如何从列中的数据列表进行分组并执行操作

debugcn 发表于 Dev

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下面是df，我需要分析数据。

   gender   dob          list
0   M   01/01/87    [['Office/Work'],['31-35'], ['Salaried']]
1   M   01/01/94    [[Movies,Restaurants'],['21-25'], ['Salaried']]
2   M   01/01/95    [['College/Park'],['21-25'],['Student']] 
3   F   01/01/97    [['College'], ['21-25'], ['Student']]

预期结果1.我需要分析数据集中有多少薪水

df ['salaried']

Total = 2, Male = 2, Female = 0

df ['students']列表中有多少个学生

Total = 2, Male = 1, Female = 1

有多少人要去电影df ['Movies']

Total = 1, Male = 1, Female=0

按不同年龄段df ['age_group']分组

Age_Group Total Male Female ['21-25'] 3 2 1 ['31-35'] 1 1 0
男对女的百分比是多少

round(len(df.loc[df['gender'] == 'M']) / (len(df.loc[df['gender'] == 'M']) + len(df.loc[df['gender'] == 'F'])),2)*100

girl女

您可以用于`explode`将列的元素列表拆分为行。

df=pd.DataFrame({'gender':['M','M','M','F'],'B':[[['Office/Work'],['31-35'], ['Salaried']],[['Movies,Restaurants'],['21-25'], ['Salaried']],[[
'College/Park'],['21-25'],['Student']],[['College'], ['21-25'], ['Student']]]})

df：

  gender                                            B
0      M         [[Office/Work], [31-35], [Salaried]]
1      M  [[Movies,Restaurants], [21-25], [Salaried]]
2      M         [[College/Park], [21-25], [Student]]
3      F              [[College], [21-25], [Student]]

x=df.explode('B')

X：

  gender                     B
0      M         [Office/Work]
0      M               [31-35]
0      M            [Salaried]
1      M  [Movies,Restaurants]
1      M               [21-25]
1      M            [Salaried]
2      M        [College/Park]
2      M               [21-25]
2      M             [Student]
3      F             [College]
3      F               [21-25]
3      F             [Student]

x['B']=x.B.astype(str) 
final_df=x.groupby(['B','gender']).size().unstack(fill_value=0)

final_df：

gender                  F  M
B                           
['21-25']               1  2
['31-35']               0  1
['College']             1  0
['College/Park']        0  1
['Movies,Restaurants']  0  1
['Office/Work']         0  1
['Salaried']            0  2
['Student']             1  1

您可以使用F，M列计算总计。

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