feat!: Removes inplace option for `pandas.core.resample.Resampler.i…

[cbpygit]

…nterpolate`. Fixes / cleans up related test cases.

• closes BUG: In main, using resample().interpolate(inplace=True) raises an exception #58690 (Replace xxxx with the GitHub issue number)
• Tests added and passed if fixing a bug or adding a new feature
• All code checks passed.
• Added type annotations to new arguments/methods/functions.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.

[cbpygit]

@MarcoGorelli somehow it did not auto-assign the reviewers. But in any case, here we are :)

[Aloqeely]

Do these need to be deprecated first? You can use the deprecate_kwarg decorator for that

[cbpygit]

Do these need to be deprecated first? You can use the deprecate_kwarg decorator for that

I don't know what the exact policy is. But as far as I understand the deprecation tells the user that a certain future change will change the API. This does not apply here, as the inplace-functionality is already broken. We would need to add the deprecation to pandas 2 to inform users about the upcoming change, and to ease migration. But I guess this would need a separate PR that targets the next 2.x release.

[MarcoGorelli]

Do these need to be deprecated first?

Thanks @Aloqeely - in general, yes, this would require a deprecation. However, here, the change is going to breaking anyway (in order to fix a historic feature bug), so to be honest I'd be OK removing inplace without warning too

[cbpygit]

@mroeschke can we merge this? 😄

[mroeschke]

It appears that a lot of interpolate usages are still modified in the PR. Only tests that used to call.resample(...).interpolate(interpolate=) should have been changed

[cbpygit]

@mroeschke Sorry for the long delay. I was checking this now again and I think we need to clear up the misunderstanding. The wrong behavior is not just related to cases that use .resample(...). See this minimal example (adjusted from the test case I fixed in the other PR):

from pandas import DataFrame, testing as tm, Timestamp
import numpy as np

df = DataFrame(
    {
        "A": [1, 2, np.nan, 4],
        "B": [1, 4, 9, np.nan],
        "C": [Timestamp("2020-08-01 00:00:01"), Timestamp("2020-08-01 00:00:02"), Timestamp("2020-08-01 00:00:03"), Timestamp("2020-08-01 00:00:05")],
        "D": list("abcd"),
    }
)

result = df.set_index("C").interpolate()
expected = df.set_index("C")
expected.loc[Timestamp("2020-08-01 00:00:03"), "A"] = 2.66667
expected.loc[Timestamp("2020-08-01 00:00:05"), "B"] = 9
tm.assert_frame_equal(result, expected)

In pandas 2.* this fails with

---------------------------------------------------------------------------
AssertionError: DataFrame.iloc[:, 0] (column name="A") are different

DataFrame.iloc[:, 0] (column name="A") values are different (25.0 %)
[index]: [2020-08-01T00:00:01.000000000, 2020-08-01T00:00:02.000000000, 2020-08-01T00:00:03.000000000, 2020-08-01T00:00:05.000000000]
[left]:  [1.0, 2.0, 3.0, 4.0]
[right]: [1.0, 2.0, 2.66667, 4.0]
At positional index 2, first diff: 3.0 != 2.66667

This is not using resample. Can you please clarify?

[github-actions]

This pull request is stale because it has been open for thirty days with no activity. Please update and respond to this comment if you're still interested in working on this.

[cbpygit]

This pull request is stale because it has been open for thirty days with no activity. Please update and respond to this comment if you're still interested in working on this.

I'll work on this, I was just unable to spend time.

[mroeschke]

Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen.