Styler.applymap(subset=...) breaks promise that "any valid indexer to .loc will work." for mult-index

[jeremysalwen]

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np
labels = np.array([[0, 0,1,1],
                     [0, 1, 0, 1]])
columns = pd.MultiIndex(
  levels=[["a", "b"], ['%', '#']], labels=labels, names=['', ''])
df = pd.DataFrame(
  [[1,-1,1,1],[-1,1,1,1]],
  index=["hello", "world"],
  columns=columns)
pct_subset = pd.IndexSlice[:, pd.IndexSlice[:, '%':'%']]

def color_negative_red(val):
    color = 'red' if val < 0 else 'black'
    return 'color: %s' % color

# Works on both 0.22 and 0.24.
df.loc[pct_subset]

# This works on 0.22, but `TypeError: unhashable type` on 0.24!
df.style.applymap(color_negative_red, subset=pct_subset)

Problem description

To quote from the docs on the subset keyword argument for Styler.applymap (https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html) :

For row and column slicing, any valid indexer to .loc will work.

The code sample demonstrates an indexer for a dataframe with multindex columns that works with .loc, but doesn't work as the subset argument to applymap. Note that this indexer previously worked in pandas version 0.22, but a regression has been introduced in 0.24.2

Expected Output

Expected the indexer to apply the styling the "%" columns, and not throw an error.

Full Backtrace

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-17-994feb9d52cf> in <module>()
      1 df.style.format(
----> 2     '{:.1f}%', subset=pct_subset)

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/io/formats/style.pyc in format(self, formatter, subset)
    399                 subset = subset, self.data.columns
    400 
--> 401             sub_df = self.data.loc[subset]
    402             row_locs = self.data.index.get_indexer_for(sub_df.index)
    403             col_locs = self.data.columns.get_indexer_for(sub_df.columns)

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
   1492             except (KeyError, IndexError, AttributeError):
   1493                 pass
-> 1494             return self._getitem_tuple(key)
   1495         else:
   1496             # we by definition only have the 0th axis

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
    866     def _getitem_tuple(self, tup):
    867         try:
--> 868             return self._getitem_lowerdim(tup)
    869         except IndexingError:
    870             pass

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup)
    967         # we may have a nested tuples indexer here
    968         if self._is_nested_tuple_indexer(tup):
--> 969             return self._getitem_nested_tuple(tup)
    970 
    971         # we maybe be using a tuple to represent multiple dimensions here

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_nested_tuple(self, tup)
   1046 
   1047             current_ndim = obj.ndim
-> 1048             obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
   1049             axis += 1
   1050 

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
   1900                     raise ValueError('Cannot index with multidimensional key')
   1901 
-> 1902                 return self._getitem_iterable(key, axis=axis)
   1903 
   1904             # nested tuple slicing

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_iterable(self, key, axis)
   1203             # A collection of keys
   1204             keyarr, indexer = self._get_listlike_indexer(key, axis,
-> 1205                                                          raise_missing=False)
   1206             return self.obj._reindex_with_indexers({axis: [keyarr, indexer]},
   1207                                                    copy=True, allow_dups=True)

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _get_listlike_indexer(self, key, axis, raise_missing)
   1152             if len(ax) or not len(key):
   1153                 key = self._convert_for_reindex(key, axis)
-> 1154             indexer = ax.get_indexer_for(key)
   1155             keyarr = ax.reindex(keyarr)[0]
   1156         else:

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexes/base.pyc in get_indexer_for(self, target, **kwargs)
   4453         """
   4454         if self.is_unique:
-> 4455             return self.get_indexer(target, **kwargs)
   4456         indexer, _ = self.get_indexer_non_unique(target, **kwargs)
   4457         return indexer

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexes/multi.pyc in get_indexer(self, target, method, limit, tolerance)
   2157                                                           method=method,
   2158                                                           limit=limit,
-> 2159                                                           tolerance=tolerance)
   2160 
   2161         if not self.is_unique:

/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexes/base.pyc in get_indexer(self, target, method, limit, tolerance)
   2753                                  'backfill or nearest reindexing')
   2754 
-> 2755             indexer = self._engine.get_indexer(target._ndarray_values)
   2756 
   2757         return ensure_platform_int(indexer)

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_indexer()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.lookup()

TypeError: unhashable type

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 2.7.14.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.20-1rodete1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 36.7.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

[mroeschke]

This looks to work on master now. Could use a test.

In [49]: import pandas as pd
    ...: import numpy as np
    ...: labels = np.array([[0, 0,1,1],
    ...:                      [0, 1, 0, 1]])
    ...: columns = pd.MultiIndex(
    ...:   levels=[["a", "b"], ['%', '#']], labels=labels, names=['', ''])
    ...: df = pd.DataFrame(
    ...:   [[1,-1,1,1],[-1,1,1,1]],
    ...:   index=["hello", "world"],
    ...:   columns=columns)
    ...: pct_subset = pd.IndexSlice[:, pd.IndexSlice[:, '%':'%']]
    ...:
    ...: def color_negative_red(val):
    ...:     color = 'red' if val < 0 else 'black'
    ...:     return 'color: %s' % color
    ...:
    ...: # Works on both 0.22 and 0.24.
    ...: df.loc[pct_subset]
/anaconda3/envs/pandas-dev/bin/ipython:6: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead

Out[49]:
       a  b
       %  %
hello  1  1
world -1  1

In [50]: df.style.applymap(color_negative_red, subset=pct_subset)
    ...:
Out[50]: <pandas.io.formats.style.Styler at 0x1a24fb1b90>

In [51]: pd.__version__
Out[51]: '0.26.0.dev0+734.g0de99558b'