如何从包含列表的 pandas 列中进行一次编码? [英] How to one-hot-encode from a pandas column containing a list?

问题描述

我想将由元素列表组成的pandas列分解为与唯一元素即one-hot-encode一样多的列(值1代表行中存在的给定元素，而0在没有的情况下).

I would like to break down a pandas column consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence).

例如，以数据框 df

Col1   Col2         Col3
 C      33     [Apple, Orange, Banana]
 A      2.5    [Apple, Grape]
 B      42     [Banana] 

我想将其转换为:

df

Col1   Col2   Apple   Orange   Banana   Grape
 C      33     1        1        1       0
 A      2.5    1        0        0       1
 B      42     0        0        1       0

我如何使用pandas/sklearn实现这一目标?

How can I use pandas/sklearn to achieve this?

推荐答案

我们还可以使用 sklearn.preprocessing.MultiLabelBinarizer :

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()
df = df.join(pd.DataFrame(mlb.fit_transform(df.pop('Col3')),
                          columns=mlb.classes_,
                          index=df.index))

结果:

In [77]: df
Out[77]:
  Col1  Col2  Apple  Banana  Grape  Orange
0    C  33.0      1       1      0       1
1    A   2.5      1       0      1       0
2    B  42.0      0       1      0       0

这篇关于如何从包含列表的 pandas 列中进行一次编码?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！