[GitHub] [spark] xinrong-meng commented on a diff in pull request #40988: [SPARK-41971][SQL][PYTHON] Add a config for pandas conversion how to handle struct types

["xinrong-meng (via GitHub)" <gi...@apache.org>]

xinrong-meng commented on code in PR #40988:
URL: https://github.com/apache/spark/pull/40988#discussion_r1181899926


##########
python/pyspark/sql/pandas/types.py:
##########
@@ -462,3 +467,233 @@ def _convert_dict_to_map_items(s: "PandasSeriesLike") -> "PandasSeriesLike":
     :return: pandas.Series of lists of (key, value) pairs
     """
     return cast("PandasSeriesLike", s.apply(lambda d: list(d.items()) if d is not None else None))
+
+
+def _to_corrected_pandas_type(dt: DataType) -> Optional[Any]:
+    """
+    When converting Spark SQL records to Pandas `pandas.DataFrame`, the inferred data type
+    may be wrong. This method gets the corrected data type for Pandas if that type may be
+    inferred incorrectly.
+    """
+    import numpy as np
+
+    if type(dt) == ByteType:
+        return np.int8
+    elif type(dt) == ShortType:
+        return np.int16
+    elif type(dt) == IntegerType:
+        return np.int32
+    elif type(dt) == LongType:
+        return np.int64
+    elif type(dt) == FloatType:
+        return np.float32
+    elif type(dt) == DoubleType:
+        return np.float64
+    elif type(dt) == BooleanType:
+        return bool
+    elif type(dt) == TimestampType:
+        return np.dtype("datetime64[ns]")
+    elif type(dt) == TimestampNTZType:
+        return np.dtype("datetime64[ns]")
+    elif type(dt) == DayTimeIntervalType:
+        return np.dtype("timedelta64[ns]")
+    else:
+        return None
+
+
+def _create_converter_to_pandas(

Review Comment:
   I am wondering if PySpark/Pandas data type mappings are ever documented publicly.



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