take_categorical.py

# Categorical boolean mask
categorical_feature_mask = X.dtypes==object
# filter categorical columns using mask and turn it into a list
categorical_cols = X.columns[categorical_feature_mask].tolist()

# import labelencoder
from sklearn.preprocessing import LabelEncoder
# instantiate labelencoder object
le = LabelEncoder()

# apply le on categorical feature columns
X[categorical_cols] = X[categorical_cols].apply(lambda col: le.fit_transform(col))
X[categorical_cols].head(10)