Label encoding across multiple columns in scikit-learn

Question

I\'m trying to use scikit-learn\'s LabelEncoder to encode a pandas DataFrame of string labels. As the dataframe has many (50+) columns, I want to a

Answer 1

I checked the source code (https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/label.py) of LabelEncoder. It was based on a set of numpy transformation, which one of those is np.unique(). And this function only takes 1-d array input. (correct me if I am wrong).

Very Rough ideas... first, identify which columns needed LabelEncoder, then loop through each column.

def cat_var(df): 
    """Identify categorical features. 

    Parameters
    ----------
    df: original df after missing operations 

    Returns
    -------
    cat_var_df: summary df with col index and col name for all categorical vars
    """
    col_type = df.dtypes
    col_names = list(df)

    cat_var_index = [i for i, x in enumerate(col_type) if x=='object']
    cat_var_name = [x for i, x in enumerate(col_names) if i in cat_var_index]

    cat_var_df = pd.DataFrame({'cat_ind': cat_var_index, 
                               'cat_name': cat_var_name})

    return cat_var_df



from sklearn.preprocessing import LabelEncoder 

def column_encoder(df, cat_var_list):
    """Encoding categorical feature in the dataframe

    Parameters
    ----------
    df: input dataframe 
    cat_var_list: categorical feature index and name, from cat_var function

    Return
    ------
    df: new dataframe where categorical features are encoded
    label_list: classes_ attribute for all encoded features 
    """

    label_list = []
    cat_var_df = cat_var(df)
    cat_list = cat_var_df.loc[:, 'cat_name']

    for index, cat_feature in enumerate(cat_list): 

        le = LabelEncoder()

        le.fit(df.loc[:, cat_feature])    
        label_list.append(list(le.classes_))

        df.loc[:, cat_feature] = le.transform(df.loc[:, cat_feature])

    return df, label_list

The returned df would be the one after encoding, and label_list will show you what all those values means in the corresponding column. This is a snippet from a data process script I wrote for work. Let me know if you think there could be any further improvement.

EDIT: Just want to mention here that the methods above work with data frame with no missing the best. Not sure how it is working toward data frame contains missing data. (I had a deal with missing procedure before execute above methods)