Sorting on multiindex level in pandas dataframe - python-2.7

Question

I have the following pivot table
MQW MSND Grand Total
Amount($m) Amount($m) Amount($m)
Total Count Total Count Total Count
Margin Call Date
2016-12-06 16.99 4 8.50 6 25.50 10
2016-12-07 11.24 4 8.55 6 19.79 10
2016-12-08 4.21 5 8.28 6 12.49 11
2016-12-09 23.29 7 8.08 6 31.37 13
2016-12-12 0.29 1 8.73 6 9.02 7
Total 56.03 21 42.14 30 98.18 51
with the structure
MultiIndex(levels=[[u' Grand Total', u'MSND', u'MQW'], [u'Amount($m)'], [u'Count', u'Total']],labels=[[2, 2, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0], [1, 0, 1, 0, 1, 0]])
and for the life of me I can't get the 'Count' and 'Total' columns to switch places using the .sortlevel method without also reversing the order of 'MQW','MSND', and 'Grand Total'. I've also tried setting 'sort_remaining' = False but it isn't working. This is what I'm trying to get.
MQW MSND Grand Total
Amount($m) Amount($m) Amount($m)
Count Total Count Total Count Total
Margin Call Date
2016-12-06 13.99 4 7.50 6 35.50 10
2016-12-07 1.24 4 16.55 6 9.79 10
2016-12-08 7.21 5 0.28 6 22.49 11
2016-12-09 33.29 7 9.08 6 21.37 13
2016-12-12 0.29 1 8.73 6 9.02 7
Total 56.03 21 42.14 30 98.18 51
Any help would be much appreciated!

Answer 1

The following solution works. However, I believe some easier alternative should be possible.
First, create a new index inverting the level 2 labels like this:
idx = df.columns
new_idx1 = idx.set_levels(idx.levels[2][::-1], level=2)
# or, equivalently,
# new_idx1 = idx.set_levels(['Total', 'Count'], level=2)
or maybe better to change the codes of the labels:
new_idx2 = idx.set_labels(labels=[0, 1] * 3, level=2)
Note that the inner structure of new_idx2 is different than new_idx1, even though they seem to be the same. (The results of sortlevel applied on them will be different.)
You can also create a new_idx from scratch with pd.MultiIndex, pd.MultiIndex.from_arrays or pd.MultiIndex.from_tuples.
And now reindex, for example:
df_sorted = df.reindex(columns=new_idx2)
df_sorted
Out[337]:
MQW MSND Grand Total
Amount($m) Amount($m) Amount($m)
Count Total Count Total Count Total
2016-12-06 4 13.99 6 7.50 10 35.50
2016-12-07 4 1.24 6 16.55 10 9.79
2016-12-08 5 7.21 6 0.28 11 22.49
2016-12-09 7 33.29 6 9.08 13 21.37
2016-12-12 1 0.29 6 8.73 7 9.02
Total 21 56.03 30 42.14 51 98.18