pandas 将列添加到 groupby 数据框 [英] pandas add column to groupby dataframe

问题描述

我有这个简单的数据框df:

I have this simple dataframe df:

df = pd.DataFrame({'c':[1,1,1,2,2,2,2],'type':['m','n','o','m','m','n','n']})

我的目标是为每个c计算type的值，然后添加一个c大小的列.所以开始:

my goal is to count values of type for each c, and then add a column with the size of c. So starting with:

In [27]: g = df.groupby('c')['type'].value_counts().reset_index(name='t')

In [28]: g
Out[28]: 
   c type  t
0  1    m  1
1  1    n  1
2  1    o  1
3  2    m  2
4  2    n  2

第一个问题解决了.那我也可以:

the first problem is solved. Then I can also:

In [29]: a = df.groupby('c').size().reset_index(name='size')

In [30]: a
Out[30]: 
   c  size
0  1     3
1  2     4

如何将 size 列直接添加到第一个数据帧?到目前为止，我使用 map 作为:

How can I add the size column directly to the first dataframe? So far I used map as:

In [31]: a.index = a['c']

In [32]: g['size'] = g['c'].map(a['size'])

In [33]: g
Out[33]: 
   c type  t  size
0  1    m  1     3
1  1    n  1     3
2  1    o  1     3
3  2    m  2     4
4  2    n  2     4

哪个有效，但有没有更直接的方法来做到这一点?

which works, but is there a more straightforward way to do this?

推荐答案

使用 transform 将一列从 groupby 聚合添加回原始 df，transform 返回一个 Series 其索引与 orig df 对齐:

Use transform to add a column back to the orig df from a groupby aggregation, transform returns a Series with its index aligned to the orig df:

In [123]:
g = df.groupby('c')['type'].value_counts().reset_index(name='t')
g['size'] = df.groupby('c')['type'].transform('size')
g

Out[123]:
   c type  t  size
0  1    m  1     3
1  1    n  1     3
2  1    o  1     3
3  2    m  2     4
4  2    n  2     4

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