[Data] Ensure consistent nan_is_null semantics in unique_post_fn of encoder#62623
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goutamvenkat-anyscale merged 1 commit intoApr 15, 2026
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…ncoder.py Signed-off-by: Ayush Kumar <ayushk7102@gmail.com>
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This pull request updates the null-dropping logic in the gen_value_index_arrow_from_arrow function within the Ray Data encoder preprocessor. The change replaces pc.drop_null with a manual filter that explicitly treats NaNs as nulls when drop_na_values is enabled, ensuring more robust handling of missing data. I have no feedback to provide as there are no review comments to evaluate.
goutamvenkat-anyscale
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…ncoder (ray-project#62623) ## Description This is a follow-up PR to ray-project#62618 to ensure consistent behavior in `gen_value_index_arrow_from_arrow` of `encoder.py`. Previously, if `drop_na_values` was True, we were dropping nulls (and not NaNs). This PR ensures that we drop NaNs as well Signed-off-by: Ayush Kumar <ayushk7102@gmail.com>
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…ncoder (ray-project#62623) ## Description This is a follow-up PR to ray-project#62618 to ensure consistent behavior in `gen_value_index_arrow_from_arrow` of `encoder.py`. Previously, if `drop_na_values` was True, we were dropping nulls (and not NaNs). This PR ensures that we drop NaNs as well Signed-off-by: Ayush Kumar <ayushk7102@gmail.com>
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Description
This is a follow-up PR to #62618 to ensure consistent behavior in
gen_value_index_arrow_from_arrowofencoder.py. Previously, ifdrop_na_valueswas True, we were dropping nulls (and not NaNs). This PR ensures that we drop NaNs as well