print(np.transpose(np.matrix(data)).shape)
return np.transpose(np.matrix(data))
FeatureUnion([('text', Pipeline([('selector', ItemSelector(key='text')), (
    'vect', CountVectorizer(ngram_range=(1, 1), binary=True, min_df=3)), (
    'tfidf', TfidfTransformer())])), ('other data', Pipeline([('selector',
    ItemSelector(key='has_foriegn_char')), ('caster', ArrayCaster())]))])
unable to use FeatureUnion in scikit-learn due to different dimensions