BUG: fix Time type columns being skipped with arrow=False

CHANGES.md

@@ -7,6 +7,7 @@
 -   Fix regression in reading date columns (#616)
 -   Fix error in `read_dataframe` when `use_arrow=True` and `columns` is used to filter
     out columns of some specific types (#611)
+-   Fix Time type columns being skipped when reading with `arrow=False` (#617)
 
 ## 0.12.0 (2025-11-26)
 

pyogrio/_io.pyx

@@ -59,7 +59,7 @@ FIELD_TYPES = [
     None,              # OFTWideStringList, deprecated, not supported
     "object",          # OFTBinary, Raw Binary data
     "datetime64[D]",   # OFTDate, Date
-    None,              # OFTTime, Time, NOTE: not directly supported in numpy
+    "object",          # OFTTime, Time, NOTE: not directly supported in numpy
     "datetime64[ms]",  # OFTDateTime, Date and Time
     "int64",           # OFTInteger64, Single 64bit integer
     "list(int64)"      # OFTInteger64List, List of 64bit integers, not supported
@@ -976,8 +976,7 @@ cdef process_fields(
             bin_value = OGR_F_GetFieldAsBinary(ogr_feature, field_index, &ret_length)
             data[i] = bin_value[:ret_length]
 
-        elif field_type == OFTDateTime or field_type == OFTDate:
-
+        elif field_type in (OFTDateTime, OFTDate, OFTTime):
             if field_type == OFTDateTime and datetime_as_string:
                 # defer datetime parsing to user/ pandas layer
                 IF CTE_GDAL_VERSION >= (3, 7, 0):
@@ -1019,6 +1018,9 @@ cdef process_fields(
                         year, month, day, hour, minute, second, microsecond
                     ).isoformat()
 
+                elif field_type == OFTTime:
+                    data[i] = datetime.time(hour, minute, second, microsecond)
+
         elif field_type == OFTIntegerList:
             # According to GDAL doc, this can return NULL for an empty list, which is a
             # valid result. So don't use check_pointer as it would throw an exception.

pyogrio/tests/test_geopandas_io.py

@@ -3,7 +3,7 @@
 import os
 import re
 import warnings
-from datetime import datetime
+from datetime import datetime, time
 from io import BytesIO
 from zipfile import ZipFile
 
@@ -566,7 +566,7 @@ def test_roundtrip_many_data_types_geojson_file(
 ):
     """Test roundtripping a GeoJSON file containing many data types."""
 
-    def validate_result(df: pd.DataFrame, use_arrow: bool, ignore_mixed_list_col=False):
+    def validate_result(df: pd.DataFrame, use_arrow: bool, after_write=False):
         """Function to validate the data of many_data_types_geojson_file.
 
         Depending on arrow being used or not there are small differences.
@@ -597,11 +597,12 @@ def validate_result(df: pd.DataFrame, use_arrow: bool, ignore_mixed_list_col=Fal
             assert is_datetime64_dtype(df["date_col"].dtype)
             assert df["date_col"].to_list() == [pd.Timestamp("2020-01-01")]
 
-        # Ignore time columns till this is solved:
-        # Reported in https://github.com/geopandas/pyogrio/issues/615
-        # assert "time_col" in df.columns
-        # assert is_object_dtype(df["time_col"].dtype)
-        # assert df["time_col"].to_list() == [time(12, 0, 0)]
+        if not (after_write and use_arrow and not GDAL_GE_311):
+            # Before GDAL 3.11, if time columns were written with arrow they were
+            # not actually written.
+            assert "time_col" in df.columns
+            assert is_object_dtype(df["time_col"].dtype)
+            assert df["time_col"].to_list() == [time(12, 0, 0)]
 
         assert "datetime_col" in df.columns
         assert is_datetime64_dtype(df["datetime_col"].dtype)
@@ -615,22 +616,21 @@ def validate_result(df: pd.DataFrame, use_arrow: bool, ignore_mixed_list_col=Fal
         assert is_object_dtype(df["list_str_col"].dtype)
         assert df["list_str_col"][0].tolist() == ["a", "b", "c"]
 
-        if not ignore_mixed_list_col:
+        if not (after_write and use_arrow):
+            # Writing a column with mixed types in a list is not supported with Arrow.
             assert "list_mixed_col" in df.columns
             assert is_object_dtype(df["list_mixed_col"].dtype)
             assert df["list_mixed_col"][0] == [1, "a", None, True]
 
     # Read and validate result of reading
     read_gdf = read_dataframe(many_data_types_geojson_file, use_arrow=use_arrow)
-    validate_result(read_gdf, use_arrow)
+    validate_result(read_gdf, use_arrow, after_write=False)
 
     # Write the data read, read it back, and validate again
     if use_arrow:
         # Writing a column with mixed types in a list is not supported with Arrow.
-        ignore_mixed_list_col = True
         read_gdf = read_gdf.drop(columns=["list_mixed_col"])
     else:
-        ignore_mixed_list_col = False
         request.node.add_marker(
             pytest.mark.xfail(
                 reason="roundtripping list types fails with use_arrow=False"
@@ -642,9 +642,7 @@ def validate_result(df: pd.DataFrame, use_arrow: bool, ignore_mixed_list_col=Fal
 
     # Validate data written
     read_back_gdf = read_dataframe(tmp_file, use_arrow=use_arrow)
-    validate_result(
-        read_back_gdf, use_arrow, ignore_mixed_list_col=ignore_mixed_list_col
-    )
+    validate_result(read_back_gdf, use_arrow, after_write=True)
 
 
 @pytest.mark.filterwarnings(