Live-Feature Engineering-All Techniques To Handle Missing Values- Day 2.ipynb

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    "# 2. Random sample imputation\n",
    "\n",
    "Here we will try to replace the NA with random value from dataset\n",
    "\n",
    "When should we use it?\n",
    "\n",
    "It will be usefull in Missing Completely At Random - MCAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c1c9945",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "9c4488b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"titanic_train.csv\",usecols=[\"Age\",\"Fare\",\"Survived\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d2b77bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Age         177\n",
       "Fare          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "6773de81",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_nan(df,variable):\n",
    "    df[\"New_\"+variable] = df[variable]\n",
    "    random_sample = df[variable].dropna().sample(df[variable].isnull().sum(),random_state=1)\n",
    "    random_sample_index = df[df[variable].isnull()].index\n",
    "    df[\"New_\"+variable][random_sample_index] = random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "e28b853d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-203-216964d8f9a3>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"New_\"+variable][random_sample_index] = random_sample\n"
     ]
    }
   ],
   "source": [
    "# replace NAN with random values from random sample\n",
    "replace_nan(df,\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "f9d7c666",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>New_Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</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>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</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>35.0</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>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived   Age     Fare  New_Age\n",
       "0         0  22.0   7.2500     22.0\n",
       "1         1  38.0  71.2833     38.0\n",
       "2         1  26.0   7.9250     26.0\n",
       "3         1  35.0  53.1000     35.0\n",
       "4         0  35.0   8.0500     35.0\n",
       "5         0   NaN   8.4583     15.0\n",
       "6         0  54.0  51.8625     54.0\n",
       "7         0   2.0  21.0750      2.0\n",
       "8         1  27.0  11.1333     27.0\n",
       "9         1  14.0  30.0708     14.0"
      ]
     },
     "execution_count": 205,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "e8ed787c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>New_Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Survived, Age, Fare, New_Age]\n",
       "Index: []"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.New_Age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "baf109e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.New_Age.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "996d8a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "3971889a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Age\"].plot(kind=\"kde\")\n",
    "df[\"New_Age\"].plot(kind=\"kde\",color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa09e6db",
   "metadata": {},
   "source": [
    "# Advantages\n",
    "1. Easy to implement\n",
    "2. Less distortion\n",
    "\n",
    "# Disadvantages\n",
    "1. Randomness might not work in real world"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6f5933",
   "metadata": {},
   "source": [
    "# 3. Capturing NAN with a new feature/variable\n",
    "\n",
    "### This is useful in Systematic Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "1378d03c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "ffbffb7a",
   "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 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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"titanic_train.csv\",usecols=[\"Age\",\"Fare\",\"Survived\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "20a49de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age_NAN\"] = np.where(df[\"Age\"].isnull(),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "dde25dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_NAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_NAN\n",
       "0         0  22.0   7.2500        0\n",
       "1         1  38.0  71.2833        0\n",
       "2         1  26.0   7.9250        0\n",
       "3         1  35.0  53.1000        0\n",
       "4         0  35.0   8.0500        0"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "80712a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"].fillna(df.Age.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "a86260f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_NAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_NAN\n",
       "0         0  22.0   7.2500        0\n",
       "1         1  38.0  71.2833        0\n",
       "2         1  26.0   7.9250        0\n",
       "3         1  35.0  53.1000        0\n",
       "4         0  35.0   8.0500        0\n",
       "5         0  28.0   8.4583        1\n",
       "6         0  54.0  51.8625        0\n",
       "7         0   2.0  21.0750        0\n",
       "8         1  27.0  11.1333        0\n",
       "9         1  14.0  30.0708        0"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c4735d",
   "metadata": {},
   "source": [
    "## Advantages\n",
    "1. easy to implement\n",
    "2. identify the missing values\n",
    "\n",
    "## Disadvantages\n",
    "1. adding new features/variables if NAN is present many multiple variables - curse of dimensionality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51644943",
   "metadata": {},
   "source": [
    "# 4. End of distribution imputation\n",
    "\n",
    "Implemented for Systematic missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91fc5f55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "bfb7ee5b",
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"titanic_train.csv\",usecols=[\"Age\",\"Fare\",\"Survived\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "def122cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "7ea907d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.27860964406095"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we will take a value after 3 Std. Deviation from mean and replace the NAN\n",
    "eod = df.Age.mean() + 3*df.Age.std()\n",
    "eod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "5b586a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "815ae297",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bipla\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\"Age\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "7e01ad69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from above we can see our outlier is on the right side so we will eod=73.27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "b8658b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_nan(df,variable,eod):\n",
    "    df[\"EOD_\"+variable] = df[variable].fillna(eod)\n",
    "    df[variable].fillna(df[variable].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "bc90ec53",
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_nan(df,\"Age\",eod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "8a57f79d",
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>EOD_Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>73.27861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>54.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>27.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>14.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare   EOD_Age\n",
       "0         0  22.0   7.2500  22.00000\n",
       "1         1  38.0  71.2833  38.00000\n",
       "2         1  26.0   7.9250  26.00000\n",
       "3         1  35.0  53.1000  35.00000\n",
       "4         0  35.0   8.0500  35.00000\n",
       "5         0  28.0   8.4583  73.27861\n",
       "6         0  54.0  51.8625  54.00000\n",
       "7         0   2.0  21.0750   2.00000\n",
       "8         1  27.0  11.1333  27.00000\n",
       "9         1  14.0  30.0708  14.00000"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "00d1650c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "4aa8739d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NLE7LHAJe02AbT2pd+41E0vrR5GyZfwWiz6IPDR9HkjQKfvyAJBXIcpekAlnuklSgbp4XJ60Do3rB2xfO1QbLXevCuD4DZ5RG9W+Y2nlb38/i8YeBejktI0kFstwlqUBPymkZ5zgllc4jd0kq0JPyyH29K+HFRUnt8shdkgrkkbuedPzNR08GHrlLUoE8cpdGrGu/GYzzD1FofCz3BgZ909y49fSTnERt6lpZS2vhtIwkFcgj9zVo+8jNI0ONS9039LV9f41O0eU+rtI8cOT4t32oE/iEVj11n78eJKiX0zKSVKCij9wlnRxLvzX0+yjile6/nL/djo7l3sO5dWm82i79J9P3YGvlHhFbgXcApwB/lZnXtbUtSaO13kuwX/4dm07wZDqebWXOPSJOAf4UeBHwLGBbRDyrjW1Jkr5dWz/GLgXuzcwvAkTEHHA58NmWtiepYOOco2/7b+W29abHyMzRP2jELwBbM/NXqtuvBH40M1/fc5/twPbq5kXAPUNs6hzgKw3jtsFc9XU1m7nq6Wou6G62Jrm+PzPP7begrSP36DP2LT9FMnMXsKvRRiL2ZuZ0k8dog7nq62o2c9XT1VzQ3Wxt5WrrPPfDwPk9t88DHmhpW5KkZdoq9/8ELoyICyLiKcAVwK0tbUuStEwr0zKZeSIiXg98hMVTIW/IzLta2FSjaZ0Wmau+rmYzVz1dzQXdzdZKrlZeUJUkjZefLSNJBbLcJalA67LcI2JrRNwTEfdGxM4xZ7khIo5FxMGesbMj4vaI+EL19awx5Do/Iu6MiLsj4q6IeEMXskXEd0XEJyLi01WuP+hCrp58p0TEpyJiT8dyHYqIAxGxPyL2diVbRJwZETdFxOeq59qPjTtXRFxU7aely6MR8RvjzlVl+83qeX8wInZX3w+t5Fp35d7Bjza4Edi6bGwncEdmXgjcUd0+2U4AOzLzh4DnAq+r9tO4sz0GvCAznw1sBrZGxHM7kGvJG4C7e253JRfAbGZu7jknugvZ3gH8Y2b+IPBsFvfdWHNl5j3VftoMbAG+Dtwy7lwRsRH4dWA6My9h8WSTK1rLlZnr6gL8GPCRntvXANeMOdMUcLDn9j3Ahur6BuCeDuy3DwI/3aVswNOATwI/2oVcLL4f4w7gBcCeLv1fAoeAc5aNjTUb8N3A/VQnZnQl17IsPwP8WxdyARuB/wbOZvFMxT1VvlZyrbsjd57YQUsOV2NdMpmZDwJUX58xzjARMQU8B/gPOpCtmvrYDxwDbs/MTuQC/gT4LeCbPWNdyAWL7/D+aETsqz66owvZngl8GfjrairrryLi9A7k6nUFsLu6PtZcmXkEeAvwJeBB4HhmfrStXOux3Ff9aAM9ISImgJuB38jMR8edByAzH8/FX5nPAy6NiEvGHImI+FngWGbuG3eWAZ6XmT/C4nTk6yLi+eMOxOLR548Af56ZzwG+xninrb5F9QbKlwL/MO4sANVc+uXABcD3AqdHxC+1tb31WO7r4aMNjkbEBoDq67FxhIiI72Sx2P8uM9/fpWwAmflVYJ7F1yzGnet5wEsj4hAwB7wgIt7TgVwAZOYD1ddjLM4fX9qBbIeBw9VvXgA3sVj248615EXAJzPzaHV73Ll+Crg/M7+cmf8HvB/48bZyrcdyXw8fbXArcFV1/SoW57tPqogI4Hrg7sx8W1eyRcS5EXFmdf2pLD7hPzfuXJl5TWael5lTLD6n/ikzf2ncuQAi4vSIePrSdRbnaQ+OO1tm/g/w3xFxUTV0GYsf6z32fVbZxhNTMjD+XF8CnhsRT6u+Py9j8QXodnKN64WOhi9MvBj4PHAf8LtjzrKbxfmz/2PxSObVwPew+MLcF6qvZ48h10+wOF31GWB/dXnxuLMBPwx8qsp1EPj9anzs+6wn4wxPvKA69lwszm1/urrctfSc70i2zcDe6v/zA8BZHcn1NOAh4IyesS7k+gMWD2YOAn8LnNZWLj9+QJIKtB6nZSRJq7DcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoH+H0TjKki/UQzLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.EOD_Age.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "4547aa68",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bipla\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dem6NTcCVpZQLgYeAuxNmlsbUnT2AdByW0HgaSmg8Cc8S4APA46WUw8DLEbGruf0c4DxgR+PX+Omi8WvoUuUYYFVaRMyi8eTr50VEoRHUQuPZxo56E+CFUsrFkzSiNG5eglDVXQk8Wkr5WCmlVko5m8ZfeHgN+JPmteAzaDw5DzT+asHpETF8SSIifitjcGksBlhVt4Qjz3a/TuOJsvfReLaqv6Xxl0TeKKX8kka0/zIinqfxDHOfm7RppTb4bGiasiJiZinlYPMyxXdo/EWKl7Pnko6X14A1lT3dfOL4U4C/ML6aajwDlqQkXgOWpCQGWJKSGGBJSmKAJSmJAZakJP8P+u27OjxRa8YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\"Age\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "7b658d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bipla\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EOD_Age'>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\"EOD_Age\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f12427",
   "metadata": {},
   "source": [
    "## Advantages\n",
    "1. easy to implement\n",
    "2. captures the importance of missingness\n",
    "\n",
    "## Disadvantages\n",
    "1. Distorts the original distribution\n",
    "2. Masks the predictive power of original distribution\n",
    "3. If NA is too big then it will intake the outliers\n",
    "4. If NA is too small then it will be considered as outlier because of inclusion of EOD values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8115569f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}