How do you rename all columns in multi level group by in pandas 0.20.1+

Question

With the release of Pandas 0.20.1, there is a new deprecation of the functionality to groupby.agg() with a dictionary for renaming.

Deprecation documentation

I'm trying to find best way to update my code to account for this, however I'm struggling with how I've currently been utilizing this rename functionality.

When I am doing an aggregate, I often have multiple functions for each source column, and I have been using this rename functionality to get to a single level index with these new column names.

Example:

df = pd.DataFrame({'A': [1, 1, 1, 2, 2],'B': range(5),'C': range(5)})

In [30]: df
Out[30]: 
   A  B  C
0  1  0  0
1  1  1  1
2  1  2  2
3  2  3  3
4  2  4  4

frame = df.groupby('A').agg({'B' : {'foo':'sum'}, 'C': {'bar' : 'min', 'bar2': 'max'}})

Which results in:

Out[33]: 
    B   C     
  foo bar bar2
A             
1   3   0    2
2   7   3    4

Which I then typically do:

frame = pd.DataFrame(frame).reset_index(col_level=1)

frame.columns = frame.columns.get_level_values(1)

frame
Out[42]: 
   A  foo  bar  bar2
0  1    3    0     2
1  2    7    3     4

So I'm looking for good ways to get a result dataframe that is single level index, but has new unique column names. Where multiple columns originated from an aggregate from a single source column. Any recommendations of best approach is greatly appreciated.

Answer 1

This works perfectly in 0.20.1 version:

d = {'sum':'foo','min':'bar','max':'bar2'}
frame = df.groupby('A').agg({'B' : ['sum'], 'C': ['min', 'max']}).rename(columns=d)
frame.columns = frame.columns.droplevel(0)
frame = frame.reset_index()
print (frame)
   A  foo  bar  bar2
0  1    3    0     2
1  2    7    3     4

If multiple mins:

d = {'B_sum':'foo','C_min':'bar','C_max':'bar2'}
frame = df.groupby('A').agg({'B' : ['sum'], 'C': ['min', 'max']})
frame.columns = frame.columns.map('_'.join)
frame = frame.reset_index().rename(columns=d)
print (frame)
   A  foo  bar  bar2
0  1    3    0     2
1  2    7    3     4