comparing time series with index of different units #63466
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rhshadrach
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Thanks for the PR. Please always add tests.
…nges' into pandas-antriksh-changes
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Hi @rhshadrach , just wanted to confirm what do you mean by add tests. If by that you meant adding more tests for the pytest to check, then i just did that and made a new pr, whose checks are currently running |
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Hi @rhshadrach, waiting for a review from your side |
| try: | ||
| other_values = other._values | ||
| if hasattr(other_values, "as_unit") and hasattr( | ||
| self._values, "equals" | ||
| ): | ||
| return self._values.equals(other_values.as_unit(self_unit)) |
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In what cases does this raise?
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an example could be if we put two date_ranges to equal(), one with frequency "D" and other being "M", then if this scenario is hit, we safely get False as answer rather then raising a ValueError, due to frequency mismatch.
basically first check if other_values has units we must compare, second check if self can have these compared to it, then simply check and return if they are equal or not
| result = idx1.intersection(idx2) | ||
| expected = date_range("2000-01-01", periods=3, tz=tz).as_unit("ns") | ||
| tm.assert_index_equal(result, expected) | ||
| tm.assert_index_equal(result, expected, exact=False) |
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Why does this need to change?
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It relaxes the dtype strictness. In the context of datetimes, it stops caring whether the storage is in nanoseconds or microseconds, as long as the dates themselves represent the same point in history.
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