0\n",
       "A13    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corroborate absence of missing values:\n",
    "\n",
    "X_train[categorical_vars].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>A1</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A8</th>\n",
       "      <th>A11</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>45.83</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>x</td>\n",
       "      <td>h</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>64.08</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>cc</td>\n",
       "      <td>h</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>31.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.625</td>\n",
       "      <td>9.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>m</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>39.25</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>4607.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>j</td>\n",
       "      <td>j</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>26.17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1 A4 A5  A6 A7 A9 A10 A12 A13     A2    A3     A8   A11    A14     A15\n",
       "0  a  u  g   q  v  t   t   t   g  45.83  10.5    5.0   7.0    0.0     0.0\n",
       "1  b  u  g   x  h  t   t   t   g  64.08  20.0   17.5   9.0    0.0  1000.0\n",
       "2  a  u  g  cc  h  t   t   t   g  31.25  3.75  0.625   9.0  181.0     0.0\n",
       "3  b  u  g   m  v  t   t   f   g  39.25   9.5    6.5  14.0  240.0  4607.0\n",
       "4  a  u  g   j  j  f   f   t   g  26.17   2.0    0.0   0.0  276.0     1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert array to a pandas dataframe:\n",
    "\n",
    "X_test = pd.DataFrame(\n",
    "    X_test,\n",
    "    columns=categorical_vars + remaining_vars,\n",
    ")\n",
    "\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A9     0\n",
       "A10    0\n",
       "A12    0\n",
       "A13    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corroborate absence of missing values:\n",
    "\n",
    "X_test[categorical_vars].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputation with a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test set:\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(\"target\", axis=1),\n",
    "    data[\"target\"],\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the imputer to replace missing data with\n",
    "# the string \"missing\":\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"constant\", fill_value=\"missing\")\n",
    "\n",
    "# Indicate which variables to impute:\n",
    "ct = ColumnTransformer(\n",
    "    [(\"imputer\", imputer, categorical_vars)], remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "# Replace missing data:\n",
    "X_train = ct.fit_transform(X_train)\n",
    "X_test = ct.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b          335\n",
       "a          144\n",
       "missing      4\n",
       "Name: A1, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert array to a pandas dataframe:\n",
    "\n",
    "X_train = pd.DataFrame(\n",
    "    X_train,\n",
    "    columns=categorical_vars + remaining_vars,\n",
    ")\n",
    "\n",
    "# Inspect the values in an imputed variable:\n",
    "\n",
    "X_train[\"A1\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature-engine\n",
    "\n",
    "[CategoricalImputer](https://feature-engine.readthedocs.io/en/latest/api_doc/imputation/CategoricalImputer.html)\n",
    "\n",
    "### Frequent category "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test set:\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(\"target\", axis=1),\n",
    "    data[\"target\"],\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CategoricalImputer(imputation_method=&#x27;frequent&#x27;,\n",
       "                   variables=[&#x27;A1&#x27;, &#x27;A4&#x27;, &#x27;A5&#x27;, &#x27;A6&#x27;, &#x27;A7&#x27;, &#x27;A9&#x27;, &#x27;A10&#x27;, &#x27;A12&#x27;,\n",
       "                              &#x27;A13&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CategoricalImputer</label><div class=\"sk-toggleable__content\"><pre>CategoricalImputer(imputation_method=&#x27;frequent&#x27;,\n",
       "                   variables=[&#x27;A1&#x27;, &#x27;A4&#x27;, &#x27;A5&#x27;, &#x27;A6&#x27;, &#x27;A7&#x27;, &#x27;A9&#x27;, &#x27;A10&#x27;, &#x27;A12&#x27;,\n",
       "                              &#x27;A13&#x27;])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CategoricalImputer(imputation_method='frequent',\n",
       "                   variables=['A1', 'A4', 'A5', 'A6', 'A7', 'A9', 'A10', 'A12',\n",
       "                              'A13'])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up the imputer to replace missing\n",
    "# data with the most frequent category:\n",
    "\n",
    "imputer = CategoricalImputer(\n",
    "    imputation_method=\"frequent\",\n",
    "    variables=categorical_vars,\n",
    ")\n",
    "\n",
    "imputer.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A1': 'b',\n",
       " 'A4': 'u',\n",
       " 'A5': 'g',\n",
       " 'A6': 'c',\n",
       " 'A7': 'v',\n",
       " 'A9': 't',\n",
       " 'A10': 'f',\n",
       " 'A12': 'f',\n",
       " 'A13': 'g'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most frequent category per variable:\n",
    "\n",
    "imputer.imputer_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing data with the most\n",
    "# frequent category:\n",
    "\n",
    "X_train = imputer.transform(X_train)\n",
    "X_test = imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A9     0\n",
       "A10    0\n",
       "A12    0\n",
       "A13    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corroborate absence of missing values:\n",
    "\n",
    "X_train[categorical_vars].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A9     0\n",
       "A10    0\n",
       "A12    0\n",
       "A13    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corroborate absence of missing values:\n",
    "\n",
    "X_test[categorical_vars].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputation with a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test set:\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(\"target\", axis=1),\n",
    "    data[\"target\"],\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the imputer to replace missing data\n",
    "# with the string \"other\":\n",
    "\n",
    "imputer = CategoricalImputer(\n",
    "    imputation_method=\"missing\",\n",
    "    fill_value=\"other\",\n",
    "    variables=categorical_vars,\n",
    ")\n",
    "\n",
    "# Replace missing data with the string \"other\":\n",
    "\n",
    "X_train = imputer.fit_transform(X_train)\n",
    "X_test = imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b        335\n",
       "a        144\n",
       "other      4\n",
       "Name: A1, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the values in an imputed variable:\n",
    "\n",
    "X_train[\"A1\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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