Custom Encoder to convert Encode multilple columns of a dataset at once

MultiLabelColumnEncoder.py

"""
This snippet is not mine. It has simply been taken from a StackOverflow Answer by PriceHardman in the following link.
(Scroll one answer up from the answer this link takes you to)
https://stackoverflow.com/questions/24458645/label-encoding-across-multiple-columns-in-scikit-learn#31939145

To future me and anyone who comes across this:
- Run this on the entire dataset before splitting the dataset for consistency in the encodings.
"""

from sklearn.preprocessing import LabelEncoder
class MultiColumnLabelEncoder:
    def __init__(self,columns = None):
        self.columns = columns # array of column names to encode

    def fit(self,X,y=None):
        return self # not relevant here

    def transform(self,X):
        '''
        Transforms columns of X specified in self.columns using
        LabelEncoder(). If no columns specified, transforms all
        columns in X.
        '''
        output = X.copy()
        if self.columns is not None:
            for col in self.columns:
                output[col] = LabelEncoder().fit_transform(output[col])
        else:
            for colname,col in output.iteritems():
                output[colname] = LabelEncoder().fit_transform(col)
        return output

    def fit_transform(self,X,y=None):
        return self.fit(X,y).transform(X)

If you use the link https://stackoverflow.com/a/30267328/ you will be directed straight to the answer, no scrolling needed.