python - pandas add column to groupby dataframe

Question

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']})

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

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

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

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

Answer 1

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