C2_W2_Lab_3_imputation__M.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use imputation functions provided by scikit-learn.  See the scikit-learn [documentation on imputation](https://scikit-learn.org/stable/modules/impute.html#iterative-imputer)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    feature_1  feature_2\n",
       "0           0        0.0\n",
       "1           1        NaN\n",
       "2           2       20.0\n",
       "3           3       30.0\n",
       "4           4       40.0\n",
       "5           5       50.0\n",
       "6           6       60.0\n",
       "7           7       70.0\n",
       "8           8       80.0\n",
       "9           9        NaN\n",
       "10         10      100.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"feature_1\": [0,1,2,3,4,5,6,7,8,9,10],\n",
    "                   \"feature_2\": [0,np.NaN,20,30,40,50,60,70,80,np.NaN,100],\n",
    "                  })\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Imputation"
   ]
  },
  {
   "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": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='mean', verbose=0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_imputer = SimpleImputer(missing_values=np.NaN, strategy='mean')\n",
    "mean_imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='mean', verbose=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_imputer.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.],\n",
       "       [  1.,  50.],\n",
       "       [  2.,  20.],\n",
       "       [  3.,  30.],\n",
       "       [  4.,  40.],\n",
       "       [  5.,  50.],\n",
       "       [  6.,  60.],\n",
       "       [  7.,  70.],\n",
       "       [  8.,  80.],\n",
       "       [  9.,  50.],\n",
       "       [ 10., 100.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparray_imputed_mean = mean_imputer.transform(df)\n",
    "nparray_imputed_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the missing values are replaced with `50` in both cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IterativeImputer(add_indicator=False, estimator=None,\n",
       "                 imputation_order='ascending', initial_strategy='mean',\n",
       "                 max_iter=10, max_value=None, min_value=None,\n",
       "                 missing_values=nan, n_nearest_features=None, random_state=None,\n",
       "                 sample_posterior=False, skip_complete=False, tol=0.001,\n",
       "                 verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_imputer = IterativeImputer()\n",
    "reg_imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IterativeImputer(add_indicator=False, estimator=None,\n",
       "                 imputation_order='ascending', initial_strategy='mean',\n",
       "                 max_iter=10, max_value=None, min_value=None,\n",
       "                 missing_values=nan, n_nearest_features=None, random_state=None,\n",
       "                 sample_posterior=False, skip_complete=False, tol=0.001,\n",
       "                 verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_imputer.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.],\n",
       "       [  1.,  10.],\n",
       "       [  2.,  20.],\n",
       "       [  3.,  30.],\n",
       "       [  4.,  40.],\n",
       "       [  5.,  50.],\n",
       "       [  6.,  60.],\n",
       "       [  7.,  70.],\n",
       "       [  8.,  80.],\n",
       "       [  9.,  90.],\n",
       "       [ 10., 100.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparray_imputed_reg = reg_imputer.transform(df)\n",
    "nparray_imputed_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the filled in values are replaced with `10` and `90` when using regression imputation. The imputation assumed a linear relationship between feature 1 and feature 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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