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Removelist.__add__ overloads.#14282
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Diff frommypy_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 covarianttornado (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 referencedJun 16, 2025
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list.__add__ overloads.list.__add__ overloads.ContributorAuthor
randolf-scholz commentedJun 16, 2025 • edited
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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 ( forsintraceback.format_list(tb+tb2): to dummy=tb+tb2forsintraceback.format_list(dummy): Then the file passes without issue. (using |
| # 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-scholzJun 26, 2025 • edited
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With this, wouldn't we getlist[str] + list[int] = list[object] when checking withmypy, rather thenlist[str | int]?
I'd preferpython/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 withmypy, rather thenlist[str | int]?
With this, wouldn't we get
list[str] + list[int] = list[object]when checking withmypy, rather thenlist[str | int]?I'd preferpython/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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See#14283.
The overloads on
list.__add__lead to divergence ofmypyandpyright. Code sample inpyright playground,https://mypy-play.net/?mypy=latest&python=3.12&gist=abf6a8834020af17a16bd8cfb44b2f10A comment from 3 years added in#8293 states that
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.