DataFrame Groupby Named Aggregation Using Lambda Results in a KeyError, Dict-of-Dicts Approach Works Fine

[tbarket]

Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd
data = [['tom', 10, 'blue', 1000, 'a'], ['nick', 15, 'blue', 2000, 'b'], ['julie', 14, 'green', 3000, 'a'], ['bob', 11, 'green', 4000, 'a'], ['cindy', 16, 'red', 5000, 'b']]
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Color', 'Num', 'Letter'])

# Dict-style renaming works fine:
df.groupby(by='Color').agg({'Num': {'SumNum'  : np.sum, 'SumNumIfLetterA': lambda x: x[df.iloc[x.index].Letter=='a'].sum()}})

Out[2]: 
         Num                
      SumNum SumNumIfLetterA
Color                       
blue    3000            1000
green   7000            7000
red     5000               0

# Named Aggregation using function also works fine:
def simplefunc(x):
    return x[df.iloc[x.index].Letter=='a'].sum()
df.groupby(by='Color').agg(SumNum = ('Num', np.sum), SumNumIfLetterA = ('Num', simplefunc))

Out[3]: 
       SumNum  SumNumIfLetterA
Color                         
blue     3000             1000
green    7000             7000
red      5000                0

# Named Aggregation using identical lambda throws a KeyError:
df.groupby(by='Color').agg(SumNum = ('Num', np.sum), SumNumIfLetterA = ('Num', lambda x: x[df.iloc[x.index].Letter=='a'].sum()))

Problem description

A lambda function that previously worked fine (using a Dict-of-Dict) no longer works with Named Aggregation. Using a function with Named Aggregation works fine but replacing the function with an identical lambda does not work with Named Aggregation. Is this expected behavior or it is a bug?

Expected Output

       SumNum  SumNumIfLetterA
Color                         
blue     3000             1000
green    7000             7000
red      5000                0

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : None python : 3.7.4.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 158 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 : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8

[jorisvandenbossche]

@TomAugspurger I thought you did a follow-up PR (after the named aggregation PR) to deal with lambdas ?

[jorisvandenbossche]

From #27519, it seems lambdas already work, but only if that is the only function for that specific column.

So this works:

In [34]: df.groupby(by='Color').agg(SumNumIfLetterA = ('Num', lambda x: x[df.iloc[x.index].Letter=='a'].sum()))                                                                               
Out[34]: 
       SumNumIfLetterA
Color                 
blue              1000
green             7000
red                  0

but once you include an aggregation for the same column (with a lambda function or not) it errors.

[TomAugspurger]

Duplicate of #27519 I think.

[TomAugspurger]

@tbarket let us know if you're interested in working on that.