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https://issues.apache.org/jira/browse/SPARK-60161?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-60161:
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Labels: pull-request-available (was: )
> Add tests for nested timestamp conversion in pandas type converters
> -------------------------------------------------------------------
>
> Key: SPARK-60161
> URL: https://issues.apache.org/jira/browse/SPARK-60161
> Project: Spark
> Issue Type: Test
> Components: PySpark, Tests
> Affects Versions: 4.4.0
> Reporter: Jason Chen
> Priority: Minor
> Labels: pull-request-available
>
> The pandas type converters in {{pyspark/sql/pandas/types.py}}
> ({{_create_converter_to_pandas}} and {{_create_converter_from_pandas}})
> handle top-level timestamp columns with a vectorized path, but timestamps
> nested inside complex types (e.g. {{ArrayType(TimestampType())}}) go through
> per-value converters that currently lack unit test coverage.
> According to Codecov, several of these branches are not exercised by any
> test, including:
> * from_pandas: timezone-aware datetime values nested in {{TimestampType}}
> * from_pandas: {{TimestampLTZNanosType}} values (the whole converter)
> * to_pandas: nested {{TimestampType}} with {{timestamp_utc_localized=False}}
> * to_pandas: nested {{TimestampLTZNanosType}} and {{TimestampNTZType}} values
> This ticket adds unit tests to
> {{python/pyspark/sql/tests/pandas/test_converter.py}} covering both
> directions for nested {{TimestampType}}, {{TimestampLTZNanosType}} and
> {{TimestampNTZType}}, including:
> * timezone-aware vs. naive input values
> * interpretation of naive values in the session timezone
> * {{timestamp_utc_localized=False}}, where naive values are read in the local
> (system) timezone
> * preservation of nanosecond precision for {{TimestampLTZNanosType}}
> * None elements and None arrays
> This is a test-only change with no user-facing impact.
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