我有一个虚拟的数据框，包含 ‘text’ 和 ‘vehicle’ 两列，我想对 ‘text’ 列使用 CountVectorizer，对 ‘vehicle’ 列使用 OneHotEncoding。
import pandas as pd from sklearn.preprocessing import OneHotEncoderfrom sklearn.compose import make_column_transformerfrom sklearn.feature_extraction.text import CountVectorizerdf = pd.DataFrame([['how are you','car'],['good mrng have a nice day','bike'],['today is my best working day','cycle'],['hello','bike']], columns = ['text','vehicle']) 
preprocess = make_column_transformer((CountVectorizer(), ['text']),(OneHotEncoder(), ['vehicle']))preprocess.fit_transform(df)---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-15-d7644861c938> in <module>()----> 1 preprocess.fit_transform(df)~\AppData\Roaming\Python\Python36\site-packages\sklearn\compose\_column_transformer.py in fit_transform(self, X, y)469 self._validate_output(Xs)470 --> 471 return self._hstack(list(Xs))472 473 def transform(self, X):~\AppData\Roaming\Python\Python36\site-packages\sklearn\compose\_column_transformer.py in _hstack(self, Xs)526 else:527 Xs = [f.toarray() if sparse.issparse(f) else f for f in Xs]--> 528 return np.hstack(Xs)529 530 C:\ProgramData\Anaconda3\lib\site-packages\numpy\core\shape_base.py in hstack(tup)338 return _nx.concatenate(arrs, 0)339 else:--> 340 return _nx.concatenate(arrs, 1)341 342 ValueError: all the input array dimensions except for the concatenation axis must match exactly这个错误是因为两个变换器的输出不同所导致的。
vect = CountVectorizer()vect.fit_transform(df['text'])#op<4x14 sparse matrix of type '<class 'numpy.int64'>'with 15 stored elements in Compressed Sparse Row format>encoder = OneHotEncoder(handle_unknown='ignore')encoder.fit_transform(df['vehicle'].to_numpy().reshape(-1, 1)).toarray()#op array([[0., 1., 0.], [1., 0., 0.], [0., 0., 1.], [1., 0., 0.]])如何应用 .to_numpy().reshape(-1,1)，或者是否有其他方法可以实现这个目标？
回答：
import pandas as pd from sklearn.preprocessing import OneHotEncoderfrom sklearn.compose import make_column_transformerfrom sklearn.feature_extraction.text import CountVectorizerdf = pd.DataFrame([['how are you','car'],['good mrng have a nice day','bike'],['today is my best working day','cycle'],['hello','bike']], columns = ['text','vehicle']) 这里有改动：
preprocess = make_column_transformer((CountVectorizer(), 'text'),(OneHotEncoder(), ['vehicle']))不是将 ‘text’ 放在列表中传递，而是应该以字符串格式传递。我认为这更像是一种安全机制，以防止将多个列传递给一个 CountVectorizer。
