GroupBy(axis=1) Does Not Offer Implicit Selection By Columns Name(s)

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

df = pd.DataFrame(np.arange(12).reshape(3, 4), index=[0, 1, 0], columns=[10, 20, 10, 20])
df.index.name = "y"
df.columns.name = "x"

print df

print
print "Grouped along index:"
print df.groupby(by="y").sum()

print
print "Grouped along columns:"
# The following raises a KeyError even though  "x" is a column name
# (like "y" above, which is an index name):
df.groupby(by="x", axis=1).sum()

Problem description

The exception at the end is surprising: the intent is clearly to group by columns, on the “x” column label.

Furthermore, the documentation for groupby() seems to confirm this, as it states for the “by” argument that “A str or list of strs may be passed to group by the columns in self”.

Expected Output

A dataframe with index [0, 1, 0] but grouped (and summed) columns [10, 20].

I wasn’t able to test with the latest Pandas version, sorry!

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None python: 2.7.13.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-642.15.1.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None

pandas: 0.21.1 pytest: 3.2.3 pip: 9.0.1 setuptools: 28.8.0 Cython: 0.27.3 numpy: 1.13.3 scipy: 0.19.1 pyarrow: None xarray: 0.8.2 IPython: 5.1.0 sphinx: 1.4.4 patsy: 0.4.1 dateutil: 2.6.1 pytz: 2017.2 blosc: None bottleneck: None tables: 3.4.4 numexpr: 2.6.5 feather: None matplotlib: 2.1.2 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: 4.6.0 html5lib: 0.9999999 sqlalchemy: 1.2.18 pymysql: None psycopg2: None jinja2: 2.8 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None

[topper-123]

I’ve never understood by to only be usable for index names, and because groupby allows axis=1, it would make sense to me that by follows the supplied axis, and not necessarily only follows the index.

So I see this as bug, given that axis=1 should give the same functionality as axis=0, but onlyshould work over the other axis?

[topper-123]

The example that @lebigot shows, should return the same as df.T.groupby(by="x").sum().T and should return

>>> df.T.groupby(by="x").sum().T  # same as df.groupby(by="x", axis=1).sum()
x  10  20
y
0   2   4
1  10  12
0  18  20

Seems like a bug in groupby to me.

@lebigot if you’d want to investigate, that’d be great.