Python: How to add specific columns of .mean to dataframe

Question

How can I add the means of b and c to my dataframe? I tried a merge but it didn't seem to work. So I want two extra columns b_mean and c_mean added to my dataframe with the results of df.groupBy('date').mean()

DataFrame

  a  b  c  date
0  2  3  5     1
1  5  9  1     1
2  3  7  1     1

I have the following code

import pandas as pd

a = [{'date': 1,'a':2, 'b':3, 'c':5}, {'date':1, 'a':5, 'b':9, 'c':1}, {'date':1, 'a':3, 'b':7, 'c':1}]

df = pd.DataFrame(a)

x =  df.groupby('date').mean()

Edit:

Desired output would be the following df.groupby('date').mean() returns:

             a         b         c
date                              
1     3.333333  6.333333  2.333333

My desired result would be the following data frame

   a  b  c  date  a_mean   b_mean
0  2  3  5     1  3.3333   6.3333
1  5  9  1     1  3.3333   6.3333 
2  3  7  1     1  3.3333   6.3333

Answer 1

As @ayhan mentioned, you can use pd.groupby.transform() for this. Transform is like apply, but it uses the same index as the original dataframe instead of the unique values in the column(s) grouped on.

df['a_mean'] = df.groupby('date')['a'].transform('mean')
df['b_mean'] = df.groupby('date')['b'].transform('mean')

>>> df
   a  b  c  date    b_mean    a_mean
0  2  3  5     1  6.333333  3.333333
1  5  9  1     1  6.333333  3.333333
2  3  7  1     1  6.333333  3.333333