如何在Pandas系列列表中使用OneHotEncoder？

Question

我有一个熊猫数据框架，其中包含一系列列表。我想在这个系列中使用SciKit Learn的OneHotEncoder。我总是得到一个值错误。在

我的问题是：

import pandas as pd
import numpy as np

d = {'A': [[5,7], [3, 4, 5], [2], [1,2,3,4]]}
df = pd.DataFrame(data=d)
df
      A
0   [5, 7]
1   [3, 4, 5]
2   [2]
3   [1, 2, 3, 4]

a = np.array(df['A'])
a
array([list([5, 7]), list([3, 4, 5]), list([2]), list([1, 2, 3, 4])],
      dtype=object)

from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder(sparse = False)

X = enc.fit_transform(a)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-47-64181a9f7331> in <module>()
----> 1 X = enc.fit_transform(a)

~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit_transform(self, X, y)
   2017         """
   2018         return _transform_selected(X, self._fit_transform,
-> 2019                                    self.categorical_features, copy=True)
   2020 
   2021     def _transform(self, X):

~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in _transform_selected(X, transform, selected, copy)
   1807     X : array or sparse matrix, shape=(n_samples, n_features_new)
   1808     """
-> 1809     X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
   1810 
   1811     if isinstance(selected, six.string_types) and selected == "all":

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    431                                       force_all_finite)
    432     else:
--> 433         array = np.array(array, dtype=dtype, order=order, copy=copy)
    434 
    435         if ensure_2d:

ValueError: setting an array element with a sequence.

我使用的是Windows10、Python3.6.4、SciKit Learn 0.19.1

非常感谢大家的任何想法！在

Answer 1

对于列表项，您应该在sklearn中使用MultiLabelBinarizer

from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
print (pd.DataFrame(mlb.fit_transform(df['A']),columns=mlb.classes_, index=df.index))
   1  2  3  4  5  7
0  0  0  0  0  1  1
1  0  0  1  1  1  0
2  0  1  0  0  0  0
3  1  1  1  1  0  0