MattBelle opened a new pull request, #28979:
URL: https://github.com/apache/flink/pull/28979
## What is the purpose of the change
This pull request adds missing-value handling methods to the PyFlink
DataFrame API, enabling users to easily drop or fill NULL and NaN values in
their data processing pipelines.
## Brief change log
- Added `drop_null()` method to remove rows containing NULL values
- Added `drop_nan()` method to remove rows containing NaN values
- Added `fill_null()` method to replace NULL values with specified values
- Added `fill_nan()` method to replace NaN values with specified values
- Implemented `IS_NAN` and `IS_NOT_NAN` built-in functions in Flink Table
API (required infrastructure for NaN detection)
- Added `is_nan` and `is_not_nan` helper functions to Expression API
(mirrors `is_null`/`is_not_null` pattern)
- Added helper methods `_validate_subset()` and `_fill_values()` for DRY
code organization
- Added comprehensive unit and integration tests for all new methods
## Verifying this change
This change added tests and can be verified as follows:
- Added unit tests (`DataFrameDropNullTests`, `DataFrameDropNanTests`,
`DataFrameFillNullTests`, `DataFrameFillNanTests`) that verify:
- Schema preservation for all operations
- Parameter validation with comprehensive error case coverage:
- Empty subset lists raise `ValueError`
- Invalid column names raise `ValueError` with clear error messages
- Non-list subset parameters raise `TypeError`
- Added integration tests (`DataFrameNullNanITTests`) that verify correct
behavior with real data:
- `drop_null()` correctly removes rows with NULL values
- `drop_nan()` correctly removes rows with NaN values
- `fill_null()` correctly replaces NULL with specified values (numeric and
string)
- `fill_nan()` correctly replaces NaN with specified values
- All methods work correctly with `subset` parameter to target specific
columns
- Tests cover success cases, error conditions, and different data types
## Does this pull request potentially affect one of the following parts:
- Dependencies (does it add or upgrade a dependency): no
- The public API, i.e., is any changed class annotated with
`@Public(Evolving)`: yes (new `@PublicEvolving` DataFrame methods)
- The serializers: no
- The runtime per-record code paths (performance sensitive): no
- Anything that affects deployment or recovery: JobManager (and its
components), Checkpointing, Kubernetes/Yarn, ZooKeeper: no
- The S3 file system connector: no
## Documentation
- Does this pull request introduce a new feature? yes
- If yes, how is the feature documented? JavaDocs (comprehensive Python
docstrings with examples and version tags)
---
##### Was generative AI tooling used to co-author this PR?
- [X] Yes (please specify the tool below)
Generated-by: Bob Shell 1.0.6
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