Multi label encoding for classes with duplicates

Question

How can I n-hot encode a column of lists with duplicates?

Something like MultiLabelBinarizer from sklearn which counts the number of instances of duplicate classes instead of binarizing.

Example input:

x = pd.Series([['a', 'b', 'a'], ['b', 'c'], ['c','c']])

Expected output:

    a   b   c
0   2   1   0
1   0   1   1
2   0   0   2

Answer 1

I have written a new class MultiLabelCounter based on the MultiLabelBinarizer code.

import itertools
import numpy as np

class MultiLabelCounter():
    def __init__(self, classes=None):
        self.classes_ = classes

    def fit(self,y):
        self.classes_ = sorted(set(itertools.chain.from_iterable(y)))
        self.mapping = dict(zip(self.classes_,
                                         range(len(self.classes_))))
        return self

    def transform(self,y):
        yt = []
        for labels in y:
            data = [0]*len(self.classes_)
            for label in labels:
                data[self.mapping[label]] +=1
            yt.append(data)
        return yt

    def fit_transform(self,y):
        return self.fit(y).transform(y)

import pandas as pd
x = pd.Series([['a', 'b', 'a'], ['b', 'c'], ['c','c']])

mlc = MultiLabelCounter()
mlc.fit_transform(x)

# [[2, 1, 0], [0, 1, 1], [0, 0, 2]]