notebooks/11_new_imputers.ipynb

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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%2F11_new_imputers.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/11_new_imputers.ipynb)\n",
    "\n",
    "# 🤖⚡ scikit-learn tip #11:\n",
    "\n",
    "Need something better than SimpleImputer for missing value imputation?\n",
    "\n",
    "Try KNNImputer or IterativeImputer (inspired by R's MICE package). Both are multivariate approaches (they take other features into account!)\n",
    "\n",
    "See example 👇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('http://bit.ly/kaggletrain', nrows=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['SibSp', 'Fare', 'Age']\n",
    "X = df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   SibSp     Fare   Age\n",
       "0      1   7.2500  22.0\n",
       "1      1  71.2833  38.0\n",
       "2      0   7.9250  26.0\n",
       "3      1  53.1000  35.0\n",
       "4      0   8.0500  35.0\n",
       "5      0   8.4583   NaN"
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     "execution_count": 3,
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   "source": [
    "X"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new in 0.21, and still \"experimental\" so it must be enabled explicitly\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  7.25      , 22.        ],\n",
       "       [ 1.        , 71.2833    , 38.        ],\n",
       "       [ 0.        ,  7.925     , 26.        ],\n",
       "       [ 1.        , 53.1       , 35.        ],\n",
       "       [ 0.        ,  8.05      , 35.        ],\n",
       "       [ 0.        ,  8.4583    , 28.50639495]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute_it = IterativeImputer()\n",
    "impute_it.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new in 0.22\n",
    "from sklearn.impute import KNNImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.    ,  7.25  , 22.    ],\n",
       "       [ 1.    , 71.2833, 38.    ],\n",
       "       [ 0.    ,  7.925 , 26.    ],\n",
       "       [ 1.    , 53.1   , 35.    ],\n",
       "       [ 0.    ,  8.05  , 35.    ],\n",
       "       [ 0.    ,  8.4583, 30.5   ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute_knn = KNNImputer(n_neighbors=2)\n",
    "impute_knn.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 5 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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