Comments (2)
If anyone can provide a bit of guidence for how this should be implemented, I could be keen to go down the rabbit hole and implement it! :)
from pandera.
you don't need more than this to get a pyspark instance to test with
from pyspark.sql import SparkSession
@pytest.fixture(scope="session")
def spark_session():
"""Create a SparkSession for testing."""
spark = SparkSession.builder \
.appName("test") \
.master("local[2]") \
.getOrCreate()
yield spark
spark.stop()
from pandera.
Related Issues (20)
- pyspark_sql docs run time error HOT 1
- Reusing Field objects causes unexpected failure
- Mypy type hinting error for direct column type annotations
- COERCION_ERRORS will not support `polars=1.0.0` HOT 2
- Deprecation warning on `with_context` HOT 2
- `@pa.parser` does not work for Index fields
- Current version does not work with numpy 2.0 HOT 5
- Auto-completion of column names of a DataFrame instance HOT 3
- Validation with `dataframe_check` gets very slow for big dataframes HOT 5
- Decimal validation not fully supported HOT 5
- Float Nullability if Entire Column None HOT 1
- dev: black include regex
- Checks with element_wise = True while using PolarsCheckBackend are called twice. HOT 2
- Next Planned Release HOT 3
- [Dependency] pandera 0.20.0 now requires `pyarrow` as a mandatory dependency (under certain contexts) :disappointed: HOT 4
- Add custom error message for pyspark `register_check_method` which currently defaults to `None` HOT 1
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- Arrow String checks not compatible ('re.Pattern' object has no attribute 'startswith') HOT 3
- -OO optimization flag of python makes pandera crash HOT 2
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from pandera.