ascending/descending on multiple index levels with sortlevel

[roblevy]

In #928, support for list of boolean/binary values was added to control ascending/descending when sorting on multiple columns.

This doesn't work with sortlevel, but nor does it raise an error. This seems potentially "dangerous" to me.

In [23]: levels = list('ABC')

In [24]: length = 10

In [25]: df = pd.DataFrame(dict(alpha=np.random.choice(levels, length),
   ....:     num=np.random.random(length), vals=np.random.random(length)))

In [26]: df = df.set_index(['alpha', 'num'])

In [27]: df
Out[27]: 
                    vals
alpha num               
C     0.101632  0.566144
      0.334399  0.242917
A     0.741676  0.807468
C     0.406477  0.756441
A     0.225341  0.214034
C     0.623214  0.696784
A     0.576516  0.181786
      0.509289  0.144184
B     0.510504  0.155965
A     0.093283  0.356627

In [28]: # Sort reverse alphabetically. But then `num` is backwards!

In [29]: df.sortlevel(['alpha', 'num'], ascending=False)
Out[29]: 
                    vals
alpha num               
C     0.623214  0.696784
      0.406477  0.756441
      0.334399  0.242917
      0.101632  0.566144
B     0.510504  0.155965
A     0.741676  0.807468
      0.576516  0.181786
      0.509289  0.144184
      0.225341  0.214034
      0.093283  0.356627

In [30]: # Attempt to sort reverse alphabetically with `num` sorting forwards:

In [31]: df.sortlevel(['alpha', 'num'], ascending=[False, True])
Out[31]: 
                    vals
alpha num               
A     0.093283  0.356627
      0.225341  0.214034
      0.509289  0.144184
      0.576516  0.181786
      0.741676  0.807468
B     0.510504  0.155965
C     0.101632  0.566144
      0.334399  0.242917
      0.406477  0.756441
      0.623214  0.696784

As you can see, this last sortlevel does not behave as expected. I'd expect to see alpha sorted descending (Z - A) and num sorted ascending (0 - 1).

[jreback]

This works in master with the new sort framework. .sortlevel I don't think was passing things thru correctly.

In [15]: np.random.seed(1234)

In [19]: df = pd.DataFrame(dict(alpha=np.random.choice(levels, length),
   ....:     num=np.random.random(length), vals=np.random.random(length))).set_index(['alpha','num'])

In [20]: df
Out[20]: 
                    vals
alpha num               
C     0.704261  0.594625
B     0.704581  0.533310
C     0.218792  0.043324
A     0.924868  0.561433
C     0.442141  0.329668
      0.909316  0.502967
A     0.059809  0.111894
B     0.184287  0.607194
      0.047355  0.565945
A     0.674881  0.006764

In [21]: df.sortlevel(level=['alpha','num'],ascending=[False,True])
Out[21]: 
                    vals
alpha num               
C     0.218792  0.043324
      0.442141  0.329668
      0.704261  0.594625
      0.909316  0.502967
B     0.047355  0.565945
      0.184287  0.607194
      0.704581  0.533310
A     0.059809  0.111894
      0.674881  0.006764
      0.924868  0.561433

[jreback]

xref #9816