Add variance checks to aggregation tests and type assertions #1546
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- Updated aggregation tests in `test_agg.py` to include checks for the `var()` method across different data types (float, bool, int, and complex). - Added a warning check for complex series when calculating variance to handle potential loss of imaginary parts. - Enhanced type assertions in `test_series.py` for the `var()` method to ensure it returns a float type.
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Pull request overview
This PR adds comprehensive variance checks to aggregation tests and enhances type assertions for the var() method across different data types (float, bool, int, and complex). The PR addresses issue #1545 by ensuring the variance method has proper type stubs and test coverage.
- Added type assertions using
assert_type()andcheck()forvar()method calls in type checking tests - Added variance test coverage across different data types with proper warning checks for complex numbers
- Updated type stubs with overloaded signatures for the
var()method to return appropriate types based on series dtype
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| tests/series/test_series.py | Enhanced test_types_var() with type assertions to verify var() returns float type |
| tests/series/test_agg.py | Added var() checks across all aggregation tests for different data types (float, bool, int, complex, str, timedelta) with appropriate warning handling for complex series |
| tests/_typing.py | SHOULD NOT BE COMMITTED - This is an auto-generated file during test runs per tests/conftest.py |
| pandas-stubs/core/series.pyi | Added overloaded type signatures for var() method with specific return types for different series types |
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Since you have clicked If I used AI to develop this pull request, I prompted it to follow AGENTS.md., please also follow the section https://github.com/pandas-dev/pandas-stubs/blob/main/AGENTS.md#pull-requests-summary
pandas-stubs/core/series.pyi
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| ddof: int = 1, | ||
| numeric_only: _bool = False, | ||
| **kwargs: Any, | ||
| ) -> np.float64: ... |
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| ) -> np.float64: ... | |
| ) -> float: ... |
tests/series/test_agg.py
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| check(assert_type(series.mean(), pd.Timedelta), pd.Timedelta) | ||
| check(assert_type(series.median(), pd.Timedelta), pd.Timedelta) | ||
| check(assert_type(series.std(), pd.Timedelta), pd.Timedelta) | ||
| # Note: var() is not supported for Timedelta series at runtime |
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Please use if TYPE_CHECKING_INVALID_USAGE and make sure series.var() here gives either errors by type checkers (like in test_agg_str) or Never (assert_type(series.var(), Never)).
Error is preferable over Never.
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| check(assert_type(series.mean(), pd.Timestamp), pd.Timestamp) | ||
| check(assert_type(series.median(), pd.Timestamp), pd.Timestamp) | ||
| check(assert_type(series.std(), pd.Timedelta), pd.Timedelta) |
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Please use if TYPE_CHECKING_INVALID_USAGE and make sure series.var() here gives either errors by type checkers (like in test_agg_str) or Never (assert_type(series.var(), Never)).
Error is preferable over Never.
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Please also add a check here for series.var()
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/pandas_nightly |
- Fix `Series[complex].var()` return type from `np.float64` to `float` - Add `Series[Timedelta].var()` overload returning `Never` (invalid operation) - Add upper bound `upper="2.3.99"` for complex variance warning test - Add Timedelta var() invalid usage test
| check(assert_type(series.median(), pd.Timedelta), pd.Timedelta) | ||
| check(assert_type(series.std(), pd.Timedelta), pd.Timedelta) | ||
| if TYPE_CHECKING_INVALID_USAGE: | ||
| series.var() # type: ignore[misc] |
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Never is considered as "valid" by type checkers. However if a function call returns Never, type checkers won't check the code after that call.
You can do something like this:
| series.var() # type: ignore[misc] | |
| def _0() -> None: # pyright: ignore[reportUnusedFunction] | |
| assert_type(series.var(), Never) |
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| check(assert_type(series.mean(), pd.Timestamp), pd.Timestamp) | ||
| check(assert_type(series.median(), pd.Timestamp), pd.Timestamp) | ||
| check(assert_type(series.std(), pd.Timedelta), pd.Timedelta) |
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Please also add a check here for series.var()
3 participants
Updated aggregation tests in
test_agg.pyto include checks for thevar()method across different data types (float, bool, int, and complex).Added a warning check for complex series when calculating variance to handle potential loss of imaginary parts.
Enhanced type assertions in
test_series.pyfor thevar()method to ensure it returns a float type.Closes Scalar type to float error with pyright #1545 (Replace xxxx with the Github issue number)
Tests added (Please use
assert_type()to assert the type of any return value)If I used AI to develop this pull request, I prompted it to follow
AGENTS.md.