Setting ordered categories for the Ames-Iowa housing data set

Ames-Iowa-category-ordering.py

quality_levels_ordered = ['Ex', 'Gd', 'TA', 'Fa', 'Po']

train_df.LotShape.cat.set_categories(['Reg', 'IR1', 'IR2', 'IR3'], ordered=True, inplace=True)
train_df.LandContour.cat.set_categories(['Lvl', 'Bnk', 'HLS', 'Low'], ordered=True, inplace=True)
train_df.Utilities.cat.set_categories(['AllPub', 'NoSewr', 'NoSeWa', 'ELO'], ordered=True, inplace=True)
train_df.LandSlope.cat.set_categories(['Gtl', 'Mod', 'Sev'], ordered=True, inplace=True)
train_df.Condition1.cat.set_categories(['PosA', 'PosN', 'Norm', 'Feedr', 'Artery', 'RRNn', 'RRNe', 'RRAn', 'RRAe'], ordered=True, inplace=True)
train_df.Condition2.cat.set_categories(['PosA', 'PosN', 'Norm', 'Feedr', 'Artery', 'RRNn', 'RRNe', 'RRAn', 'RRAe'], ordered=True, inplace=True)
train_df.BldgType.cat.set_categories(['1Fam', 'Duplx', 'TwnhsE', 'TwnhsI', '2FmCon'], ordered=True, inplace=True)
train_df.HouseStyle.cat.set_categories(['1Story', '1.5Unf', '1.5Fin', 'SFoyer', 'SLvl', '2Story', '2.5Unf', '2.5Fin'], ordered=True, inplace=True)
train_df.ExterQual.cat.set_categories(quality_levels_ordered, ordered=True, inplace=True)
train_df.ExterCond.cat.set_categories(quality_levels_ordered, ordered=True, inplace=True)
train_df.BsmtQual.cat.set_categories(['NA'] + quality_levels_ordered, ordered=True, inplace=True)
train_df.BsmtCond.cat.set_categories(['NA'] + quality_levels_ordered, ordered=True, inplace=True)
train_df.BsmtExposure.cat.set_categories(['NA', 'Gd', 'Av', 'Mn', 'No'], ordered=True, inplace=True)
train_df.BsmtFinType1.cat.set_categories(['NA', 'GLQ', 'ALQ', 'BLQ', 'Rec', 'LwQ', 'Unf'], ordered=True, inplace=True)
train_df.BsmtFinType2.cat.set_categories(['NA', 'GLQ', 'ALQ', 'BLQ', 'Rec', 'LwQ', 'Unf'], ordered=True, inplace=True)
train_df.HeatingQC.cat.set_categories(quality_levels_ordered, ordered=True, inplace=True)
train_df.Electrical.cat.set_categories(['SBrkr', 'FuseA', 'FuseF', 'FuseP', 'Mix'], ordered=True, inplace=True)
train_df.KitchenQual.cat.set_categories(quality_levels_ordered, ordered=True, inplace=True)
train_df.Functional.cat.set_categories(['Typ', 'Min1', 'Min2', 'Mod', 'Maj1', 'Maj2', 'Sev', 'Sal'], ordered=True, inplace=True)
train_df.FireplaceQu.cat.set_categories(['NA'] + quality_levels_ordered, ordered=True, inplace=True)
train_df.GarageFinish.cat.set_categories(['Fin', 'RFn', 'Unf', 'NA'], ordered=True, inplace=True)
train_df.GarageQual.cat.set_categories(['NA'] + quality_levels_ordered, ordered=True, inplace=True)
train_df.GarageCond.cat.set_categories(['NA'] + quality_levels_ordered, ordered=True, inplace=True)
train_df.PavedDrive.cat.set_categories(['Y', 'P', 'N'], ordered=True, inplace=True)
train_df.PoolQC.cat.set_categories(['NA', 'Ex', 'Gd', 'TA', 'Fa'], ordered=True, inplace=True)
train_df.Fence.cat.set_categories(['GdPrv', 'MnPrv', 'GdWd', 'MnWw', 'NA'], ordered=True, inplace=True)
train_df.SaleType.cat.set_categories(['New', 'Con', 'ConLI', 'ConLD', 'ConLw', 'WD', 'VWD', 'CWD', 'COD', 'Oth'], ordered=True, inplace=True)