pandas add column to groupby dataframe

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?

Answers:

Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.

Method 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

Method 2

Another solution with transform len:

df['size'] = df.groupby('c')['type'].transform(len)
print df
   c type size
0  1    m    3
1  1    n    3
2  1    o    3
3  2    m    4
4  2    m    4
5  2    n    4
6  2    n    4

Another solution with Series.map and Series.value_counts:

df['size'] = df['c'].map(df['c'].value_counts())
print (df)
   c type  size
0  1    m     3
1  1    n     3
2  1    o     3
3  2    m     4
4  2    m     4
5  2    n     4
6  2    n     4

Method 3

You can calculate the groupby object and use it multiple times:

g = df.groupby('c')['type']

df = g.value_counts().reset_index(name='counts')
df['size'] = g.transform('size')

or

g.value_counts().reset_index(name='counts').assign(size=g.transform('size'))

Output:

   c type  counts  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


All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0

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