random-sample-imputation.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv',usecols=['Age','Fare','Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
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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"
      ]
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     "execution_count": 116,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived     0.00000\n",
       "Age         19.86532\n",
       "Fare         0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Survived'])\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
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       "      Age      Fare\n",
       "30   40.0   27.7208\n",
       "10    4.0   16.7000\n",
       "873  47.0    9.0000\n",
       "182   9.0   31.3875\n",
       "876  20.0    9.8458\n",
       "..    ...       ...\n",
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       "168   NaN   25.9250\n",
       "\n",
       "[712 rows x 2 columns]"
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     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-121-55cc01c98bba>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  X_train['Age_imputed'] = X_train['Age']\n",
      "<ipython-input-121-55cc01c98bba>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  X_test['Age_imputed'] = X_test['Age']\n"
     ]
    }
   ],
   "source": [
    "X_train['Age_imputed'] = X_train['Age']\n",
    "X_test['Age_imputed'] = X_test['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "      Age     Fare  Age_imputed\n",
       "89   24.0   8.0500         24.0\n",
       "80   22.0   9.0000         22.0\n",
       "846   NaN  69.5500          NaN\n",
       "870  26.0   7.8958         26.0\n",
       "251  29.0  10.4625         29.0"
      ]
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     "execution_count": 124,
     "metadata": {},
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   ],
   "source": [
    "X_test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91842\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:8765: 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",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "X_train['Age_imputed'][X_train['Age_imputed'].isnull()] = X_train['Age'].dropna().sample(X_train['Age'].isnull().sum()).values\n",
    "X_test['Age_imputed'][X_test['Age_imputed'].isnull()] = X_train['Age'].dropna().sample(X_test['Age'].isnull().sum()).values"
   ]
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    "X_train['Age'].dropna().sample(X_train['Age'].isnull().sum()).values"
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    "X_train['Age'].isnull().sum()"
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      ],
      "text/plain": [
       "      Age      Fare  Age_imputed\n",
       "30   40.0   27.7208         40.0\n",
       "10    4.0   16.7000          4.0\n",
       "873  47.0    9.0000         47.0\n",
       "182   9.0   31.3875          9.0\n",
       "876  20.0    9.8458         20.0\n",
       "..    ...       ...          ...\n",
       "534  30.0    8.6625         30.0\n",
       "584   NaN    8.7125          3.0\n",
       "493  71.0   49.5042         71.0\n",
       "527   NaN  221.7792         16.0\n",
       "168   NaN   25.9250         26.0\n",
       "\n",
       "[712 rows x 3 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(X_train['Age'],label='Original',hist=False)\n",
    "sns.distplot(X_train['Age_imputed'],label = 'Imputed',hist=False)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original variable variance:  204.3495133904614\n",
      "Variance after random imputation:  208.32836615859927\n"
     ]
    }
   ],
   "source": [
    "print('Original variable variance: ', X_train['Age'].var())\n",
    "print('Variance after random imputation: ', X_train['Age_imputed'].var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>Fare</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age_imputed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>2368.246832</td>\n",
       "      <td>71.512440</td>\n",
       "      <td>43.000388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>71.512440</td>\n",
       "      <td>204.349513</td>\n",
       "      <td>204.349513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_imputed</th>\n",
       "      <td>43.000388</td>\n",
       "      <td>204.349513</td>\n",
       "      <td>208.328366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Fare         Age  Age_imputed\n",
       "Fare         2368.246832   71.512440    43.000388\n",
       "Age            71.512440  204.349513   204.349513\n",
       "Age_imputed    43.000388  204.349513   208.328366"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[['Fare', 'Age', 'Age_imputed']].cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18046a56580>"
      ]
     },
     "execution_count": 140,
     "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": [
    "X_train[['Age', 'Age_imputed']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_value = X_train['Age'].dropna().sample(1, random_state=int(observation['Fare']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('house-train.csv',usecols=['GarageQual','FireplaceQu', 'SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>FireplaceQu</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TA</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FireplaceQu GarageQual  SalePrice\n",
       "0         NaN         TA     208500\n",
       "1          TA         TA     181500\n",
       "2          TA         TA     223500\n",
       "3          Gd         TA     140000\n",
       "4          TA         TA     250000"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FireplaceQu    47.260274\n",
       "GarageQual      5.547945\n",
       "SalePrice       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data\n",
    "y = data['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-146-7b50da2dfa67>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  X_train['GarageQual_imputed'] = X_train['GarageQual']\n",
      "<ipython-input-146-7b50da2dfa67>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  X_test['GarageQual_imputed'] = X_test['GarageQual']\n",
      "<ipython-input-146-7b50da2dfa67>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  X_train['FireplaceQu_imputed'] = X_train['FireplaceQu']\n",
      "<ipython-input-146-7b50da2dfa67>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  X_test['FireplaceQu_imputed'] = X_test['FireplaceQu']\n"
     ]
    }
   ],
   "source": [
    "X_train['GarageQual_imputed'] = X_train['GarageQual']\n",
    "X_test['GarageQual_imputed'] = X_test['GarageQual']\n",
    "\n",
    "X_train['FireplaceQu_imputed'] = X_train['FireplaceQu']\n",
    "X_test['FireplaceQu_imputed'] = X_test['FireplaceQu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>FireplaceQu</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>GarageQual_imputed</th>\n",
       "      <th>FireplaceQu_imputed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TA</td>\n",
       "      <td>197000</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>265000</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TA</td>\n",
       "      <td>154300</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>277000</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TA</td>\n",
       "      <td>202900</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FireplaceQu GarageQual  SalePrice GarageQual_imputed FireplaceQu_imputed\n",
       "989         NaN         TA     197000                 TA                 NaN\n",
       "517          TA         TA     265000                 TA                  TA\n",
       "910         NaN         TA     154300                 TA                 NaN\n",
       "208          Gd         TA     277000                 TA                  Gd\n",
       "340         NaN         TA     202900                 TA                 NaN"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-148-f5eeca8d3074>:1: 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",
      "  X_train['GarageQual_imputed'][X_train['GarageQual_imputed'].isnull()] = X_train['GarageQual'].dropna().sample(X_train['GarageQual'].isnull().sum()).values\n",
      "C:\\Users\\91842\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:8765: 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",
      "  self._update_inplace(new_data)\n",
      "<ipython-input-148-f5eeca8d3074>:2: 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",
      "  X_test['GarageQual_imputed'][X_test['GarageQual_imputed'].isnull()] = X_train['GarageQual'].dropna().sample(X_test['GarageQual'].isnull().sum()).values\n",
      "<ipython-input-148-f5eeca8d3074>:4: 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",
      "  X_train['FireplaceQu_imputed'][X_train['FireplaceQu_imputed'].isnull()] = X_train['FireplaceQu'].dropna().sample(X_train['FireplaceQu'].isnull().sum()).values\n",
      "<ipython-input-148-f5eeca8d3074>: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",
      "  X_test['FireplaceQu_imputed'][X_test['FireplaceQu_imputed'].isnull()] = X_train['FireplaceQu'].dropna().sample(X_test['FireplaceQu'].isnull().sum()).values\n"
     ]
    }
   ],
   "source": [
    "X_train['GarageQual_imputed'][X_train['GarageQual_imputed'].isnull()] = X_train['GarageQual'].dropna().sample(X_train['GarageQual'].isnull().sum()).values\n",
    "X_test['GarageQual_imputed'][X_test['GarageQual_imputed'].isnull()] = X_train['GarageQual'].dropna().sample(X_test['GarageQual'].isnull().sum()).values\n",
    "\n",
    "X_train['FireplaceQu_imputed'][X_train['FireplaceQu_imputed'].isnull()] = X_train['FireplaceQu'].dropna().sample(X_train['FireplaceQu'].isnull().sum()).values\n",
    "X_test['FireplaceQu_imputed'][X_test['FireplaceQu_imputed'].isnull()] = X_train['FireplaceQu'].dropna().sample(X_test['FireplaceQu'].isnull().sum()).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.concat(\n",
    "        [\n",
    "            X_train['GarageQual'].value_counts() / len(X_train['GarageQual'].dropna()),\n",
    "            X_train['GarageQual_imputed'].value_counts() / len(X_train)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "temp.columns = ['original', 'imputed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>original</th>\n",
       "      <th>imputed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TA</th>\n",
       "      <td>0.951043</td>\n",
       "      <td>0.949486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fa</th>\n",
       "      <td>0.037171</td>\n",
       "      <td>0.037671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gd</th>\n",
       "      <td>0.009973</td>\n",
       "      <td>0.011130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Po</th>\n",
       "      <td>0.000907</td>\n",
       "      <td>0.000856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex</th>\n",
       "      <td>0.000907</td>\n",
       "      <td>0.000856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    original   imputed\n",
       "TA  0.951043  0.949486\n",
       "Fa  0.037171  0.037671\n",
       "Gd  0.009973  0.011130\n",
       "Po  0.000907  0.000856\n",
       "Ex  0.000907  0.000856"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>original</th>\n",
       "      <th>imputed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gd</th>\n",
       "      <td>0.494272</td>\n",
       "      <td>0.648709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TA</th>\n",
       "      <td>0.412439</td>\n",
       "      <td>0.538721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fa</th>\n",
       "      <td>0.040917</td>\n",
       "      <td>0.052750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Po</th>\n",
       "      <td>0.027823</td>\n",
       "      <td>0.038159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex</th>\n",
       "      <td>0.024550</td>\n",
       "      <td>0.032548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    original   imputed\n",
       "Gd  0.494272  0.648709\n",
       "TA  0.412439  0.538721\n",
       "Fa  0.040917  0.052750\n",
       "Po  0.027823  0.038159\n",
       "Ex  0.024550  0.032548"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat(\n",
    "        [\n",
    "            X_train['FireplaceQu'].value_counts() / len(X_train['FireplaceQu'].dropna()),\n",
    "            X_train['FireplaceQu_imputed'].value_counts() / len(df)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "temp.columns = ['original', 'imputed']\n",
    "\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "for category in X_train['FireplaceQu'].dropna().unique():\n",
    "    sns.distplot(X_train[X_train['FireplaceQu'] == category]['SalePrice'],hist=False,label=category)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "for category in X_train['FireplaceQu_imputed'].dropna().unique():\n",
    "    sns.distplot(X_train[X_train['FireplaceQu_imputed'] == category]['SalePrice'],hist=False,label=category)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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