The journey continues: QgsArrowIterator is now merged! This makes it possible to iterate over QgsFeatures as Arrow batches. This is where we are now, quoting Dewey Dunnington: import geopandasfrom nanoarrow.c_array import allocate_c_arrayimport qgisfrom qgis.core import QgsVectorLayer# Create a vector layerlayer = QgsVectorLayer("tests/testdata/zonalstatistics/polys.shp", "layer_name", "ogr")schema = qgis.core.QgsArrowIterator.inferSchema(layer)it = qgis.core.QgsArrowIterator(layer.getFeatures())it.setSchema(schema, 1)c_array = allocate_c_array()schema.exportToAddress(c_array.schema._addr())it.nextFeatures(5, c_array._addr())print(geopandas.GeoDataFrame.from_arrow(c_array))#> lev3_name geometry#> 0 poly_1 MULTIPOLYGON (((100.37934 -0.96049, 100.37934 ...#> 1 poly_2 MULTIPOLYGON (((100.37944 -0.96044, 100.37955 ...#> 2 poly_3 MULTIPOLYGON (((100.37938 -0.96049, 100.37949 ...print(geopandas.read_file("tests/testdata/zonalstatistics/polys.shp"))#> lev3_name geometry#> 0 poly_1 POLYGON ((100.37934 -0.96049, 100.37934 -0.960...#> 1 poly_2 POLYGON ((100.37944 -0.96044, 100.37955 -0.960...#> 2 poly_3 POLYGON ((100.37938 -0.96049, 100.37949 -0.960...Further improvements are already being planned. To quote from the ticket:“The final state after this improvement would be a compact way for Arrow Python consumers like GeoPandas to ergonomically consume a layer. Maybe:geopandas.GeoDataFrame.from_arrow(qgis_layer_object)Or maybe:geopandas.GeoDataFrame.from_arrow(qgis_layer_object.getArrowStream())Looking forward to seeing this develop further.