Pandas - groupby one column and get mean of all other columns

Please try:

df.groupby('A').agg('mean')

sample data

   B  C  A
0  1  4  K
1  2  6  S
2  4  7  K
3  6  3  K
4  2  1  S
5  7  3  K
6  8  9  K
7  9  3  K


print(df.groupby('A').agg('mean'))




     B         C
A                    
K  5.833333  4.833333
S  2.000000  3.500000

You can use df.groupby('col').mean(). For example to calcualte mean for columns 'A', 'B' and 'C':

   A    B  C  D
0  1  NaN  1  1
1  1  2.0  2  1
2  2  3.0  1  1
3  1  4.0  1  1
4  2  5.0  2  1

df[['A', 'B', 'C']].groupby('A').mean()

or

df.groupby('A')[['A', 'B', 'C']].mean()

Output:

     B         C
A
1  3.0  1.333333
2  4.0  1.500000

If you need mean for all columns:

df.groupby('A').mean()

Output:

     B         C    D
A
1  3.0  1.333333  1.0
2  4.0  1.500000  1.0

Perhaps the missing column is string rather than numeric?

df = pd.DataFrame({
  'A': ['big', 'small','small', 'small'],
  'B': [1,0,0,0],
  'C': [1,1,1,0],
  'D': ['1','0','0','0']
})
df.groupby(['A']).mean()

Output:

Here, converting the column to a numeric type such as int or float produces the desired result:

df.D = df.D.astype(int)
df.groupby(['A']).mean()

Output: