[BUGFIX] ExpectColumnValuesToBeOfType returns full map-format result for all backends#11868
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EshwarCVS wants to merge 7 commits into
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[BUGFIX] ExpectColumnValuesToBeOfType returns full map-format result for all backends#11868EshwarCVS wants to merge 7 commits into
EshwarCVS wants to merge 7 commits into
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…ations#11206) Pydantic coerces False/True to "False"/"True" when str appears before Literal[False]/Literal[True] in a Union. Fix _get_annotation_type to sort bool-like types to the front of generated Union types, so index_col=False (and similar bool flags) are preserved correctly. https://claude.ai/code/session_01ETHG72BuUQaQWWdtWDcQJM
for more information, see https://pre-commit.ci
…s full map-format result
For non-map backends (Pandas non-object dtype, SQLAlchemy, Spark), the
expectation previously returned only {"observed_value": ...}. It now returns
the complete ColumnMapExpectation result structure (element_count,
unexpected_count, unexpected_percent, missing_count, etc.) alongside
observed_value, matching the documented output schema.
Also confirmed great-expectations#11026 (accented chars in row_conditions) is already resolved
via alphas8bit in legacy_row_conditions.py.
https://claude.ai/code/session_014PRzTm8HD61UpoyPvE3ciH
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thanks for the PR @EshwarCVS . This expectation is unusual in that the Pandas implementation behaves as a row level check, while the Spark and SQL implementations behave as a schema level check. We'll address the gap, but haven't settled on a solution yet. I'll leave this open, but will hold off reviewing for the time being. |
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Summary
Fixes #11076 —
ExpectColumnValuesToBeOfTypewas returning only{"observed_value": <type>}for non-object Pandas dtypes, SQLAlchemy,and Spark backends instead of the standard
ColumnMapExpectationresultstructure documented in the expectation.
Also confirms #11026 (accented characters in
row_conditionstrings) isalready resolved via
alphas8bitinlegacy_row_conditions.py.Changes
expect_column_values_to_be_of_type.pytable.row_countandcolumn_values.nonnull.unexpected_countto
get_validation_dependenciesfor all backends so missing/nullstats are always available.
_build_map_result()helper that wraps aggregate type-checkresults through
_format_map_output, computingnonnull_countfromthe null metric and preserving
observed_value._validate()to route non-map paths through_build_map_result.test_expect_column_values_to_be_of_type.pycolumns containing nulls.
Before / After
Before:
{ "success": false, "result": { "observed_value": "StringType" } }After:
{ "success": false, "result": { "element_count": 5, "unexpected_count": 4, "unexpected_percent": 100.0, "partial_unexpected_list": [], "missing_count": 1, "missing_percent": 20.0, "unexpected_percent_total": 80.0, "unexpected_percent_nonmissing": 100.0, "observed_value": "StringType" } }