python - 将数据框列字符串值转换为虚拟变量列

Question

我有以下数据框(不包括其余列):

| customer_id | department                    |
| ----------- | ----------------------------- |
| 11          | ['nail', 'men_skincare']      |
| 23          | ['nail', 'fragrance']         |
| 25          | []                            |
| 45          | ['skincare', 'men_fragrance'] |

我正在对我的数据进行预处理以适应模型。我想将部门变量转换为每个唯一部门类别的虚拟变量(对于可能存在的许多唯一部门，不仅限于此处的内容)。

想要得到这样的结果:

| customer_id | department                    | nail | men_skincare | fragrance | skincare | men_fragrance |
| ----------- | ----------                    | ---- | ------------ | --------- | -------- | ------------- |
| 11          | ['nail', 'men_skincare']      | 1    | 1            | 0         | 0        | 0             |
| 23          | ['nail', 'fragrance']         | 1    | 0            | 1         | 0        | 0             |
| 25          | []                            | 0    | 0            | 0         | 0        | 0             |
| 45          | ['skincare', 'men_fragrance'] | 0    | 0            | 0         | 1        | 1             |

我试过这个link ，但是当我拼接它时，它把它当作一个字符串，并且只为字符串中的每个字符创建一个列；我用了什么:

df['1st'] = df['department'].str[0]
df['2nd'] = df['department'].str[1]
df['3rd'] = df['department'].str[2]
df['4th'] = df['department'].str[3]
df['5th'] = df['department'].str[4]
df['6th'] = df['department'].str[5]
df['7th'] = df['department'].str[6]
df['8th'] = df['department'].str[7]
df['9th'] = df['department'].str[8]
df['10th'] = df['department'].str[9]

然后我尝试拆分字符串并使用以下方法变成列表:

df['new_column'] = df['department'].apply(lambda x: x.split(","))

然后再次尝试，仍然只为每个字符创建列。

有什么建议吗？

编辑:我使用 anky 发送的链接找到了答案，特别是我使用了这个:https://stackoverflow.com/a/29036042

对我有用的:

df['department'] = df['department'].str.replace("'",'').str.replace("]",'').str.replace("[",'').str.replace(' ','')
df['department'] = df['department'].apply(lambda x: x.split(","))
s = df['department']
df1 = pd.get_dummies(s.apply(pd.Series).stack()).sum(level=0)
df = pd.merge(df, df1, right_index=True, left_index=True, how = 'left')

Answer 1

import pandas as pd

您可以通过 explode() 、value_counts() 和 fillna() 方法来做到这一点:

data=df.explode('department').fillna('empty')

现在使用crosstab()方法:

data=pd.crosstab(data['customer_id'],data['department'])

因为 concat() 方法给你一个错误，所以使用 merge() 方法和 drop() 方法:

data=pd.merge(df.set_index('customer_id'),data,left_index=True,right_index=True).drop(columns=['empty'])

现在，如果您打印data，您将获得所需的输出: