categorical_cols = ['a', 'b', 'c', 'd']   from sklearn.preprocessing import LabelEncoder # instantiate labelencoder object le = LabelEncoder()  # apply le on categorical feature columns data[categorical_cols] = data[categorical_cols].apply(lambda col: le.fit_transform(col))     from sklearn.preprocessing import OneHotEncoder ohe = OneHotEncoder()  #One-hot-encode the categorical columns. #Unfortunately outputs an array instead of dataframe. array_hot_encoded = ohe.fit_transform(data[categorical_cols])  #Convert it to df data_hot_encoded = pd.DataFrame(array_hot_encoded, index=data.index)  #Extract only the columns that didnt need to be encoded data_other_cols = data.drop(columns=categorical_cols)  #Concatenate the two dataframes :  data_out = pd.concat([data_hot_encoded, data_other_cols], axis=1)

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