Multi-Index Sorting in Pandas

To sort a MultiIndex by the "index columns" (aka. levels) you need to use the .sort_index() method and set its level argument. If you want to sort by multiple levels, the argument needs to be set to a list of level names in sequential order.

This should give you the DataFrame you need:

df.groupby(['Manufacturer',
            'Product Name', 
            'Launch Date']
          ).sum().sort_index(level=['Manufacturer','Launch Date'])

This one liner works for me:

In [1]: grouped.sortlevel(["Manufacturer","Product Launch Date"], sort_remaining=False)

                                               Sales
Manufacturer Product Name Product Launch Date       
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

Note this works too:

groups.sortlevel([0,2], sort_remaining=False)

This wouldn't have worked when you originally posted over two years ago, because sortlevel by default sorted on ALL indices which mucked up your company hierarchy. sort_remaining which disables that behavior was added last year. Here's the commit link for reference: https://github.com/pydata/pandas/commit/3ad64b11e8e4bef47e3767f1d31cc26e39593277

A hack would be to change the order of the levels:

In [11]: g
Out[11]:
                                               Sales
Manufacturer Product Name Product Launch Date
Apple        iPad         2010-04-03              30
             iPod         2001-10-23              34
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

In [12]: g.index = g.index.swaplevel(1, 2)

Sortlevel, which (as you've found) sorts the MultiIndex levels in order:

In [13]: g = g.sortlevel()

And swap back:

In [14]: g.index = g.index.swaplevel(1, 2)

In [15]: g
Out[15]:
                                               Sales
Manufacturer Product Name Product Launch Date
Apple        iPod         2001-10-23              34
             iPad         2010-04-03              30
Samsung      Galaxy       2009-04-27              24
             Galaxy Tab   2010-09-02              22

I'm of the opinion that sortlevel should not sort the remaining labels in order, so will create a github issue. :) Although it's worth mentioning the docnote about "the need for sortedness".

Note: you could avoid the first swaplevel by reordering the order of the initial groupby:

g = df.groupby(['Manufacturer', 'Product Launch Date', 'Product Name']).sum()