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:
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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