ENH: add masked algorithm for mean() function by Akshatt · Pull Request #34814 · pandas-dev/pandas (original) (raw)
This operation is already covered by
| class NanOps: |
|---|
| params = [ |
| [ |
| "var", |
| "mean", |
| "median", |
| "max", |
| "min", |
| "sum", |
| "std", |
| "sem", |
| "argmax", |
| "skew", |
| "kurt", |
| "prod", |
| ], |
| [10 ** 3, 10 ** 6], |
| ["int8", "int32", "int64", "float64", "Int64", "boolean"], |
| ] |
| param_names = ["func", "N", "dtype"] |
| def setup(self, func, N, dtype): |
| if func == "argmax" and dtype in {"Int64", "boolean"}: |
| # Skip argmax for nullable int since this doesn't work yet (GH-24382) |
| raise NotImplementedError |
| self.s = Series([1] * N, dtype=dtype) |
| self.func = getattr(self.s, func) |
| def time_func(self, func, N, dtype): |
| self.func() |
(I added Int64 in general for all ops, so including mean, when I added a masked sum/prod algorithm)
So no need to write additional benchmarks I think.
For tests, it would be good to add it here:
| def test_ops_consistency_on_empty(self, method): |
|---|
(we mainly need to check the behaviour on empty)