GroupBy(axis=1) Does Not Offer Implicit Selection By Columns Name(s) · Issue #27614 · pandas-dev/pandas (original) (raw)

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