HyukjinKwon commented on code in PR #45699:
URL: https://github.com/apache/spark/pull/45699#discussion_r1538564875
##########
python/pyspark/sql/connect/session.py:
##########
@@ -418,6 +426,28 @@ def createDataFrame(
# If no schema supplied by user then get the names of columns only
if schema is None:
_cols = [str(x) if not isinstance(x, str) else x for x in
data.columns]
+ infer_pandas_dict_as_map = (
+
str(self.conf.get("spark.sql.execution.pandas.inferPandasDictAsMap")).lower()
+ == "true"
+ )
+ if infer_pandas_dict_as_map:
+ fields = []
+ pa_schema = pa.Schema.from_pandas(data)
+ spark_type: Union[MapType, DataType]
+ for field in pa_schema:
+ field_type = field.type
+ if isinstance(field_type, pa.StructType):
+ if len(field_type) == 0:
+ raise PySparkValueError(
+ error_class="CANNOT_INFER_EMPTY_SCHEMA",
+ message_parameters={},
+ )
+ arrow_type = field_type.field(0).type
+ spark_type = MapType(StringType(),
from_arrow_type(arrow_type))
+ else:
+ spark_type = from_arrow_type(field_type)
Review Comment:
This gets a type from the first field. Is this the same when users
explicitly specify the schema? e.g., what happen if the dictionary contains
different types of values?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
