"SpecificationError: nested dictionary is ambiguous in aggregation" in a certain case of groupby-aggregation

All of these examples for using agg work fine:

import pandas as pd
df = pd.DataFrame({"A":['A','A','B','B','B'],
                   "B":[1,2,1,1,2],
                   "C":[9,8,7,6,5]})
df.groupby('A')[['B','C']].agg({'B':'sum','C':'count'})
df.groupby('A')[['B','C']].agg({'B':['sum','count'],'C':'count'})
df.groupby('A')[['B']].agg('sum')
df.groupby('A')['B'].agg('sum')

This one throws a future warning as mentioned here:

df.groupby('A')['B'].agg({'B':['sum','count']})

This one works just fine:

df.groupby('A')[['B','C']].agg({'B':'sum'})

But this one throws an error (I’m aware this expression isn’t necessary):

df.groupby('A')[['B']].agg({'B':'sum'})

SpecificationError: nested dictionary is ambiguous in aggregation

Why does it throw this error?

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None python: 3.6.8.final.0 python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: None.None

pandas: 0.24.1 pytest: 3.9.1 pip: 19.0.1 setuptools: 40.8.0 Cython: 0.29.5 numpy: 1.15.4 scipy: 1.2.0 pyarrow: None xarray: None IPython: 7.2.0 sphinx: 1.8.4 patsy: 0.5.1 dateutil: 2.7.5 pytz: 2018.9 blosc: None bottleneck: 1.2.1 tables: 3.4.4 numexpr: 2.6.9 feather: None matplotlib: 3.0.2 openpyxl: 2.6.0 xlrd: 1.2.0 xlwt: 1.3.0 xlsxwriter: 1.1.2 lxml.etree: 4.3.1 bs4: 4.7.1 html5lib: 1.0.1 sqlalchemy: 1.2.18 pymysql: None psycopg2: None jinja2: 2.10 s3fs: None fastparquet: 0.2.1 pandas_gbq: None pandas_datareader: None gcsfs: None

[stuarteberg]

I can confirm in pandas 0.25.0.

These work fine:

In [27]: df = pd.DataFrame({'x': [1,1,2], 'y': [3,4,4]})

In [28]: df
Out[28]:
   x  y
0  1  3
1  1  4
2  2  4

In [29]: df.groupby('x').agg({'x': 'sum'})
Out[29]:
   x
x
1  2
2  2

In [30]: df.groupby('y').agg({'x': 'sum'})
Out[30]:
   x
y
3  1
4  3

But this raises an error:

In [31]: df.groupby('y')[['x']].agg({'x': 'sum'})

...

SpecificationError: nested dictionary is ambiguous in aggregation

And the .agg() call only requires a single column, but you provide more than one column when you select columns from the groupby, then you get a weird result. (Why does the result below include a y column at all?)

In [34]: df.groupby('y')[['x', 'y']].agg({'x': 'sum'})
Out[34]:
   x
   x  y
y
3  1  3
4  3  8

[talukder-sowrov]

Take