Fix pickle exception

src/datacustomcode/io/reader/query_api.py

@@ -21,6 +21,7 @@
     Union,
 )
 
+import pandas.api.types as pd_types
 from pyspark.sql.types import (
     BooleanType,
     DoubleType,
@@ -48,8 +49,6 @@
     "object": StringType(),
     "int64": LongType(),
     "float64": DoubleType(),
-    "datetime64[ns]": TimestampType(),
-    "datetime64[ns, UTC]": TimestampType(),
     "bool": BooleanType(),
 }
 
@@ -59,7 +58,11 @@ def _pandas_to_spark_schema(
 ) -> StructType:
     fields = []
     for column, dtype in pandas_df.dtypes.items():
-        spark_type = PANDAS_TYPE_MAPPING.get(str(dtype), StringType())
+        spark_type: AtomicType
+        if pd_types.is_datetime64_any_dtype(dtype):
+            spark_type = TimestampType()
+        else:
+            spark_type = PANDAS_TYPE_MAPPING.get(str(dtype), StringType())
         fields.append(StructField(column, spark_type, nullable))
     return StructType(fields)
 

tests/io/reader/test_query_api.py

@@ -14,6 +14,7 @@
     StringType,
     StructField,
     StructType,
+    TimestampType,
 )
 import pytest
 
@@ -59,6 +60,55 @@ def test_pandas_to_spark_schema_nullable(self):
         schema = _pandas_to_spark_schema(df, nullable=False)
         assert not schema.fields[0].nullable
 
+    def test_pandas_to_spark_schema_datetime_types(self):
+        """Test conversion of pandas datetime types to Spark TimestampType."""
+
+        # Create test data with different datetime types
+        data = {
+            "datetime_ns": pd.to_datetime(
+                ["2023-01-01 10:00:00", "2023-01-02 11:00:00"]
+            ),
+            "datetime_ns_utc": pd.to_datetime(
+                ["2023-01-01 10:00:00", "2023-01-02 11:00:00"], utc=True
+            ),
+            "datetime_ms": pd.to_datetime(
+                ["2023-01-01 10:00:00", "2023-01-02 11:00:00"]
+            ).astype("datetime64[ms]"),
+            "datetime_ms_utc": pd.to_datetime(
+                ["2023-01-01 10:00:00", "2023-01-02 11:00:00"], utc=True
+            )
+            .tz_localize(None)
+            .astype("datetime64[ms]"),
+        }
+        df = pd.DataFrame(data)
+
+        # Convert to Spark schema
+        schema = _pandas_to_spark_schema(df)
+
+        # Verify the schema
+        assert isinstance(schema, StructType)
+        assert len(schema.fields) == 4
+
+        # Check that all datetime columns map to TimestampType
+        field_dict = {field.name: field for field in schema.fields}
+        for field_name in [
+            "datetime_ns",
+            "datetime_ns_utc",
+            "datetime_ms",
+            "datetime_ms_utc",
+        ]:
+            assert isinstance(field_dict[field_name].dataType, TimestampType), (
+                f"Field {field_name} should be TimestampType, "
+                f"got {type(field_dict[field_name].dataType)}"
+            )
+            assert field_dict[field_name].nullable
+
+        # Verify the actual pandas dtypes to ensure our test data has the expected types
+        assert str(df["datetime_ns"].dtype) == "datetime64[ns]"
+        assert str(df["datetime_ns_utc"].dtype) == "datetime64[ns, UTC]"
+        assert str(df["datetime_ms"].dtype) == "datetime64[ms]"
+        assert str(df["datetime_ms_utc"].dtype) == "datetime64[ms]"
+
 
 # Completely isolated test class for QueryAPIDataCloudReader
 @pytest.mark.usefixtures("patch_all_requests")