DEPR: DataFrame.groupby(axis=1)

[jbrockmendel]

It isn't obvious to me what you can do with df.groupby(axis=1) that you can't do with df.T.groupby(axis=0). In many cases we do the transpose under the hood anyway, so it isn't saving much/anything perf-wise.

[rhshadrach]

There are only two benefits I see with keeping the axis argument.

1. Wrapping the operation with transpose may be described as a clever trick that is likely not apparent to new users.
2. dtypes aren't coerced to object (I'd guess they are coerced to object internally and then fixed, but I haven't checked)

For 1, this can at least partially be mitigated by adding a section to the User Guide. For 2, users can call infer_objects, although they may not realize the coercion happened.

I don't think either of these justify keeping the axis argument. I'm +1 on deprecating.

[DeaMariaLeon]

I don’t understand why we would remove the axis keyword from the groupby method.
I’m personally using code that groups columns using this option.
Sure, we can transpose, group by the index and transpose again but this is not convenient for the user.
Besides, I fail to see why groupby would be different from the many other methods that have an axis option. Why for instance would we remove it from groupby while keeping it in sum?

[jbrockmendel]

Besides, I fail to see why groupby would be different from the many other methods that have an axis option. Why for instance would we remove it from groupby while keeping it in sum?

The main difference is maintenance burden. DataFrame.sum(axis=1) is pretty simple, the cause of few bugs, and easy to debug when one is found. DataFrameGroupBy with axis=1 is the opposite.

[rhshadrach]

@DeaMariaLeon - how often do you find yourself using axis=1 in groupby? Could you share an example, or some examples? In particular, it'd be good to know how the groups are determined (a list?), the number of groups, and what operations you find useful applying to them.

[DeaMariaLeon]

@rhshadrach here is an example.

It is quite a common need to group by axis=1 when analyzing coorporate financial data. I used to have this: column names as months, rows were the different type of costs. Then I needed to find total costs by quarters and/or years.

This example is a bit different:
groupby example.pdf

[rhshadrach]

Thanks for the example @DeaMariaLeon. In your case, I would also consider separating out the three sets of columns, computing via .sum(axis=1), and then combining via pd.concat([...], axis=1). I agree this is more verbose than what is possible using groupby with axis=1. However, we also need to consider the number of functions that groupby must support, for example, here is an odd one that currently gives incorrect ouptut:

df = pd.DataFrame({'a': [1, 1, 2], 'b': [3, 4, 5], 'c': [6, 7, 8]})
print(df.groupby([0, 0, 1], axis=1).describe())
#      count      mean      std  min  25%  50%  75%  max
# 0 a    3.0  1.333333  0.57735  1.0  1.0  1.0  1.5  2.0
#   b    3.0  4.000000  1.00000  3.0  3.5  4.0  4.5  5.0
# 1 c    3.0  7.000000  1.00000  6.0  6.5  7.0  7.5  8.0

In my opinion, this is not worth the effort it would take to fix. Aside from deprecating axis=1, the alternatives here are not allowing some ops to have axis=1 or just not fixing bugs like these. I find neither of these to be palatable.

[DeaMariaLeon]

Thanks for the explanations @rhshadrach and @jbrockmendel.

[rhshadrach]

For visibility and any thoughts: cc @pandas-dev/pandas-core