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Handle overloaded method ambiguity caused by self-types - #22011

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ilevkivskyi:overload-self-amb
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Handle overloaded method ambiguity caused by self-types#22011
ilevkivskyi wants to merge 5 commits into
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ilevkivskyi:overload-self-amb

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@ilevkivskyi ilevkivskyi commented Sep 20, 2026

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Fixes #11347
Closes #17239

I think we should handle this consistently with overloaded functions, essentially foo.meth(x) should behave the same as Foo.meth(foo, x). Although this is a relatively niche situation, it seems to be important for some numerical libraries.

It looks like there are no simple way to implement this properly. The only way I see requires a new attribute on Overloaded and a lot of plumbing (form binging site to the call site). I tried to apply various optimizations to reduce possible performance impact.

cc @JukkaL @hauntsaninja

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ilevkivskyi commented Sep 20, 2026

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The fallout in colour is likely because this uncovers some other bug. Normally inferring more Anys should never cause problems. I will take a look at this later.

What is more concerning, is all the test failure in pandas-stubs and scipy-stubs. @jorenham You mentioned in #18343 (comment) that #11347 negatively affects some numerical libraries, but from what I see, it may be not what you actually want. Looking at some test failures and corresponding definitions this PR behaves as expected. For example:

tests/spatial/test__rotation.pyi:115: error: Expression is of type "ndarray[Any, Any]", not "ndarray[tuple[Any, ...], dtype[float64]]"  [assert-type]

Relevant line

assert_type(_rot_nd.as_quat(), onp.ArrayND[np.float64])

relevant definition

    @overload
    def as_quat(
        self: Rotation[_JustAnyShape], /, canonical: bool = False, *, scalar_first: bool = False
    ) -> onp.ArrayND[np.float64]: ...
    @overload
    def as_quat(
        self: Rotation[tuple[()]], /, canonical: bool = False, *, scalar_first: bool = False
    ) -> onp.Array1D[np.float64]: ...
    @overload
    def as_quat(
        self: Rotation[tuple[int]], /, canonical: bool = False, *, scalar_first: bool = False
    ) -> onp.Array2D[np.float64]: ...
    @overload
    def as_quat(self, /, canonical: bool = False, *, scalar_first: bool = False) -> onp.ArrayND[np.float64]: ...

and

_rot_nd: Rotation

So Rotation is the same as Rotation[tuple[Any, ...]] (due to type variable default). Then since Rotation[tuple[Any, ...]] is a subtype of several self-types here (Rotation is covariant and tuple[Any, ...] is a subtype of tuple[int] etc). Therefore multiple overloads match. Therefore now (with this PR) we identify this as "ambiguous overload match caused by Any", and correctly use erased return type.

@jorenham Could you please comment on which overload(s) you expect to match here and why?

@jorenham

jorenham commented Sep 20, 2026 •
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The first overload with Rotation[_JustAnyShape] is just a workaround for pyright, which eagerly selects the first overload in case of unknown shape-type. The last overload is the fallback overload, which intentionally has the same gradual-shape-typed result type as the first. This last overload returns onp.ArrayND[np.float64], which is assignable to all of the other overloads' return types. So according to the typing spec, using onp.ArrayND[np.float64] as return type for Rotation[tuple[Any, ...]] would be sound, and would match my intention.

You might also be interested in a recent similar discussion for Pyrefly: facebook/pyrefly#4910 (comment)

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ilevkivskyi commented Sep 20, 2026

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@jorenham Specific fallback type may be more precise, this is true. Mypy already infers ndarray[Any, Any] instead of plain Any, but better fallback logic may be (very) hard to specify in general.

Anyway, my question is different: you mentioned that the bug I am fixing here (where bug == mypy randomly selecting first overload in case if Any-ambiguity is caused by self-types) causes problems form numerical libraries. But from what I see, this PR only makes things worse (at least at first glance), so my question is do you actually want/need this?

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jorenham commented Sep 20, 2026

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But from what I see, this PR only makes things worse (at least at first glance), so my question is do you actually want/need this?

Well, in the meantime I've worked around most of these cases where possible in NumPy's bundled stubs and in scipy-stubs (e.g. using the tuple[Never, Never, ...] trick in numpy/numpy#29218).

But it looks like this solution results in many Anys to be inferred, more than necessary (typing-spec-wise, see my previous comment). Maybe that's worth it, but that's hard to tell because the primer diff doesn't show the cases where my workarounds can now be removed, and at this point I'm not sure if those new Any inferences can be worked around yet.

The number of new primer errors for scipy-stubs isn't all too bad, so I can live with it. But the diff for colour looks a bit more worrying, and I can't speak for its maintainers. I'm kinda suprised that numpy isn't in the primer diff though, but I guess that's good sign? The new unused-ignore and red diff lines show that this is at least doing something right, so at the very least you're on the right track with this.

Anyway, don't consider scipy-stubs a blocker for this. I've seen those ndarray[Any, Any] before in other contexts in numpy and scipy-stubs, so improving those (i.e. maning them more precise and less Any-ish) is probably another topic altogether I'm guessing.

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ilevkivskyi commented Sep 20, 2026 •
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@jorenham
About color: after a brief look at the relevant code there I think I know what is going to on. Mypy has a special case in narrowing logic: it never narrows an annotated variable to a plain Any. So now that we infer more plain Anys, some cases that previously worked, will stop working. For the record I didn't like this special case (I still think mypy should follow SSA as close as possible), but caved to the peer pressure. cc @cdce8p and @JukkaL who said that not having this special case will cause too many false negatives.

Now back to the main topic

Anyway, don't consider scipy-stubs a blocker for this.

Note pandas-stubs also has a lot of new errors. In general it seems to me we may first need to find a way to find better fallback type in case of ambiguity. For example, one may check if one of the return types is both subtype and supertype of all other return types, like onp.ArrayND[np.float64] in the example we discussed. Such logic is however quadratic in number of matches, so we may need to put some arbitrary limit (which is not something new for overloads btw, several things related to overloads are quadratic and need artificial cut-offs).

This was referenced Sep 20, 2026
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ilevkivskyi added a commit that referenced this pull request Sep 28, 2026
@ilevkivskyi
Limit binder special-casing to imprecise Any (#22050)
41ddcdb 
There is a bunch of special-casing for `Any` types in binder. The goal
of this special-casing is to limit the spread of `Any` types from legacy
code with missing/imprecise types. This special-casing is quite ad-hoc,
and thus should be limited only to imprecise kinds of `Any`.

Note this should help with #22011
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ilevkivskyi commented Sep 28, 2026

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OK, mypy_primer looks broken, but I think I have a fix hauntsaninja/mypy_primer#266

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ilevkivskyi commented Sep 29, 2026

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OK, primer now looks better. The pandas-stubs and scipy-stubs fallout will be hopefully reduced by #22060

ilevkivskyi added a commit that referenced this pull request Sep 30, 2026
@ilevkivskyi
Find precise fallback for ambiguous Any overloads (#22060)
b5b279f 
It is a common pattern (especially in numeric libraries) to have a
fallback overload that has a relatively precise return type (i.e. not
just `Any` or `list[Any]`). We should try to find and use that overload
if there is an ambiguity caused by an argument that contains `Any`.

This will likely cause some new errors, but this should limit the
fallout from #22011
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Diff from mypy_primer, showing the effect of this PR on open source code:

colour (https://github.com/colour-science/colour)
- colour/models/rgb/transfer_functions/log.py:240: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]]", variable has type "ndarray[tuple[Any, ...], dtype[float64]]")  [assignment]
+ colour/appearance/llab.py:411: error: Argument "a" to "CAM_Specification_LLAB" has incompatible type "floating[_16Bit] | floating[_32Bit] | float64"; expected "float | ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]] | None"  [arg-type]
+ colour/appearance/llab.py:412: error: Argument "b" to "CAM_Specification_LLAB" has incompatible type "floating[_16Bit] | floating[_32Bit] | float64"; expected "float | ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]] | None"  [arg-type]
+ colour/appearance/atd95.py:305: error: Argument "A_1" to "CAM_Specification_ATD95" has incompatible type "floating[_16Bit] | floating[_32Bit] | float64"; expected "float | ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]] | None"  [arg-type]
+ colour/appearance/atd95.py:306: error: Argument "T_1" to "CAM_Specification_ATD95" has incompatible type "floating[_16Bit] | floating[_32Bit] | float64"; expected "float | ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]] | None"  [arg-type]
+ colour/appearance/atd95.py:307: error: Argument "D_1" to "CAM_Specification_ATD95" has incompatible type "floating[_16Bit] | floating[_32Bit] | float64"; expected "float | ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]] | None"  [arg-type]
+ colour/appearance/atd95.py:308: error: Argument "A_2" to "CAM_Specification_ATD95" has incompatible type "floating[_16Bit] | floating[_32Bit] | float64"; expected "float | ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]] | None"  [arg-type]
+ colour/appearance/atd95.py:309: error: Argument "T_2" to "CAM_Specification_ATD95" has incompatible type "floating[_16Bit] | floating[_32Bit] | float64"; expected "float | ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]] | None"  [arg-type]
+ colour/appearance/atd95.py:310: error: Argument "D_2" to "CAM_Specification_ATD95" has incompatible type "floating[_16Bit] | floating[_32Bit] | float64"; expected "float | ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]] | None"  [arg-type]
- colour/models/hdr_ipt.py:145: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[float64]]", variable has type "float")  [assignment]
- colour/models/hdr_ipt.py:147: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[float64]]", variable has type "float")  [assignment]
- colour/models/hdr_ipt.py:149: error: Incompatible return value type (got "float", expected "ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]]")  [return-value]
- colour/models/hdr_cie_lab.py:142: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[float64]]", variable has type "float")  [assignment]
- colour/models/hdr_cie_lab.py:144: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[float64]]", variable has type "float")  [assignment]
- colour/models/hdr_cie_lab.py:146: error: Incompatible return value type (got "float", expected "ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]]")  [return-value]
- colour/colorimetry/illuminants.py:226: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]]", variable has type "ndarray[tuple[Any, ...], dtype[float64]]")  [assignment]
- colour/colorimetry/illuminants.py:227: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]]", variable has type "ndarray[tuple[Any, ...], dtype[float64]]")  [assignment]
+ colour/notation/munsell/centore2014.py:434: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]]", variable has type "floating[_16Bit] | floating[_32Bit] | float64")  [assignment]
+ colour/notation/munsell/centore2014.py:435: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]]", variable has type "floating[_16Bit] | floating[_32Bit] | float64")  [assignment]
- colour/notation/munsell/centore2014.py:714: error: Argument 1 to "hue_angle_to_hue" has incompatible type "ndarray[tuple[Any, ...], dtype[floating[Any]]]"; expected "float"  [arg-type]
+ colour/notation/munsell/centore2014.py:714: error: Argument 1 to "hue_angle_to_hue" has incompatible type "ndarray[tuple[Any, ...], dtype[float64]]"; expected "float"  [arg-type]
- colour/notation/munsell/centore2014.py:741: error: Argument 1 to "append" of "list" has incompatible type "ndarray[tuple[Any, ...], dtype[floating[Any]]]"; expected "float"  [arg-type]
+ colour/notation/munsell/centore2014.py:741: error: Argument 1 to "append" of "list" has incompatible type "ndarray[tuple[Any, ...], dtype[float64]]"; expected "float"  [arg-type]
- colour/notation/munsell/centore2014.py:742: error: Argument 1 to "append" of "list" has incompatible type "ndarray[tuple[Any, ...], dtype[floating[Any]]]"; expected "int"  [arg-type]
+ colour/notation/munsell/centore2014.py:742: error: Argument 1 to "append" of "list" has incompatible type "ndarray[Any, dtype[Any]]"; expected "int"  [arg-type]

zulip (https://github.com/zulip/zulip)
- zerver/lib/attachments.py:182: error: "Message" has no attribute "recipient_id"  [attr-defined]
- zerver/actions/user_groups.py:130: error: Argument 1 to "list" has incompatible type "QuerySet[UserProfile, dict[str, Any]]"; expected "Iterable[MemberGroupUserDict]"  [arg-type]
- zerver/actions/user_groups.py:135: error: Argument 1 to "list" has incompatible type "QuerySet[UserProfile, dict[str, Any]]"; expected "Iterable[MemberGroupUserDict]"  [arg-type]
- zerver/actions/user_groups.py:141: error: Argument 1 to "list" has incompatible type "QuerySet[UserProfile, dict[str, Any]]"; expected "Iterable[MemberGroupUserDict]"  [arg-type]
- zerver/actions/user_groups.py:144: error: Argument 1 to "list" has incompatible type "QuerySet[UserProfile, dict[str, Any]]"; expected "Iterable[MemberGroupUserDict]"  [arg-type]

pandas-stubs (https://github.com/pandas-dev/pandas-stubs)
+ tests/test_windowing.py:48: error: Returning Any from function declared to return "float"  [no-any-return]
+ tests/test_resampler.py:39: error: Returning Any from function declared to return "float"  [no-any-return]
+ tests/test_groupby.py:79: error: Returning Any from function declared to return "float"  [no-any-return]
+ tests/series/test_truediv.py:149: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_truediv.py:152: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_truediv.py:155: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_truediv.py:195: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_truediv.py:205: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_truediv.py:241: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_truediv.py:261: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_truediv.py:272: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_sub.py:263: error: Expression is of type "Any", not "Series[Timedelta]"  [assert-type]
+ tests/series/test_mul.py:80: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_mul.py:81: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_mul.py:82: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_mul.py:83: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_floordiv.py:64: error: Expression is of type "Series[Any]", not "Series[Timedelta]"  [assert-type]
+ tests/series/test_floordiv.py:124: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_floordiv.py:125: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_floordiv.py:126: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_floordiv.py:135: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_floordiv.py:171: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_floordiv.py:180: error: Expression is of type "Series[Any]", not "Series[Timedelta]"  [assert-type]
+ tests/series/test_floordiv.py:203: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_floordiv.py:211: error: Expression is of type "Series[Any]", not "Series[Timedelta]"  [assert-type]
+ tests/series/test_floordiv.py:221: error: Expression is of type "Any", not "Never"  [assert-type]
+ tests/series/test_floordiv.py:230: error: Expression is of type "Series[Any]", not "Series[Timedelta]"  [assert-type]
+ tests/series/test_floordiv.py:277: error: Expression is of type "Series[Any]", not "Series[Timedelta]"  [assert-type]
+ tests/series/test_agg.py:21: error: Expression is of type "Any", not "float"  [assert-type]
+ tests/series/test_agg.py:26: error: Expression is of type "Any", not "float"  [assert-type]
+ tests/series/test_agg.py:31: error: Expression is of type "Any", not "float"  [assert-type]
+ tests/series/test_agg.py:36: error: Expression is of type "Any", not "float"  [assert-type]
+ tests/series/test_add.py:87: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_add.py:88: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_add.py:89: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_add.py:90: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/indexes/test_truediv.py:61: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_sub.py:27: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_sub.py:32: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_sub.py:42: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_sub.py:47: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_mul.py:60: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_mul.py:62: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_mul.py:63: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_floordiv.py:84: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_floordiv.py:85: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_floordiv.py:86: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_add.py:60: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_add.py:61: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_add.py:62: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/indexes/test_add.py:63: error: Expression is of type "Any", not "Index[Any]"  [assert-type]
+ tests/test_timefuncs.py:1824: error: Expression is of type "Series[Any]", not "Series[Timestamp]"  [assert-type]
+ tests/test_pandas.py:1702: error: Returning Any from function declared to return "float"  [no-any-return]
+ tests/test_pandas.py:2017: error: Returning Any from function declared to return "float"  [no-any-return]
+ tests/frame/test_groupby.py:640: error: Returning Any from function declared to return "float"  [no-any-return]
+ tests/frame/test_frame.py:1154: error: Expression is of type "Any", not "ndarray[tuple[int], dtype[Any]]"  [assert-type]
+ tests/series/test_series.py:682: error: Expression is of type "Any", not "floating[Any]"  [assert-type]
+ tests/series/test_series.py:2174: error: Expression is of type "ndarray[Any, Any]", not "ndarray[tuple[int], dtype[Any]]"  [assert-type]
+ tests/series/test_series.py:3604: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_series.py:3614: error: Expression is of type "Any", not "Series[Any]"  [assert-type]
+ tests/series/test_series.py:3623: error: Expression is of type "Any", not "Series[Any]"  [assert-type]

scikit-learn (https://github.com/scikit-learn/scikit-learn)
- sklearn/linear_model/tests/test_ransac.py:26: error: Argument 1 to "__iadd__" of "ndarray" has incompatible type "ndarray[tuple[Any, ...], dtype[float64]]"; expected "_SupportsArray[dtype[numpy.bool[builtins.bool] | integer[Any]]] | _NestedSequence[_SupportsArray[dtype[numpy.bool[builtins.bool] | integer[Any]]]] | int | _NestedSequence[int]"  [arg-type]
- sklearn/linear_model/tests/test_ransac.py:28: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[number[Any, float]]]", variable has type "ndarray[tuple[int], dtype[signedinteger[_32Bit | _64Bit]]]")  [assignment]
- sklearn/linear_model/tests/test_ransac.py:29: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[number[Any, float]]]", variable has type "ndarray[tuple[Any, ...], dtype[floating[Any]]]")  [assignment]

pandas (https://github.com/pandas-dev/pandas)
+ pandas/core/util/hashing.py:411: error: Unused "type: ignore" comment  [unused-ignore]

xarray (https://github.com/pydata/xarray)
+ xarray/tests/test_typed_ops.py:28: error: Unused "type: ignore" comment  [unused-ignore]
+ xarray/tests/test_typed_ops.py:79: error: Unused "type: ignore" comment  [unused-ignore]

rclip (https://github.com/yurijmikhalevich/rclip)
+ rclip/utils/preprocess.py:44: error: Incompatible return value type (got "ndarray[tuple[Any, ...], dtype[float64]]", expected "ndarray[tuple[Any, ...], dtype[floating[_32Bit]]]")  [return-value]

static-frame (https://github.com/static-frame/static-frame)
+ static_frame/core/rank.py:166: error: Unused "type: ignore" comment  [unused-ignore]

scipy-stubs (https://github.com/scipy/scipy-stubs)
+ tests/spatial/test__rotation.pyi:151: error: Expression is of type "Any", not "float64 | ndarray[tuple[Any, ...], dtype[float64]]"  [assert-type]
+ tests/interpolate/test_interpolate.pyi:43: error: Expression is of type "ndarray[Any, Any]", not "ndarray[tuple[int], dtype[float64]]"  [assert-type]
+ tests/interpolate/test_cubic.pyi:75: error: Expression is of type "ndarray[Any, Any]", not "ndarray[tuple[int], dtype[float64]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:539: error: Expression is of type "Any", not "float64 | ndarray[tuple[Any, ...], dtype[float64]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:564: error: Expression is of type "Any", not "float64 | ndarray[tuple[Any, ...], dtype[float64]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:685: error: Expression is of type "Any", not "float64 | ndarray[tuple[Any, ...], dtype[float64]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:736: error: Expression is of type "ndarray[Any, Any]", not "ndarray[tuple[int, int, *tuple[Any, ...]], dtype[float64]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:746: error: Expression is of type "ndarray[Any, Any]", not "ndarray[tuple[int, int, *tuple[Any, ...]], dtype[float64]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:843: error: Expression is of type "tuple[Any, ...]", not "tuple[float64, float64] | tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:846: error: Expression is of type "tuple[Any, ...]", not "tuple[float64, float64] | tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:850: error: Expression is of type "tuple[Any, ...]", not "tuple[float64, float64] | tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:854: error: Expression is of type "tuple[Any, ...]", not "tuple[float64, float64] | tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:893: error: Expression is of type "Any", not "float64 | ndarray[tuple[Any, ...], dtype[float64]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:907: error: Expression is of type "Any", not "float64 | ndarray[tuple[Any, ...], dtype[float64]]"  [assert-type]
+ tests/stats/test_multivariate.pyi:923: error: Expression is of type "tuple[Any, ...]", not "tuple[float64, float64] | tuple[ndarray[tuple[Any, ...], dtype[float64]], float64] | tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]"  [assert-type]

@ilevkivskyi

ilevkivskyi commented Oct 1, 2026

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@jorenham After I merged #22060 situation with scipy-stubs improved, but there are still a bunch of failures in tests/stats/test_multivariate.pyi Could you please check these? If mypy is doing something wrong there, could you please reduce it to a simple standalone repro?

@Dr-Irv @cmp0xff There are still a lot of new Anys in pandas-stubs. I am trying to understand expectations here. I am looking at one of the failures:

tests/indexes/test_mul.py:60: error: Expression is of type "Any", not "Index[Any]"  [assert-type]

the relevant lines are

def test_mul_numpy_array(left_i: pd.Index) -> None:
    """Test pd.Index[Any] (int) * numpy arrays"""
    b = np.array([True, False, True], np.bool_)
    ...
    check(assert_type(left_i * b, pd.Index), pd.Index)

I looked at the definition of Index.__mul__() at https://github.com/pandas-dev/pandas-stubs/blob/main/pandas-stubs/core/indexes/base.pyi#L861, mypy correctly matches argument types (Index[Any], ndarray[bool]) to three overloads: 0, 5, and 9 (simplified):

(self: Index[Never], other: <long union>) -> Index[Any]
(self: Index[_str], other: <long union>) -> Never
(self: Index[T_COMPLEX], other: np_ndarray_bool | Index[bool]) -> Index[T_COMPLEX]

Because of the Never return type, the only fallback return type due to Any argument ambiguity we can infer is Any. Why do you expect Index[Any] here? Do you expect some special-casing for Never return type in overloads?

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Overload ambiguity is ignored in self-annotated methods

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@ilevkivskyi @jorenham