Kaggle competition: Categorical Feature Encoding Challenge
train['Store'].value_counts().head(20 )<count_values>

 df_train = pd.read_csv('.. /input/cat-in-the-dat/train.csv',index_col=0)
 df_test = pd.read_csv('.. /input/cat-in-the-dat/test.csv',index_col=0)
 print('There are no missing values in the dataset.')
 print(f"There are {df_train.shape[1]} features and {df_train.shape[0]:,} observation.")
 d = df_train.astype('str' ).describe().T
d['top, %'] = d['freq']/df_train.shape[0]*100
d.drop(['count','freq'],axis=1 )
train['Store'].value_counts().tail(20 )<count_values>
target = 'target'
bin_cols = [col for col in df_train.columns if 'bin' in col]
cyclic_cols = ['day','month']
ord_cols = [col for col in df_train.columns if 'ord' in col]
nom_cols = [col for col in df_train.columns if 'nom' in col]
no_target = [col for col in df_train.columns if 'target' not in col]
train['DayOfWeek'].value_counts()<count_values>
mean_1_contrast(df_train,bin_cols )
train['Open'].value_counts()<count_values>
mean_1_contrast(df_train,cyclic_cols )
train['Promo'].value_counts()<data_type_conversions>
mean_1_contrast(df_train,ord_cols )
train['Date']=pd.to_datetime(train['Date'],format='%Y-%m-%d' )<count_missing_values>
mean_1_contrast(df_train,nom_cols[:5] )
train.isna().sum()<count_missing_values>
mean_1_contrast(df_train,nom_cols[5:] )
test.isna().sum()<count_missing_values>
df_train.nom_9.value_counts().head()