04.01-Complete-Case-Analysis.ipynb

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    "## Complete Case Analysis\n",
    "\n",
    "\n",
    "Complete-case analysis (CCA), also called \"list-wise deletion\" of cases, consists in **discarding** observations where values in **any** of the variables are missing. Complete Case Analysis means literally analysing only those observations for which there is information in **all** of the variables in the dataset. \n",
    "\n",
    "### Which variables can I impute with CCA?\n",
    "\n",
    "CCA can be applied to both categorical and numerical variables.\n",
    "\n",
    "\n",
    "### Assumptions\n",
    "\n",
    "CCA works well when the data are missing completely at random (MCAR). In fact, we should use CCA if we have reasons to believe that data is missing at random, and not otherwise. When data is MCAR, excluding observations with missing information is in essence the same as randomly excluding some observations from the dataset. Therefore the dataset after CCA is a fair representation of the original dataset. \n",
    "\n",
    "\n",
    "### Advantages\n",
    "\n",
    "- Easy to implement\n",
    "- No data manipulation required\n",
    "- Preserves variable distribution (if data is MCAR, then the distribution of the variables of the reduced dataset should match the distribution in the original dataset)\n",
    "\n",
    "### Disadvantages\n",
    "\n",
    "- It can exclude a large fraction of the original dataset (if missing data is abundant)\n",
    "- Excluded observations could be informative for the analysis (if data is not missing at random)\n",
    "- CCA will create a biased dataset if the complete cases differ from the original data (e.g., when missing information is in fact MAR or NMAR and not missing at random).\n",
    "- When using our models in production, the model will not know how to handle missing data\n",
    "\n",
    "### When to use CCA\n",
    "\n",
    "- Data is missing completely at random\n",
    "- No more than 5% of the total dataset contains missing data\n",
    "\n",
    "In practice, CCA may be an acceptable method when the amount of missing information is small. Unfortunately, there is no rule of thumb to determine how much missing data is small or negligible. However, as general guidance, if the total amount of missing data is ~5% of the original dataset or less, CCA is a viable option.\n",
    "\n",
    "In many real life datasets, the amount of missing data is never small, and therefore CCA is typically never an option.\n",
    "\n",
    "### CCA and models in production\n",
    "\n",
    "When using CCA, we remove all observations that contain missing information. However, the data that we want to score with our model, may indeed contain missing information. This will pose a problem when using our model in live systems, or as we call it, when putting or models into production: when an observation contains missing data, the model will not be able to handle it. \n",
    "\n",
    "In order to avoid this problem, when putting models into production we need to do 1 of 2 things: either we do not score observations with missing data, or we replace the missing values by another number. We can choose any from the imputation techniques that we will discuss in the following lectures to replace NA in the data to be scored.\n",
    "\n",
    "## In this demo:\n",
    "\n",
    "We will use the House Prices dataset to demonstrate how to perform Complete Case Analysis.\n",
    "\n",
    "- For instructions on how to download the dataset, please refer to the lecture **Datasets** in **Section 1** of the course."
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# to show all the columns of the dataframe in the notebook\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
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     "execution_count": 2,
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   ],
   "source": [
    "# let's load the House Prices dataset\n",
    "# and explore its shape (rows and columns)\n",
    "\n",
    "data = pd.read_csv('../houseprice.csv')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "      <th>Id</th>\n",
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       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
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       "      <th>0</th>\n",
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       "      <td>VinylSd</td>\n",
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       "      <td>TA</td>\n",
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       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
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       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
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       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
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       "      <td>756</td>\n",
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       "      <td>0</td>\n",
       "      <td>1717</td>\n",
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       "      <td>Typ</td>\n",
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       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
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       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5       2003   \n",
       "1       Norm     1Fam     1Story            6            8       1976   \n",
       "2       Norm     1Fam     2Story            7            5       2001   \n",
       "3       Norm     1Fam     2Story            7            5       1915   \n",
       "4       Norm     1Fam     2Story            8            5       2000   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1          1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3          1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ         706          Unf           0        150          856   \n",
       "1          ALQ         978          Unf           0        284         1262   \n",
       "2          GLQ         486          Unf           0        434          920   \n",
       "3          ALQ         216          Unf           0        540          756   \n",
       "4          GLQ         655          Unf           0        490         1145   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710             1             0         2         1             3   \n",
       "1       1262             0             1         2         0             3   \n",
       "2       1786             1             0         2         1             3   \n",
       "3       1717             1             0         1         0             3   \n",
       "4       2198             1             0         2         1             4   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       2003.0          RFn           2         548         TA   \n",
       "1     Attchd       1976.0          RFn           2         460         TA   \n",
       "2     Attchd       2001.0          RFn           2         608         TA   \n",
       "3     Detchd       1998.0          Unf           3         642         TA   \n",
       "4     Attchd       2000.0          RFn           3         836         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
       "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
       "2            0         0    NaN   NaN         NaN        0       9    2008   \n",
       "3            0         0    NaN   NaN         NaN        0       2    2006   \n",
       "4            0         0    NaN   NaN         NaN        0      12    2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  \n",
       "0       WD        Normal     208500  \n",
       "1       WD        Normal     181500  \n",
       "2       WD        Normal     223500  \n",
       "3       WD       Abnorml     140000  \n",
       "4       WD        Normal     250000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's visualise the dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage',\n",
       " 'Alley',\n",
       " 'MasVnrType',\n",
       " 'MasVnrArea',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinType2',\n",
       " 'Electrical',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageYrBlt',\n",
       " 'GarageFinish',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PoolQC',\n",
       " 'Fence',\n",
       " 'MiscFeature']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the variables with missing observations\n",
    "\n",
    "vars_with_na = [var for var in data.columns if data[var].isnull().mean() > 0]\n",
    "vars_with_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage     float64\n",
       "Alley            object\n",
       "MasVnrType       object\n",
       "MasVnrArea      float64\n",
       "BsmtQual         object\n",
       "BsmtCond         object\n",
       "BsmtExposure     object\n",
       "BsmtFinType1     object\n",
       "BsmtFinType2     object\n",
       "Electrical       object\n",
       "FireplaceQu      object\n",
       "GarageType       object\n",
       "GarageYrBlt     float64\n",
       "GarageFinish     object\n",
       "GarageQual       object\n",
       "GarageCond       object\n",
       "PoolQC           object\n",
       "Fence            object\n",
       "MiscFeature      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's find out whether they are numerical or categorical\n",
    "data[vars_with_na].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are both numerical and categorical variables with missing observations. We can see from the variable types that some are float and some are object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>LotFrontage</th>\n",
       "      <th>Alley</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stone</td>\n",
       "      <td>186.0</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stone</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>Unf</td>\n",
       "      <td>FuseF</td>\n",
       "      <td>TA</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>Fa</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotFrontage Alley MasVnrType  MasVnrArea BsmtQual BsmtCond BsmtExposure  \\\n",
       "0         65.0   NaN    BrkFace       196.0       Gd       TA           No   \n",
       "1         80.0   NaN       None         0.0       Gd       TA           Gd   \n",
       "2         68.0   NaN    BrkFace       162.0       Gd       TA           Mn   \n",
       "3         60.0   NaN       None         0.0       TA       Gd           No   \n",
       "4         84.0   NaN    BrkFace       350.0       Gd       TA           Av   \n",
       "5         85.0   NaN       None         0.0       Gd       TA           No   \n",
       "6         75.0   NaN      Stone       186.0       Ex       TA           Av   \n",
       "7          NaN   NaN      Stone       240.0       Gd       TA           Mn   \n",
       "8         51.0   NaN       None         0.0       TA       TA           No   \n",
       "9         50.0   NaN       None         0.0       TA       TA           No   \n",
       "\n",
       "  BsmtFinType1 BsmtFinType2 Electrical FireplaceQu GarageType  GarageYrBlt  \\\n",
       "0          GLQ          Unf      SBrkr         NaN     Attchd       2003.0   \n",
       "1          ALQ          Unf      SBrkr          TA     Attchd       1976.0   \n",
       "2          GLQ          Unf      SBrkr          TA     Attchd       2001.0   \n",
       "3          ALQ          Unf      SBrkr          Gd     Detchd       1998.0   \n",
       "4          GLQ          Unf      SBrkr          TA     Attchd       2000.0   \n",
       "5          GLQ          Unf      SBrkr         NaN     Attchd       1993.0   \n",
       "6          GLQ          Unf      SBrkr          Gd     Attchd       2004.0   \n",
       "7          ALQ          BLQ      SBrkr          TA     Attchd       1973.0   \n",
       "8          Unf          Unf      FuseF          TA     Detchd       1931.0   \n",
       "9          GLQ          Unf      SBrkr          TA     Attchd       1939.0   \n",
       "\n",
       "  GarageFinish GarageQual GarageCond PoolQC  Fence MiscFeature  \n",
       "0          RFn         TA         TA    NaN    NaN         NaN  \n",
       "1          RFn         TA         TA    NaN    NaN         NaN  \n",
       "2          RFn         TA         TA    NaN    NaN         NaN  \n",
       "3          Unf         TA         TA    NaN    NaN         NaN  \n",
       "4          RFn         TA         TA    NaN    NaN         NaN  \n",
       "5          Unf         TA         TA    NaN  MnPrv        Shed  \n",
       "6          RFn         TA         TA    NaN    NaN         NaN  \n",
       "7          RFn         TA         TA    NaN    NaN        Shed  \n",
       "8          Unf         Fa         TA    NaN    NaN         NaN  \n",
       "9          RFn         Gd         TA    NaN    NaN         NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's have a look at the values of the variables with\n",
    "# missing data\n",
    "\n",
    "data[vars_with_na].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>variable</th>\n",
       "      <th>na_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PoolQC</td>\n",
       "      <td>0.995205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MiscFeature</td>\n",
       "      <td>0.963014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alley</td>\n",
       "      <td>0.937671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Fence</td>\n",
       "      <td>0.807534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FireplaceQu</td>\n",
       "      <td>0.472603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LotFrontage</td>\n",
       "      <td>0.177397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GarageType</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GarageYrBlt</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GarageFinish</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GarageQual</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GarageCond</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BsmtExposure</td>\n",
       "      <td>0.026027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BsmtFinType2</td>\n",
       "      <td>0.026027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BsmtFinType1</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BsmtCond</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BsmtQual</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MasVnrType</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Electrical</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable  na_percentage\n",
       "16        PoolQC       0.995205\n",
       "18   MiscFeature       0.963014\n",
       "1          Alley       0.937671\n",
       "17         Fence       0.807534\n",
       "10   FireplaceQu       0.472603\n",
       "0    LotFrontage       0.177397\n",
       "11    GarageType       0.055479\n",
       "12   GarageYrBlt       0.055479\n",
       "13  GarageFinish       0.055479\n",
       "14    GarageQual       0.055479\n",
       "15    GarageCond       0.055479\n",
       "6   BsmtExposure       0.026027\n",
       "8   BsmtFinType2       0.026027\n",
       "7   BsmtFinType1       0.025342\n",
       "5       BsmtCond       0.025342\n",
       "4       BsmtQual       0.025342\n",
       "3     MasVnrArea       0.005479\n",
       "2     MasVnrType       0.005479\n",
       "9     Electrical       0.000685"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's find out the percentage of observations missing per variable\n",
    "\n",
    "# calculate the percentage of missing (as we did in section 3)\n",
    "# using the isnull() and mean() methods from pandas\n",
    "data_na = data[vars_with_na].isnull().mean()\n",
    "\n",
    "# transform the array into a dataframe\n",
    "data_na = pd.DataFrame(data_na.reset_index())\n",
    "\n",
    "# add column names to the dataframe\n",
    "data_na.columns = ['variable', 'na_percentage']\n",
    "\n",
    "# order the dataframe according to percentage of na per variable\n",
    "data_na.sort_values(by='na_percentage', ascending=False, inplace=True)\n",
    "\n",
    "# show\n",
    "data_na"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first 6 variables contain a lot of missing information. So we can't use CCA if we consider those variables, as most of the observations in the dataset will be discarded. We could otherwise use CCA if we omit using those variables with a lot of NA.\n",
    "\n",
    "For this demo, I will ignore the first 6 variables with a lot of missing data, and proceed with CCA in the remaining of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Id',\n",
       " 'MSSubClass',\n",
       " 'MSZoning',\n",
       " 'LotArea',\n",
       " 'Street',\n",
       " 'LotShape',\n",
       " 'LandContour',\n",
       " 'Utilities',\n",
       " 'LotConfig',\n",
       " 'LandSlope',\n",
       " 'Neighborhood',\n",
       " 'Condition1',\n",
       " 'Condition2',\n",
       " 'BldgType',\n",
       " 'HouseStyle',\n",
       " 'OverallQual',\n",
       " 'OverallCond',\n",
       " 'YearBuilt',\n",
       " 'YearRemodAdd',\n",
       " 'RoofStyle',\n",
       " 'RoofMatl',\n",
       " 'Exterior1st',\n",
       " 'Exterior2nd',\n",
       " 'MasVnrType',\n",
       " 'MasVnrArea',\n",
       " 'ExterQual',\n",
       " 'ExterCond',\n",
       " 'Foundation',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtFinType2',\n",
       " 'BsmtFinSF2',\n",
       " 'BsmtUnfSF',\n",
       " 'TotalBsmtSF',\n",
       " 'Heating',\n",
       " 'HeatingQC',\n",
       " 'CentralAir',\n",
       " 'Electrical',\n",
       " '1stFlrSF',\n",
       " '2ndFlrSF',\n",
       " 'LowQualFinSF',\n",
       " 'GrLivArea',\n",
       " 'BsmtFullBath',\n",
       " 'BsmtHalfBath',\n",
       " 'FullBath',\n",
       " 'HalfBath',\n",
       " 'BedroomAbvGr',\n",
       " 'KitchenAbvGr',\n",
       " 'KitchenQual',\n",
       " 'TotRmsAbvGrd',\n",
       " 'Functional',\n",
       " 'Fireplaces',\n",
       " 'GarageCars',\n",
       " 'GarageArea',\n",
       " 'PavedDrive',\n",
       " 'WoodDeckSF',\n",
       " 'OpenPorchSF',\n",
       " 'EnclosedPorch',\n",
       " '3SsnPorch',\n",
       " 'ScreenPorch',\n",
       " 'PoolArea',\n",
       " 'MiscVal',\n",
       " 'MoSold',\n",
       " 'YrSold',\n",
       " 'SaleType',\n",
       " 'SaleCondition',\n",
       " 'SalePrice']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# capture variables with no or less than 5% NA\n",
    "\n",
    "vars_cca = [var for var in data.columns if data[var].isnull().mean() < 0.05]\n",
    "vars_cca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9671232876712329"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate percentage of observations with complete\n",
    "# cases: i.e., with values for all the variables\n",
    "\n",
    "# the method dropna(), discards the observations that contain\n",
    "# na in any of the rows / columns\n",
    "\n",
    "len(data[vars_cca].dropna()) / len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 81), (1412, 70))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the complete case dataset\n",
    "# in other words, remove observations with na in any variable\n",
    "\n",
    "data_cca = data[vars_cca].dropna()\n",
    "data.shape, data_cca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the histograms for all numerival variables in the complete\n",
    "# case dataset (as we did in section 3)\n",
    "\n",
    "data_cca.hist(bins=50, density=True, figsize=(12, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x70221078d0>"
      ]
     },
     "execution_count": 12,
     "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": [
    "## let's check the distribution of a few variables before and after \n",
    "# cca: histogram\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "data['GrLivArea'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "data_cca['GrLivArea'].hist(bins=50, ax=ax, color='blue', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7024b36160>"
      ]
     },
     "execution_count": 13,
     "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": [
    "## let's check the distribution of a few variables before and after \n",
    "# cca: density plot\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "data['GrLivArea'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "data_cca['GrLivArea'].plot.density(color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7024b2cbe0>"
      ]
     },
     "execution_count": 14,
     "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": [
    "## let's check the distribution of a few variables before and after \n",
    "# cca: histogram\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "data['BsmtFinSF1'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "data_cca['BsmtFinSF1'].hist(bins=50, ax=ax, color='blue', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x70228dd470>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "## let's check the distribution of a few variables before and after \n",
    "# cca: density plot\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "data['BsmtFinSF1'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "data_cca['BsmtFinSF1'].plot.density(color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the above plots, the distribution of the selected numerical variables in the original and complete case dataset is very similar, which is what we expect from CCA if data is missing at random and only for a small proportion of the observations.\n",
    "\n",
    "In the next cells I will explore the distribution of categorical variables. To do so, I will evaluate the percentage of observations that show each of the unique categories, as we did in sections 2 and 3 of the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the following function captures the percentage of observations\n",
    "# for each category in the original and complete case dataset\n",
    "# and puts them together in a new dataframe\n",
    "\n",
    "\n",
    "def categorical_distribution(df, df_cca, variable):\n",
    "    tmp = pd.concat(\n",
    "        [\n",
    "            # percentage of observations per category, original data\n",
    "            df[variable].value_counts() / len(df),\n",
    "\n",
    "            # percentage of observations per category, cca data\n",
    "            df_cca[variable].value_counts() / len(df_cca)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "    # add column names\n",
    "    tmp.columns = ['original', 'cca']\n",
    "\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TA</th>\n",
       "      <td>0.444521</td>\n",
       "      <td>0.458924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gd</th>\n",
       "      <td>0.423288</td>\n",
       "      <td>0.431303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex</th>\n",
       "      <td>0.082877</td>\n",
       "      <td>0.084986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fa</th>\n",
       "      <td>0.023973</td>\n",
       "      <td>0.024788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    original       cca\n",
       "TA  0.444521  0.458924\n",
       "Gd  0.423288  0.431303\n",
       "Ex  0.082877  0.084986\n",
       "Fa  0.023973  0.024788"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the function in a categorical variable\n",
    "categorical_distribution(data, data_cca, 'BsmtQual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>original</th>\n",
       "      <th>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>0.591781</td>\n",
       "      <td>0.588527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrkFace</th>\n",
       "      <td>0.304795</td>\n",
       "      <td>0.310198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stone</th>\n",
       "      <td>0.087671</td>\n",
       "      <td>0.090652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrkCmn</th>\n",
       "      <td>0.010274</td>\n",
       "      <td>0.010623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         original       cca\n",
       "None     0.591781  0.588527\n",
       "BrkFace  0.304795  0.310198\n",
       "Stone    0.087671  0.090652\n",
       "BrkCmn   0.010274  0.010623"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_distribution(data, data_cca, 'MasVnrType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>0.820548</td>\n",
       "      <td>0.820822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial</th>\n",
       "      <td>0.085616</td>\n",
       "      <td>0.086402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abnorml</th>\n",
       "      <td>0.069178</td>\n",
       "      <td>0.070822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.013699</td>\n",
       "      <td>0.014164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alloca</th>\n",
       "      <td>0.008219</td>\n",
       "      <td>0.005666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdjLand</th>\n",
       "      <td>0.002740</td>\n",
       "      <td>0.002125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         original       cca\n",
       "Normal   0.820548  0.820822\n",
       "Partial  0.085616  0.086402\n",
       "Abnorml  0.069178  0.070822\n",
       "Family   0.013699  0.014164\n",
       "Alloca   0.008219  0.005666\n",
       "AdjLand  0.002740  0.002125"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_distribution(data, data_cca, 'SaleCondition')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the output of the cells above, the distribution of houses in each of the categories, is very similar in the original and complete case dataset, which again, is what is expected if the data is missing completely at random, and the percentage of missing data is small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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