Imputing missing values part 1

hpp_missing_values_1.py

# columns with attributes like Pool, Fence etc. marked as NaN indicate the absence of these features.
attributes_with_na = ['PoolQC','MiscFeature','Alley','Fence','MasVnrType','FireplaceQu',
               'GarageQual','GarageCond','GarageFinish','GarageType',
               'BsmtExposure','BsmtCond','BsmtQual','BsmtFinType1','BsmtFinType2']

# replace 'NaN' with 'None' in these columns
for col in attributes_with_na:
    total_df[col].fillna('None',inplace=True)
    
#NAs in basement related columns will indicate no masonry veneer. Thus replacing MasVnr Area with 0
total_df.MasVnrArea.fillna(0,inplace=True)

#NAs in basement related columns will indicate no basement. Thus replacing all areas and count column with 0 
total_df.BsmtFullBath.fillna(0,inplace=True)
total_df.BsmtHalfBath.fillna(0,inplace=True)
total_df.BsmtFinSF1.fillna(0,inplace=True)
total_df.BsmtFinSF2.fillna(0,inplace=True)
total_df.BsmtUnfSF.fillna(0,inplace=True)
total_df.TotalBsmtSF.fillna(0,inplace=True)

#Similarly for Garage Cars-- fill with 0; cause if no garage, no cars can parked in it
total_df.GarageCars.fillna(0,inplace=True)   
# doing the same for GarageArea
total_df.GarageArea.fillna(0,inplace=True)