how to use one hot encoding in multiple columns at once
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)
