Scikit-Learn - one-hot 编码熊猫数据帧的某些列

Question

我有一个数据框X，其中包含整数、浮点数和字符串列。我想对每个“对象”类型的列进行一次热编码，所以我正在尝试这样做：

encoding_needed = X.select_dtypes(include='object').columns
ohe = preprocessing.OneHotEncoder()
X[encoding_needed] = ohe.fit_transform(X[encoding_needed].astype(str)) #need astype bc I imputed with 0, so some rows have a mix of zeroes and strings.

但是，我最终得到了IndexError: tuple index out of range。根据编码器期望的X: array-like, shape [n_samples, n_features]，我不太了解documentation，所以我应该可以传递数据帧。如何对encoding_needed 中特别标记的列列表进行一次性编码？

编辑：

数据是机密的，所以我不能分享它，我也不能创建一个虚拟的，因为它有 123 列。

我可以提供以下内容：

X.shape: (40755, 123)
encoding_needed.shape: (81,) and is a subset of columns.

全栈：

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-90-6b3e9fdb6f91> in <module>()
      1 encoding_needed = X.select_dtypes(include='object').columns
      2 ohe = preprocessing.OneHotEncoder()
----> 3 X[encoding_needed] = ohe.fit_transform(X[encoding_needed].astype(str))

~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/frame.py in __setitem__(self, key, value)
   3365             self._setitem_frame(key, value)
   3366         elif isinstance(key, (Series, np.ndarray, list, Index)):
-> 3367             self._setitem_array(key, value)
   3368         else:
   3369             # set column

~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/frame.py in _setitem_array(self, key, value)
   3393                 indexer = self.loc._convert_to_indexer(key, axis=1)
   3394                 self._check_setitem_copy()
-> 3395                 self.loc._setitem_with_indexer((slice(None), indexer), value)
   3396 
   3397     def _setitem_frame(self, key, value):

~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
    592                     # GH 7551
    593                     value = np.array(value, dtype=object)
--> 594                     if len(labels) != value.shape[1]:
    595                         raise ValueError('Must have equal len keys and value '
    596                                          'when setting with an ndarray')

IndexError: tuple index out of range

Answer 1

# example data
X = pd.DataFrame({'int':[0,1,2,3],
                   'float':[4.0, 5.0, 6.0, 7.0],
                   'string1':list('abcd'),
                   'string2':list('efgh')})

   int  float string1 string2
0    0    4.0       a       e
1    1    5.0       b       f
2    2    6.0       c       g
3    3    7.0       d       h

使用pandas

使用pandas.get_dummies，它会自动选择您的object 列并删除这些列，同时附加单热编码列：

pd.get_dummies(X)

   int  float  string1_a  string1_b  string1_c  string1_d  string2_e  \
0    0    4.0          1          0          0          0          1   
1    1    5.0          0          1          0          0          0   
2    2    6.0          0          0          1          0          0   
3    3    7.0          0          0          0          1          0   

   string2_f  string2_g  string2_h  
0          0          0          0  
1          1          0          0  
2          0          1          0  
3          0          0          1  

---

使用sklearn

这里我们必须指定我们只需要object 列：

from sklearn.preprocessing import OneHotEncoder

ohe = OneHotEncoder()

X_object = X.select_dtypes('object')
ohe.fit(X_object)

codes = ohe.transform(X_object).toarray()
feature_names = ohe.get_feature_names(['string1', 'string2'])

X = pd.concat([df.select_dtypes(exclude='object'), 
               pd.DataFrame(codes,columns=feature_names).astype(int)], axis=1)

   int  float  string1_a  string1_b  string1_c  string1_d  string2_e  \
0    0    4.0          1          0          0          0          1   
1    1    5.0          0          1          0          0          0   
2    2    6.0          0          0          1          0          0   
3    3    7.0          0          0          0          1          0   

   string2_f  string2_g  string2_h  
0          0          0          0  
1          1          0          0  
2          0          1          0  
3          0          0          1  

Answer 2

没有看到您的数据，我很难找到您的错误。你可以试试 pandas 的get_dummies 方法吗？

pd.get_dummies(X[encoding_needed])