Python Pandas sorting by multiindex and column

Question

In Pandas 0.17 I try to sort by a specific column while maintaining the hierarchical index (A and B). B is a running number created when setting up the dataframe through concatenation. My data looks like this:

          C      D
A   B
bar one   shiny  10
    two   dull   5
    three glossy 8
foo one   dull   3
    two   shiny  9
    three matt   12

This is what I need:

          C      D
A   B
bar two   dull   5
    three glossy 8
    one   shiny  10
foo one   dull   3
    three matt   12
    two   shiny  9

Below is the code I am using and the result. Note: Pandas 0.17 alerts that dataframe.sort will be deprecated.

df.sort_values(by="C", ascending=True)
          C      D
A   B
bar two   dull   5
foo one   dull   3
bar three glossy 8
foo three matt   12
bar one   shiny  10
foo two   shiny  9

Adding .groupby produces the same result:

df.sort_values(by="C", ascending=True).groupby(axis=0, level=0, as_index=True)

Similarly, switching to sorting indices first, and then groupby the column is not fruitful:

df.sort_index(axis=0, level=0, as_index=True).groupby(C, as_index=True)

I am not certain about reindexing I need to keep the first index A, second index B can be reassigned, but does not have to. It would surprise me if there is not an easy solution; I guess I just don't find it. Any suggestions are appreciated.

---

Edit: In the meantime I dropped the second index B, reassigned first index A to be a column instead of an index sorted multiple columns, then re-indexed it:

df.index = df.index.droplevel(1)
df.reset_index(level=0, inplace=True)
df_sorted = df.sort_values(["A", "C"], ascending=[1,1]) #A is a column here, not an index.
df_reindexed = df_sorted.set_index("A")

Still very verbose.

Answer 1

Feels like there could be a better way, but here's one approach:

In [163]: def sorter(sub_df):
     ...:     sub_df = sub_df.sort_values('C')
     ...:     sub_df.index = sub_df.index.droplevel(0)
     ...:     return sub_df

In [164]: df.groupby(level='A').apply(sorter)
Out[164]: 
                C   D
A   B                
bar two      dull   5
    three  glossy   8
    one     shiny  10
foo one      dull   3
    three    matt  12
    two     shiny   9

Answer 2

Based on chrisb's code:

Note that in my case, it's a Series not a DataFrame,

s.groupby(level='A', group_keys=False).apply(lambda x: x.sort_values(ascending=False))