Python 标签编码多个分类列

我有一个混合了int、float、category和bool数据类型的数据帧，我正在尝试使用LabelEncoder.fit_转换将category和bool数据转换为int。当在单个列上执行时，它工作得非常好，但是当我尝试通过DF执行for循环时，出现以下错误：

relabel = preprocessing.LabelEncoder()
for i in first_buyer.columns:
    if str(first_buyer[i].dtypes) not in ["float64","int64","bool"]:
        first_buyer[i] = relabel.fit_transform(first_buyer[i])

错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py in _encode(values, uniques, encode)
    104         try:
--> 105             res = _encode_python(values, uniques, encode)
    106         except TypeError:

~\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py in _encode_python(values, uniques, encode)
     58     if uniques is None:
---> 59         uniques = sorted(set(values))
     60         uniques = np.array(uniques, dtype=values.dtype)

TypeError: '<' not supported between instances of 'str' and 'int'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-17-42e60975f0b6> in <module>
      4 for i in first_buyer.columns:
      5     if str(first_buyer[i].dtypes) not in ["float64","int64","bool"]:
----> 6         first_buyer[i] = relabel.fit_transform(first_buyer[i])

~\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py in fit_transform(self, y)
    234         """
    235         y = column_or_1d(y, warn=True)
--> 236         self.classes_, y = _encode(y, encode=True)
    237         return y
    238 

~\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py in _encode(values, uniques, encode)
    105             res = _encode_python(values, uniques, encode)
    106         except TypeError:
--> 107             raise TypeError("argument must be a string or number")
    108         return res
    109     else:

TypeError: argument must be a string or number

---

我希望我的代码输出将所有分类变量转换为数字，以便我可以训练我的数据集

虽然我没有看到您的数据帧，但错误很可能是由于某列中存在NAs，或者是由于存在混合类型的列

请尝试以下示例：

from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd

relabel = LabelEncoder()

# Nas in the dataframe
data1 = pd.DataFrame([['a', 'b', 'c'] ,['1', '2', np.nan]], columns=['A', 'B', 'C'])
# Will raise an error
relabel.fit_transform(data1['C'])

# Mixed types
data2 = pd.DataFrame([['a', 'b', 'c'], ['1', '2', 3]], columns=['A', 'B', 'C'])
# Will raise an error
relabel.fit_transform(data2['C'])

# Clean data
data3 = pd.DataFrame([['a', 'b', 'c'], ['1', '2', '3']], columns=['A', 'B', 'C'])
# Will work
relabel.fit_transform(data3['C'])

具有混合类型的列将通过筛选器的原因是其“数据类型”为“对象”

在使用LabelEncoder之前，您应该进行一些额外的预处理，以确保列没有丢失值，并且是相同类型的列

如果知道要转换的列的所有数据类型，还应使用“in”而不是“not in”的筛选器

您可以使用columns对象的fillna（）和astype（）方法来完成此操作：

# Define a dummy variable for missing values that is of the same type as the column 
data1['C'] = data1['C'].fillna('DUMMY_VARIABLE_FOR_NA')
# Will work now
relabel.fit_transform(data1['C'])

data2['C'] = data2['C'].astype(str)
# Will work now
relabel.fit_transform(data2['C'])

# Define a dummy variable for missing values that is of the same type as the column 
data1['C'] = data1['C'].fillna('DUMMY_VARIABLE_FOR_NA')
# Will work now
relabel.fit_transform(data1['C'])

data2['C'] = data2['C'].astype(str)
# Will work now
relabel.fit_transform(data2['C'])