BUG: `DataFrame.groupby with axis=1 is deprecated.` <-- please do not deprecate it

[johentsch]

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Reproducible Example

import pandas as pd

df = pd.DataFrame.from_dict(
    {
        ('column_1', 'a'): {
            ('group1', 'group2', 0): '01a',
            ('group1', 'group2', 1): '11a',
            ('group1', 'group2', 2): '21a'
        },
        ('column_1', 'b'): {
            ('group1', 'group2', 0): '01b',
            ('group1', 'group2', 1): '11b',
            ('group1', 'group2', 2): '21b'
        },
        ('column_2', 'a'): {
            ('group1', 'group2', 0): '02a',
            ('group1', 'group2', 1): '12a',
            ('group1', 'group2', 2): '22a'
        },
        ('column_2', 'b'): {
            ('group1', 'group2', 0): '02b',
            ('group1', 'group2', 1): '12b',
            ('group1', 'group2', 2): '22b'
        }}
       
)
desired_result = df.groupby(level=0, axis=1).apply(lambda df: df.apply(tuple, axis=1)) # semantically chrystal-clear, why deprecate?
should_be_equivalent = df.T.groupby(level=0).apply(lambda df: df.apply(tuple)).T # has one column level more, not less
this_works = df.T.groupby(level=0).apply(lambda df: df.apply(tuple, result_type="reduce")).T
desired_result.equals(should_be_equivalent) # False
desired_result.equals(this_works)           # True

Issue Description

Related to #51203, #51778 where people fail to see the use of .groupby(axis=1). These issues fail to name good reasons for deprecating this that could counter this: It should be retained for API consistency and for backward compatibility. It does not matter if transforms are happening under the hood, as a user I don't care. I care about consistency and being able to use my code in the future.

So here comes the problem: Here's a dataframe with two-by-two multicolumns, four columns in total. The aim is to eliminate the second level and to end up with only two columns in total (according to the first level) that should contain tuples being pairs which correspond to a combination of the two squashed columns from the second level (desired_result). The problem is, when, instead of grouping columns and applying tuple to rows, I transform, group, and apply tuple to all colums, the output of tuple, by default, will be interpreted as a transformation of the column (corresponding to result_type="expand" behaviour) and not as a "squashed" tuple that would get rid of the second, now-index level (corresponding to result_type="reduce" behaviour). That is, I still end up with four columns. Even worse, the fact that group_keys=True by default results in an additional column level duplicating the original first one.

As shown above, the fix is to include .apply(tuple, result_type="reduce"). In this case, no need to include group_keys=False to avoid the duplicated column level.

So my plea, again: Please do not deprecate .groupby(axis=1). Rather, seek to make the behaviours consistent.

Installed Versions

INSTALLED VERSIONS

commit : 2a953cf
python : 3.10.13.final.0
python-bits : 64
OS : Linux
OS-release : 6.1.62-1-MANJARO
Version : #1 SMP PREEMPT_DYNAMIC Thu Nov 9 03:01:44 UTC 2023
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.3
numpy : 1.26.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 69.0.2
pip : 23.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : 5.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.18.0
pandas_datareader : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.2
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.11.4
sqlalchemy : 2.0.23
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

...lib/python3.10/site-packages/_distutils_hack/init.py:33: UserWarning:

Setuptools is replacing distutils.

[MarcoGorelli]

this_works = df.T.groupby(level=0).apply(lambda df: df.apply(tuple, result_type="reduce")).T

this looks fine to be honest - transposing is expensive and shouldn't be done under-the-hood in my opinion

[johentsch]

transposing is expensive and shouldn't be done under-the-hood in my opinion

Then the best solution for this issue is to implement a performant .groupby(axis=1) without transform, rather than pretending this is some esoteric use-case in which users just go figure with their transforms, opaque default behaviours of .apply() and what not. In my opinion, #51203 invokes good arguments to keep this.

[rhshadrach]

Thanks for the feedback here! Though we may disagree, it's important to hear from users on issues such as this.

Then the best solution for this issue is to implement a performant .groupby(axis=1) without transform

I don't see how that's possible short of adding a whole new implementation for groupby just for axis=1. The current implementation is already a large maintenance burden, and this would exacerbate the issue.

The problem is, when, instead of grouping columns and applying tuple to rows, I transform, group, and apply tuple to all colums, the output of tuple, by default, will be interpreted as a transformation of the column

You should be able to use .groupby(...).agg here to accomplish what you want, but currently it works the same as .apply. We've laid the ground work to fix this in pandas 3.0 as part of the 2.x series, I hope to tackle it in the future.

rather than pretending this is some esoteric use-case

I don't think this helps the conversation. My view is that it sees much less usage compared to axis=0 in groupby and that the maintenance burden is not worth supporting this feature. But I do acknowledge (as I did in #51203) that there are benefits to keeping axis=1.

[johentsch]

Thank you @rhshadrach! I'll go with the two-fold transposing solution for now, even though it cramps my Zen. Pandas is at the core of a data analysis library I'm developing and it uses MultiIndex for every index and for columns a fair bit, too. In terms of usability it makes a significant difference to be able to group by column levels, so I'm glad as long as y'all be putting up with the maintenance burden of it. 🙏

[chubukov]

Thanks for the feedback here! Though we may disagree, it's important to hear from users on issues such as this.

I will just add two cents that groupby(axis=1) is a common use case for me. The grouping variable is typically either a level of a MultiIndex or an external Series. To be fair, it is mostly syntactic sugar, and I suspect I would be able to do everything with a combination of explicit for loops and concat. My number of columns is generally small, so I doubt I am taking advantage of performant implementations.

Is there a recommended compact equivalent to for i,g in df.groupby(level=level_name,axis=1):? I have come up with many alternatives, but they're all very ugly.

[phofl]

@chubukov

Why can't you just transpose your DataFrame?