Fix type annotations for fill_nan()

[lorenzwalthert]

I added the types as suggested in #3066 (comment) for series, data frame and expression. tested it but for some reason, when comparing series and expressions with their reference, I could not get them to be equal. I had to convert to a tuple to pass the equality check. E.g. check this:

import polars as pl
lazy = pl.DataFrame({"a": [1.0, np.nan, 3.0]}).lazy().fill_nan(None).collect()["a"]
assert lazy.series_equal(pl.Series("a", [1.0, None, 3.0]))

They have the same printed output but they are not identical.
For eager, it works:

lazy = pl.DataFrame({"a": [1.0, np.nan, 3.0]}).fill_nan(None)
assert lazy.frame_equal(pl.DataFrame({"a": [1.0, None, 3.0]}))

Not sure the comparison method needs to be adapted too, but I considered that out of scope for this PR.

[matteosantama]

If you don't convert to tuple what is the mismatch? Is it a datatype problem?

[lorenzwalthert]

The print methods are identical, but comparision with frame_assert_equal() yields not identical. So I don't really understand. You can also c/p the example I posted in my initial PR description to see the problem and maybe you'll find a hint...

[matteosantama]

Ah, I see. This is a problem with Series.series_equal.

In [25]: s = pl.Series("a", [1.0, None, 3.0])

In [26]: s.series_equal(s)
Out[26]: False

I will open a separate issue.

[matteosantama]

@lorenzwalthert Series.series_equal has a null_equal parameter that defaults to False. Can you change the assert statement to

assert (
        df.lazy()
        .fill_nan(None)
        .collect()["a"]
        .series_equal(pl.Series("a", [1.0, None, 3.0]), null_equal=True)
    )

[lorenzwalthert]

Ups of course. Did not know that, thanks.

[lorenzwalthert]

So if null_equal defaults to True for data frames in frame_equal(), shouldn't it also have the same default value for polars.series?

[ritchie46]

Yes, it should. That's more consistent.

[ritchie46]

Thanks @lorenzwalthert 👍