pandas#1789
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RamilCDISC
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Jun 29, 2026
| .groupby(self.params.grouping, as_index=False, group_keys=False) | ||
| .data[self.params.target] | ||
| .apply(list) | ||
| .apply(_apply_dropna_list) |
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Before the grouped path used .apply(list) which would preserve the missing values but _apply_dropna_list drops them. If this is intentional then the ungrouped non reference path still uses data = result[self.params.target].unique() and then result = list(data) which preserves the missing values in the list. The distinct operation will have two different behaviors then. Please let me know if I misunderstanding something here.
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made them have parity
RamilCDISC
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The PR is one of the last changes for pandas 3.0 compatibility. The PR updates the Distinct operation and updates pandas version in the tests.The PR was validated by:
- Reviewing the PR for any unwanted code or comments.
- Reviewing the PR in accordance to AC.
- Reviewing the updated operation for consistency throughout its different return paths.
- Ensuring all unit and regression tests pass.
- Ensuring pandas version update does not break any CI check.
- Ensuring pandas version update does not cause any regressions.
gerrycampion
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Jun 30, 2026
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per @filippsatverily Removes the last remaining upper-bound version cap. All pandas 3.0 compatibility fixes are now on main (#1767, #1772, #1774, #1776, #1777, #1782, #1786).
I had to add some extra changes to preserve behavior in 2 operations:
day_data_validator.py, explicitly cast columns toobjecttype before mapping timestamp parsing functions, ensuring compatibility withpandas3 and preventing type casting-related issues. [1] [2]distinct.py, replaced directlistconversion with a new_apply_dropna_listfunction to removeNaNvalues before collecting unique values, addressing changes in howpandas3 handles missing data in groupby operations. [1] [2]