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Remove list.__add__ overloads. #14282

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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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jorenham
jorenham reviewed Jun 26, 2025
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stdlib/builtins.pyi
# Overloading looks unnecessary, but is needed to work around complex mypy problems
@overload
def __add__(self, value: list[_T], /) -> list[_T]: ...
@overload
def __add__(self, value: list[_S], /) -> list[_S | _T]: ...
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maybe something like this could work?

Suggested change
def __add__(self, value: list[_S], /) -> list[_S | _T]: ...
def __add__(self: list[_S], value: list[_S], /) -> list[_S]: ...

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@randolf-scholz randolf-scholz Jun 26, 2025 •
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With this, wouldn't we get list[str] + list[int] = list[object] when checking with mypy, rather then list[str | int]?

I'd prefer python/mypy#19304 be fixed; then I think we would see most mypy-primer issues here go away. If you have seen this before and know and easier way to reproduce, please comment it.

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With this, wouldn't we get list[str] + list[int] = list[object] when checking with mypy, rather then list[str | int]?

With this, wouldn't we get list[str] + list[int] = list[object] when checking with mypy, rather then list[str | int]?

I'd prefer python/mypy#19304 be fixed; then I think we would see most mypy-primer issues here go away. If you have seen this before and know and easier way to reproduce, please comment it.

That's what I indeed expect here, given mypy's join vs union behavior. But if that's how they decide to behave, then that's up to them, afaik. At least it won't be rejected this way (is the hope)

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Well, my goal with this PR was to reduce type-checker divergence, not to cement it.

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fair enough

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