imputation_with_mean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Create Data\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3051</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.4949</td>\n",
       "      <td>0.2654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6974</td>\n",
       "      <td>0.2615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3769</td>\n",
       "      <td>0.5846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.4615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.3410</td>\n",
       "      <td>0.8308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.4436</td>\n",
       "      <td>0.4962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.5897</td>\n",
       "      <td>0.3269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6308</td>\n",
       "      <td>0.5346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6731</td>\n",
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      ],
      "text/plain": [
       "       x0      x1\n",
       "0  0.3051     NaN\n",
       "1  0.4949  0.2654\n",
       "2  0.6974  0.2615\n",
       "3  0.3769  0.5846\n",
       "4  0.2231  0.4615\n",
       "5  0.3410  0.8308\n",
       "6  0.4436  0.4962\n",
       "7  0.5897  0.3269\n",
       "8  0.6308  0.5346\n",
       "9  0.5000  0.6731"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty dataset\n",
    "df = pd.DataFrame()\n",
    "\n",
    "# Create two variables called x0 and x1. Make the first value of x1 a missing value\n",
    "df['x0'] = [0.3051,0.4949,0.6974,0.3769,0.2231,0.341,0.4436,0.5897,0.6308,0.5]\n",
    "df['x1'] = [np.nan,0.2654,0.2615,0.5846,0.4615,0.8308,0.4962,0.3269,0.5346,0.6731]\n",
    "\n",
    "# View the dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Fitting Imputer\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an imputer object that looks for 'Nan' values, then replaces them with the mean value of the feature by columns (axis=0)\n",
    "mean_imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "# Train the imputor on the df dataset\n",
    "mean_imputer = mean_imputer.fit(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Applying Imputer\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the imputer to the df dataset\n",
    "imputed_df = mean_imputer.transform(df.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>View Data\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3051    , 0.49273333],\n",
       "       [0.4949    , 0.2654    ],\n",
       "       [0.6974    , 0.2615    ],\n",
       "       [0.3769    , 0.5846    ],\n",
       "       [0.2231    , 0.4615    ],\n",
       "       [0.341     , 0.8308    ],\n",
       "       [0.4436    , 0.4962    ],\n",
       "       [0.5897    , 0.3269    ],\n",
       "       [0.6308    , 0.5346    ],\n",
       "       [0.5       , 0.6731    ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the data\n",
    "imputed_df ## nan >> 0.49273333"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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