Yicong-Huang commented on code in PR #53992:
URL: https://github.com/apache/spark/pull/53992#discussion_r2752551692
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -409,21 +414,21 @@ def arrow_to_pandas(
ndarray_as_list=ndarray_as_list,
)
- def _create_array(self, series, arrow_type, spark_type=None,
arrow_cast=False):
+ def _create_array(self, series, spark_type, *, arrow_cast=False,
prefers_large_types=False):
"""
- Create an Arrow Array from the given pandas.Series and optional type.
+ Create an Arrow Array from the given pandas.Series and Spark type.
Parameters
----------
series : pandas.Series
A single series
- arrow_type : pyarrow.DataType, optional
- If None, pyarrow's inferred type will be used
spark_type : DataType, optional
- If None, spark type converted from arrow_type will be used
- arrow_cast: bool, optional
+ The Spark type to use. If None, pyarrow's inferred type will be
used.
Review Comment:
That makes sense, thanks for the suggestion.
I want to make sure I understand what you mean by factoring out the
createDataFrame usage.
In this PR I am not changing the createDataFrame behavior. Spark still
allows users to pass an optional schema (see [doc
here](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.SparkSession.createDataFrame.html)),
and when it is not provided, we can end up without a Spark type at this stage.
That is already part of the current behavior. So even if we refactor and
isolate the createDataFrame-related logic, we would still have cases where the
Spark type is None before Arrow conversion.
Are you suggesting that instead we should make `createDataFrame` always let
Arrow infer the type first, and then convert that inferred Arrow type back into
a Spark type, so that downstream we can assume the Spark type is always defined?
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