Multiple lambdas for the same column return KeyError in DataFrameGroupBy.agg with named aggregation

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Multiple lambdas for the same column return KeyError in DataFrameGroupBy.agg

In [1]: import pandas as pd

In [2]: df = pd.DataFrame({"A": [1, 2]})

In [3]: df.groupby([1, 1]).agg(foo=('A', lambda x: x.max()), bar=('A', lambda x: x.min()))

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-58-5b7e2c8bacf8> in <module>
      3 df = pd.DataFrame({"A": [1, 2]})
      4 
----> 5 df.groupby([1, 1]).agg(foo=('A', lambda x: x.max()), bar=("A", lambda x: x.min()))

~\AppData\Local\Continuum\anaconda3\envs\insight\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, arg, *args, **kwargs)
   1453     @Appender(_shared_docs["aggregate"])
   1454     def aggregate(self, arg=None, *args, **kwargs):
-> 1455         return super().aggregate(arg, *args, **kwargs)
   1456 
   1457     agg = aggregate

~\AppData\Local\Continuum\anaconda3\envs\insight\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
    262 
    263         if relabeling:
--> 264             result = result[order]
    265             result.columns = columns
    266 

~\AppData\Local\Continuum\anaconda3\envs\insight\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   2979             if is_iterator(key):
   2980                 key = list(key)
-> 2981             indexer = self.loc._convert_to_indexer(key, axis=1, raise_missing=True)
   2982 
   2983         # take() does not accept boolean indexers

~\AppData\Local\Continuum\anaconda3\envs\insight\lib\site-packages\pandas\core\indexing.py in _convert_to_indexer(self, obj, axis, is_setter, raise_missing)
   1269                 # When setting, missing keys are not allowed, even with .loc:
   1270                 kwargs = {"raise_missing": True if is_setter else raise_missing}
-> 1271                 return self._get_listlike_indexer(obj, axis, **kwargs)[1]
   1272         else:
   1273             try:

~\AppData\Local\Continuum\anaconda3\envs\insight\lib\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
   1076 
   1077         self._validate_read_indexer(
-> 1078             keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
   1079         )
   1080         return keyarr, indexer

~\AppData\Local\Continuum\anaconda3\envs\insight\lib\site-packages\pandas\core\indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
   1161                 raise KeyError(
   1162                     "None of [{key}] are in the [{axis}]".format(
-> 1163                         key=key, axis=self.obj._get_axis_name(axis)
   1164                     )
   1165                 )

KeyError: "None of [MultiIndex([('A', '<lambda>'),\n            ('A', '<lambda>')],\n           )] are in the [columns]"

Problem description

When using the new groupby aggregation with relabeling API in pandas 0.25.0, a KeyError is raised when the same source column is used with multiple lambdas, as in the example above. This issue isn't present when using multiple lambdas with SeriesGroupBy, as in the release notes.

@TomAugspurger notes also that in DataFrameGroupby.aggregate, order needs to be mangled too.

Expected Output

Out[3]:
   foo  bar
1    2    2

Bonus related issue

If the applied function has the same name, a SpecificationError is raised with the message Function names must be unique, found multiple named mean, even though the kwargs are different:

df.groupby([1, 1]).agg(mean=('A', 'mean'), another_mean=('A', 'mean'))

(Obviously this is a silly example, but I encountered it having defined a closure for np.percentile to get around the lambda issue!)

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None

pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : 2.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.3
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None

[TomAugspurger]

We would either need to mangle both func and order here:

pandas/pandas/core/groupby/generic.py

Line 220 in 86049dd

func, columns, order = _normalize_keyword_aggregation(kwargs)

, or mangle order here:

pandas/pandas/core/groupby/generic.py

Lines 263 to 265 in 86049dd

	if relabeling:
	result = result[order]
	result.columns = columns

.

Note that the mangling will be a bit different, since we're mangling names (strings), not the names of lambda functions.

[TomAugspurger]

We may also be able to do the mangling earlier on, so that the column and order are done on the mangled names.

[akdor1154]

I think I am still seeing this in 0.25.3, the fix should be in that right?