LabelEncoder在DataFrame中指定类 [英] LabelEncoder specify classes in DataFrame
本文介绍了LabelEncoder在DataFrame中指定类的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我正在将LabelEncoder应用于pandas DataFrame,df
I’m applying a LabelEncoder to a pandas DataFrame, df
Feat1 Feat2 Feat3 Feat4 Feat5
A A A A E
B B C C E
C D C C E
D A C D E
我正在将标签编码器应用于这样的数据帧-
I'm applying a label encoder to a dataframe like this -
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
intIndexed = df.apply(le.fit_transform)
这是标签的映射方式
A = 0
B = 1
C = 2
D = 3
E = 0
我猜测E
的值没有赋予4
,因为它没有出现在Feat 5
以外的任何其他列中.
I'm guessing that E
isn't given the value of 4
as it doesn't appear in any other column other than Feat 5
.
我希望为E
赋予4
的值-但不知道如何在DataFrame中执行此操作.
I want E
to be given the value of 4
- but don't know how to do this in a DataFrame.
推荐答案
您可以 transform
将标签更改为归一化的编码,如下所示:
You could fit
the label encoder and later transform
the labels to their normalized encoding as follows:
In [4]: from sklearn import preprocessing
...: import numpy as np
In [5]: le = preprocessing.LabelEncoder()
In [6]: le.fit(np.unique(df.values))
Out[6]: LabelEncoder()
In [7]: list(le.classes_)
Out[7]: ['A', 'B', 'C', 'D', 'E']
In [8]: df.apply(le.transform)
Out[8]:
Feat1 Feat2 Feat3 Feat4 Feat5
0 0 0 0 0 4
1 1 1 2 2 4
2 2 3 2 2 4
3 3 0 2 3 4
默认情况下,指定标签的一种方法是:
One way to specify labels by default would be:
In [9]: labels = ['A', 'B', 'C', 'D', 'E']
In [10]: enc = le.fit(labels)
In [11]: enc.classes_ # sorts the labels in alphabetical order
Out[11]:
array(['A', 'B', 'C', 'D', 'E'],
dtype='<U1')
In [12]: enc.transform('E')
Out[12]: 4
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