(self, *, name, axis, mask_size)
| 1566 | return result |
| 1567 | |
| 1568 | def _wrap_na_result(self, *, name, axis, mask_size): |
| 1569 | mask = np.ones(mask_size, dtype=bool) |
| 1570 | |
| 1571 | float_dtyp = "float32" if self.dtype == "Float32" else "float64" |
| 1572 | if name in ["mean", "median", "var", "std", "skew", "kurt", "sem"]: |
| 1573 | np_dtype = float_dtyp |
| 1574 | elif name in ["min", "max"] or self.dtype.itemsize == 8: |
| 1575 | np_dtype = self.dtype.numpy_dtype.name |
| 1576 | else: |
| 1577 | is_windows_or_32bit = is_platform_windows() or not IS64 |
| 1578 | int_dtyp = "int32" if is_windows_or_32bit else "int64" |
| 1579 | uint_dtyp = "uint32" if is_windows_or_32bit else "uint64" |
| 1580 | np_dtype = {"b": int_dtyp, "i": int_dtyp, "u": uint_dtyp, "f": float_dtyp}[ |
| 1581 | self.dtype.kind |
| 1582 | ] |
| 1583 | |
| 1584 | value = np.array([1], dtype=np_dtype) |
| 1585 | return self._maybe_mask_result(value, mask=mask) |
| 1586 | |
| 1587 | def _wrap_min_count_reduction_result( |
| 1588 | self, name: str, result, *, skipna, min_count, axis |
no test coverage detected