House_Price_16.py

#                                                Ordinal features
for i in ['BsmtCond','BsmtQual']:
    train[i].replace(['Ex','Gd','TA','Fa','Po',np.nan],[5,4,3,2,1,0],inplace=True)

train['BsmtExposure'].replace(['Gd','Av','Mn','No',np.nan],[4,3,2,1,0],inplace=True)

for i in ['BsmtFinType1','BsmtFinType2']:
    train[i].replace(['GLQ','ALQ','BLQ','Rec','LwQ','Unf',np.nan],[6,5,4,3,2,1,0],inplace=True)     

#                                               Numerical features
for i in ['BsmtHalfBath','BsmtFullBath','BsmtFinSF1','BsmtFinSF2','BsmtUnfSF','TotalBsmtSF']:
    train[i].fillna(0,inplace=True)