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Remove list.__add__ overloads. #14282

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@randolf-scholz randolf-scholz commented Jun 16, 2025 •
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See #14283.

The overloads on list.__add__ lead to divergence of mypy and pyright. Code sample in pyright playground, https://mypy-play.net/?mypy=latest&python=3.12&gist=abf6a8834020af17a16bd8cfb44b2f10

from typing import Any, overload

class ListA[T]:  # emulates builtins list
    @overload
    def __add__(self, other: "ListA[T]", /) -> "ListA[T]": return ListA()
    @overload
    def __add__[S](self, other: "ListA[S]", /) -> "ListA[T | S]": return ListA()
    
    
class ListB[T]:  # without overloads
    def __add__[S](self, other: "ListB[S]", /) -> "ListB[T | S]": return ListB()

                                            # mypy              | pyright                             
reveal_type( list[str]() + list[str]() )    # list[str]         | list[str]          ✅
reveal_type( list[str]() + list[int]() )    # list[str | int]   | list[str | int]    ✅
reveal_type( list[str]() + list[Any]() )    # list[Any]         | list[str]          ❌

reveal_type( ListA[str]() + ListA[str]() )  # ListA[str]        | ListA[str]         ✅
reveal_type( ListA[str]() + ListA[int]() )  # ListA[str | int]  | ListA[str | int]   ✅
reveal_type( ListA[str]() + ListA[Any]() )  # ListA[Any]        | ListA[str]         ❌

reveal_type( ListB[str]() + ListB[str]() )  # ListB[str]        | ListB[str]         ✅
reveal_type( ListB[str]() + ListB[int]() )  # ListB[str | int]  | ListB[str | int]   ✅
reveal_type( ListB[str]() + ListB[Any]() )  # ListB[str | Any]  | ListB[str | Any]   ✅

A comment from 3 years added in #8293 states that

# Overloading looks unnecessary, but is needed to work around complex mypy problems

I want to see the impact on mypy primer, and whether this comment is still valid given that mypy has had several major releases since.

@randolf-scholz randolf-scholz changed the title do not merge Check usefulness of list.__add__ overloads. Jun 16, 2025
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Diff from mypy_primer, showing the effect of this PR on open source code:

meson (https://github.com/mesonbuild/meson)
+ mesonbuild/scripts/gettext.py:54:70: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/utils/universal.py:2365:87: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/cmake/interpreter.py:1061:69: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/cmake/interpreter.py:1199:60: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ run_mypy.py:154:62: error: Unsupported operand types for + ("list[str | Any]" and "list[str]")  [operator]
+ mesonbuild/modules/gnome.py:1548:23: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/modules/gnome.py:1565:77: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/depfixer.py:436:66: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:62:40: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:73:40: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:83:35: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:120:35: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:128:35: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:134:59: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:140:64: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:147:64: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:156:47: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/scripts/coverage.py:172:40: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/modules/_qt.py:453:114: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/modules/_qt.py:474:126: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/modules/_qt.py:872:86: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/modules/_qt.py:916:173: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ mesonbuild/mtest.py:2250:47: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]

pydantic (https://github.com/pydantic/pydantic)
- pydantic/aliases.py:29: error: Incompatible types in assignment (expression has type "list[str]", variable has type "list[int | str]")  [assignment]
- pydantic/aliases.py:29: note: "list" is invariant -- see https://mypy.readthedocs.io/en/stable/common_issues.html#variance
- pydantic/aliases.py:29: note: Consider using "Sequence" instead, which is covariant
- pydantic/aliases.py:29: error: Argument 1 to "list" has incompatible type "tuple[str | int, ...]"; expected "Iterable[str]"  [arg-type]
+ pydantic/aliases.py:29: error: Argument 1 to "list" has incompatible type "tuple[str | int, ...]"; expected "Iterable[int]"  [arg-type]

cloud-init (https://github.com/canonical/cloud-init)
+ cloudinit/config/cc_mounts.py:420: error: Incompatible return value type (got "list[list[str | Any | None]]", expected "list[list[str]]")  [return-value]

zulip (https://github.com/zulip/zulip)
+ zerver/lib/export.py:1703: error: Unsupported operand types for + ("list[dict[str, Any]]" and "list[dict[str, Any]]")  [operator]

pandas (https://github.com/pandas-dev/pandas)
+ pandas/io/stata.py:1458: error: List comprehension has incompatible type List[object]; expected List[str | dtype[Any]]  [misc]
+ pandas/core/frame.py:11090: error: Incompatible return value type (got "Any | DataFrame | Series", expected "DataFrame")  [return-value]
+ pandas/core/groupby/ops.py:800: error: No overload variant of "unique" matches argument types "list[int]", "bool"  [call-overload]
+ pandas/core/groupby/ops.py:800: note: Possible overload variants:
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[False] = ..., return_inverse: Literal[False] = ..., return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> ndarray[tuple[Any, ...], dtype[_ScalarT]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[False] = ..., return_inverse: Literal[False] = ..., return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> ndarray[tuple[Any, ...], dtype[Any]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[True], return_inverse: Literal[False] = ..., return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[True], return_inverse: Literal[False] = ..., return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[False], return_inverse: Literal[True], return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[False] = ..., *, return_inverse: Literal[True], return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[False], return_inverse: Literal[True], return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[False] = ..., *, return_inverse: Literal[True], return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[False], return_inverse: Literal[False], return_counts: Literal[True], axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[False] = ..., return_inverse: Literal[False] = ..., *, return_counts: Literal[True], axis: SupportsIndex | None = ..., equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[False], return_inverse: Literal[False], return_counts: Literal[True], axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[False] = ..., return_inverse: Literal[False] = ..., *, return_counts: Literal[True], axis: SupportsIndex | None = ..., equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[True], return_inverse: Literal[True], return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[True], return_inverse: Literal[True], return_counts: Literal[False] = ..., axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[True], return_inverse: Literal[False], return_counts: Literal[True], axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[True], return_inverse: Literal[False] = ..., *, return_counts: Literal[True], axis: SupportsIndex | None = ..., equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[True], return_inverse: Literal[False], return_counts: Literal[True], axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[True], return_inverse: Literal[False] = ..., *, return_counts: Literal[True], axis: SupportsIndex | None = ..., equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[False], return_inverse: Literal[True], return_counts: Literal[True], axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[False] = ..., *, return_inverse: Literal[True], return_counts: Literal[True], axis: SupportsIndex | None = ..., equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[False], return_inverse: Literal[True], return_counts: Literal[True], axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[False] = ..., *, return_inverse: Literal[True], return_counts: Literal[True], axis: SupportsIndex | None = ..., equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def [_ScalarT: generic[Any]] unique(ar: _SupportsArray[dtype[_ScalarT]] | _NestedSequence[_SupportsArray[dtype[_ScalarT]]], return_index: Literal[True], return_inverse: Literal[True], return_counts: Literal[True], axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/core/groupby/ops.py:800: note:     def unique(ar: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], return_index: Literal[True], return_inverse: Literal[True], return_counts: Literal[True], axis: SupportsIndex | None = ..., *, equal_nan: bool = ...) -> tuple[ndarray[tuple[Any, ...], dtype[Any]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_32Bit | _64Bit]]]]
+ pandas/tests/strings/conftest.py:6: error: Need type annotation for "_any_string_method"  [var-annotated]
+ pandas/tests/groupby/conftest.py:130: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ pandas/tests/frame/test_constructors.py:2318: error: Unsupported operand types for + ("list[ExtensionDtype | str | dtype[Any] | type[object] | type[str] | type[complex] | type[bool]]" and "list[ExtensionDtype | str | dtype[Any] | type[str] | type[complex] | type[bool] | type[object]]")  [operator]
+ pandas/tests/frame/test_constructors.py:2328: error: Unsupported operand types for + ("list[ExtensionDtype | str | dtype[Any] | type[object] | type[str] | type[complex] | type[bool]]" and "list[ExtensionDtype | str | dtype[Any] | type[str] | type[complex] | type[bool] | type[object]]")  [operator]
+ pandas/tests/arrays/test_datetimes.py:69: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ pandas/conftest.py:1638: error: Unsupported operand types for + ("list[ExtensionDtype | str | dtype[Any] | type[object] | type[str] | type[complex] | type[bool]]" and "list[ExtensionDtype | str | dtype[Any] | type[str] | type[complex] | type[bool] | type[object]]")  [operator]

freqtrade (https://github.com/freqtrade/freqtrade)
+ freqtrade/exchange/exchange_utils.py:119: error: Incompatible types (expression has type "list[dict[str, Any] | dict[str, str]]", TypedDict item "trade_modes" has type "list[TradeModeType]")  [typeddict-item]
+ freqtrade/data/entryexitanalysis.py:287: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ freqtrade/data/entryexitanalysis.py:292: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ freqtrade/data/entryexitanalysis.py:298: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]
+ freqtrade/data/entryexitanalysis.py:299: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]

spark (https://github.com/apache/spark)
+ python/pyspark/pandas/groupby.py:3682: error: Unsupported operand types for + ("list[Series[Any]]" and "list[Series[Any]]")  [operator]

mkosi (https://github.com/systemd/mkosi)
+ mkosi/qemu.py:447:22: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]

paasta (https://github.com/yelp/paasta)
+ paasta_tools/config_utils.py:101: error: Unsupported operand types for + ("List[str]" and "List[str]")  [operator]

mypy (https://github.com/python/mypy)
+ mypyc/crash.py:29: error: Unsupported operand types for + ("list[FrameSummary]" and "list[FrameSummary]")  [operator]
+ mypyc/crash.py:29: note: See https://mypy.rtfd.io/en/stable/_refs.html#code-operator for more info
+ mypy/strconv.py:454: error: Unsupported operand types for + ("list[Any]" and "list[Union[str, tuple[str, list[Any]]]]")  [operator]
+ mypy/errors.py:1285: error: Unsupported operand types for + ("list[FrameSummary]" and "StackSummary")  [operator]
+ mypy/fastparse.py:1612: error: Unsupported operand types for + ("list[expr]" and "list[expr]")  [operator]
+ mypy/test/testcmdline.py:77: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]

core (https://github.com/home-assistant/core)
+ homeassistant/components/monzo/sensor.py:91: error: Unsupported operand types for + ("list[MonzoSensor]" and "list[MonzoSensor]")  [operator]

materialize (https://github.com/MaterializeInc/materialize)
+ misc/python/materialize/cli/ci_upload_heap_profiles.py:42: error: Unsupported operand types for + ("list[str | Any]" and "list[str]")  [operator]

spack (https://github.com/spack/spack)
+ lib/spack/spack/environment/environment.py:1525: error: Incompatible return value type (got "tuple[list[Any], list[Any], list[tuple[Any, Any] | tuple[Any, None]]]", expected "tuple[list[Spec], list[Spec], list[tuple[Spec, Spec]]]")  [return-value]

prefect (https://github.com/PrefectHQ/prefect)
- src/prefect/utilities/callables.py:579: error: Incompatible types in assignment (expression has type "list[None]", variable has type "list[Optional[expr]]")  [assignment]
- src/prefect/utilities/callables.py:579: note: "list" is invariant -- see https://mypy.readthedocs.io/en/stable/common_issues.html#variance
- src/prefect/utilities/callables.py:579: note: Consider using "Sequence" instead, which is covariant

tornado (https://github.com/tornadoweb/tornado)
+ tornado/netutil.py:158: error: Incompatible types in assignment (expression has type "tuple[Union[int, Any], ...]", variable has type "Union[tuple[str, int], tuple[str, int, int, int], tuple[int, bytes]]")  [assignment]
+ tornado/autoreload.py:234: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]

scipy (https://github.com/scipy/scipy)
+ scipy/fft/_pocketfft/tests/test_basic.py:473: error: Unsupported operand types for + ("list[int]" and "list[int]")  [operator]
+ scipy/fft/_pocketfft/tests/test_basic.py:483: error: Unsupported operand types for + ("list[int]" and "list[int]")  [operator]
+ scipy/fft/_pocketfft/tests/test_basic.py:502: error: Unsupported operand types for + ("list[int]" and "list[int]")  [operator]
+ scipy/fft/_pocketfft/tests/test_basic.py:512: error: Unsupported operand types for + ("list[int]" and "list[int]")  [operator]
+ scipy/fftpack/tests/test_basic.py:429: error: Unsupported operand types for + ("list[int]" and "list[int]")  [operator]
+ scipy/fftpack/tests/test_basic.py:439: error: Unsupported operand types for + ("list[int]" and "list[int]")  [operator]
+ scipy/fftpack/tests/test_basic.py:458: error: Unsupported operand types for + ("list[int]" and "list[int]")  [operator]
+ scipy/fftpack/tests/test_basic.py:468: error: Unsupported operand types for + ("list[int]" and "list[int]")  [operator]
+ scipy/optimize/tests/test_optimize.py:1289: error: Unsupported operand types for + ("list[str]" and "list[str]")  [operator]

graphql-core (https://github.com/graphql-python/graphql-core)
+ src/graphql/type/validate.py:409: error: Unsupported operand types for + ("list[NamedTypeNode]" and "list[NamedTypeNode]")  [operator]
+ src/graphql/validation/rules/overlapping_fields_can_be_merged.py:89: error: Unsupported operand types for + ("list[FieldNode]" and "list[FieldNode]")  [operator]

This was referenced Jun 16, 2025
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@randolf-scholz randolf-scholz changed the title Check usefulness of list.__add__ overloads. Remove list.__add__ overloads. Jun 16, 2025
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randolf-scholz commented Jun 16, 2025 •
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There must be a strange bug in mypy. Take this example from the mypy primer above

If I change the offending line (Unsupported operand types for + ("list[FrameSummary]" and "list[FrameSummary]")) from

for s in traceback.format_list(tb + tb2):

to

dummy = tb + tb2
for s in traceback.format_list(dummy):

Then the file passes without issue. (using python -m mypy.stubtest --custom-typeshed-dir=../typeshed/ tmp)

@randolf-scholz randolf-scholz mentioned this pull request Jun 16, 2025
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