FeatureUnion was not meant to be used that way. It instead takes two feature extractors / vectorizers and applies them to the input. It does not take data in the constructor the way it is shown. CountVectorizer is expecting a sequence of strings. The easiest way to provide it with that is to concatenate the … Read more
We can also use sklearn.preprocessing.MultiLabelBinarizer: Often we want to use sparse DataFrame for the real world data in order to save a lot of RAM. Sparse solution (for Pandas v0.25.0+) from sklearn.preprocessing import MultiLabelBinarizer mlb = MultiLabelBinarizer(sparse_output=True) df = df.join( pd.DataFrame.sparse.from_spmatrix( mlb.fit_transform(df.pop(‘Col3’)), index=df.index, columns=mlb.classes_)) result: In [38]: df Out[38]: Col1 Col2 Apple Banana Grape Orange … Read more