如何在 pandas 中使用groupby根据另一列中的条件计算百分比/比例总数

2022-01-22 00:00:00 python pandas dataframe pivot group-by

问题描述

我正在尝试研究如何使用 pandas 中的 groupby 函数在给定的是/否标准下计算出每年值的比例.

I'm trying to work out how to use the groupby function in pandas to work out the proportions of values per year with a given Yes/No criteria.

例如，我有一个名为 names 的数据框:

For example, I have a dataframe called names:

  Name  Number  Year   Sex Criteria
0  name1     789  1998  Male      N
1  name1     688  1999  Male      N
2  name1     639  2000  Male      N
3  name2     551  1998  Male      Y
4  name2     499  1999  Male      Y

我可以使用

namesgrouped = names.groupby(["Sex", "Year", "Criteria"]).sum()

得到:

                   Number
Sex    Year      Criteria
Male   1998 N        14507
            Y         2308
       1999 N        14119
            Y         2331

等等.我希望数字标准"列显示每个性别和年份的总数百分比 - 所以上面 1998 年的 N = 14507 和 Y = 2308 我将有 N = 86.27% 和 Y = 13.73%.

and so on. I would like the 'Number Criteria' column to show the % of the total for each gender and year - so instead of N = 14507 and Y = 2308 for 1998 above I'd have N = 86.27% and Y = 13.73%.

谁能建议如何做到这一点?

Can anyone advise how to do this?

解决方案

这个问题是建议重复.借用接受的答案，这将起作用:

This question is a direct extension of the suggested duplicate. Borrowing from the accepted answer, this will work:

In [46]: namesgrouped.groupby(level=[0, 1]).apply(lambda g: g / g.sum())
Out[46]: 
                      Number
Sex  Year Criteria          
Male 1998 N         0.588806
          Y         0.411194
     1999 N         0.579612
          Y         0.420388
     2000 N         1.000000

<小时>

编辑:转换操作可能比应用更快:

Edit: a transform operation might be faster than apply:

namesgrouped / namesgrouped.groupby(level=[0, 1]).transform('sum')

相关文章