我正在尝试使用scikit-learn的LabelBinarizer处理pandas DataFrame的分类字段。
这样做时出现错误
“ TypeError:不可排序的类型:float()
您可以看到train_data['embarked']
下面是一个分类字段,它仅包含3个值。但是当我使用LabelBinarizer
时,出现了上述错误
train_data['embarked'].head()
train_data['embarked'].value_counts()
from sklearn.preprocessing import LabelBinarizer
labelbinarizer = LabelBinarizer()
lb_result = labelbinarizer.fit_transform(train_data["embarked"])
前两行的输出如下。
0 S
1 C
2 S
3 S
4 S
Name: embarked, dtype: object
S 644
C 168
Q 77
Name: embarked, dtype: int64
导致错误的最后一行。整个错误消息如下所示。
Traceback (most recent call last):
File "<pyshell#20>", line 1, in <module>
lb_result = labelbinarizer.fit_transform(train_data["embarked"])
File "/usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/label.py", line 307, in fit_transform
return self.fit(y).transform(y)
File "/usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/label.py", line 276, in fit
self.y_type_ = type_of_target(y)
File "/usr/local/lib/python3.5/dist-packages/sklearn/utils/multiclass.py", line 284, in type_of_target
if (len(np.unique(y)) > 2) or (y.ndim >= 2 and len(y[0]) > 1):
File "/usr/local/lib/python3.5/dist-packages/numpy/lib/arraysetops.py", line 264, in unique
ret = _unique1d(ar, return_index, return_inverse, return_counts)
File "/usr/local/lib/python3.5/dist-packages/numpy/lib/arraysetops.py", line 312, in _unique1d
ar.sort()
TypeError: unorderable types: float() < str()
我无法理解的这段代码是什么问题?
答案 0 :(得分:0)
使用std::string regexPunc = "[\\p{P}]"; // matches any punctuations;
re2::RE2 re2Punc(regexPunc);
std::string sampleString = "test...test";
StringPiece input(sampleString);
int numberOfMatches = 0;
while(re2::RE2::FindAndConsume(&input, re2Punc)) {
++numberOfMatches;
}
astype('str')