vectorize a pandas dataframe with scikit-learn <= 0.19

pandasvectorizer.py

from sklearn.feature_extraction import DictVectorizer

class PandasVectorizer(DictVectorizer):
    def fit(self, x, y=None):
        return super(PandasVectorizer, self).fit(x.to_dict('records'))
    
    def fit_transform(self, x, y=None):
        return super(PandasVectorizer, self).fit_transform(x.to_dict('records'))
    
    def transform(self, x):
        return super(PandasVectorizer, self).transform(x.to_dict('records'))

zzexample.py

"""
>>> import pandas as pd
>>> from pandasvectorizer import PandasVectorizer
>>> df = pd.DataFrame({'a': [1,2,3], 'b': ['a', 'b', 'a']})
>>> PandasVectorizer().fit_transform(df).toarray()
array([[ 1.,  1.,  0.],
       [ 2.,  0.,  1.],
       [ 3.,  1.,  0.]])
"""