I have a data frame like this

```
lvl1=['l1A','l1A','l1B','l1C','l1D']
lvl2=['l2A','l2A','l2A','l26','l27']
wgt=[.2,.3,.15,.05,.3]
lvls=[lvl1,lvl2]
df=pd.DataFrame(wgt, lvls).reset_index()
df.columns = ['lvl' + str(i) for i in range(1,3)] + ['wgt']
df
  lvl1 lvl2   wgt
0  l1A  l2A  0.20
1  l1A  l2A  0.30
2  l1B  l2A  0.15
3  l1C  l26  0.05
4  l1D  l27  0.30

```

I want to get the average weight at each level and add them as a separate column to this data frame. 

```
pd.concat([df, df.groupby('lvl1').transform('mean').add_suffix('_l1avg'), df.groupby('lvl2').transform('mean').add_suffix('_l2avg')], axis=1)
  lvl1 lvl2   wgt  wgt_l1avg  wgt_l2avg
0  l1A  l2A  0.20       0.25   0.216667
1  l1A  l2A  0.30       0.25   0.216667
2  l1B  l2A  0.15       0.15   0.216667
3  l1C  l26  0.05       0.05   0.050000
4  l1D  l27  0.30       0.30   0.300000

```

The levels can be more than two so I would like to do this using variable instead. What is the best and efficient way to do this as the dataset get to grow very large. I don't necessarily need these to be in the same data frame. It can be just a matrix of average weights in a separate n x m matrix (2 x 5) in this case.