feat: support nullable boolean and Int64 dtypes in `insert_rows_from_…

…dataframe` (#1816)

google/cloud/bigquery/_pandas_helpers.py

@@ -958,6 +958,25 @@ def dataframe_to_json_generator(dataframe):
             # considered a NaN, however.
             if isinstance(is_nan, bool) and is_nan:
                 continue
+
+            # Convert numpy types to corresponding Python types.
+            # https://stackoverflow.com/a/60441783/101923
+            if isinstance(value, numpy.bool_):
+                value = bool(value)
+            elif isinstance(
+                value,
+                (
+                    numpy.int64,
+                    numpy.int32,
+                    numpy.int16,
+                    numpy.int8,
+                    numpy.uint64,
+                    numpy.uint32,
+                    numpy.uint16,
+                    numpy.uint8,
+                ),
+            ):
+                value = int(value)
             output[column] = value
 
         yield output

tests/system/test_pandas.py

@@ -835,7 +835,9 @@ def test_insert_rows_from_dataframe(bigquery_client, dataset_id):
     schema = [
         SF("float_col", "FLOAT", mode="REQUIRED"),
         SF("int_col", "INTEGER", mode="REQUIRED"),
+        SF("int64_col", "INTEGER", mode="NULLABLE"),
         SF("bool_col", "BOOLEAN", mode="REQUIRED"),
+        SF("boolean_col", "BOOLEAN", mode="NULLABLE"),
         SF("string_col", "STRING", mode="NULLABLE"),
         SF("date_col", "DATE", mode="NULLABLE"),
         SF("time_col", "TIME", mode="NULLABLE"),
@@ -898,6 +900,15 @@ def test_insert_rows_from_dataframe(bigquery_client, dataset_id):
     dataframe["date_col"] = dataframe["date_col"].astype("dbdate")
     dataframe["time_col"] = dataframe["time_col"].astype("dbtime")
 
+    # Support nullable integer and boolean dtypes.
+    # https://github.com/googleapis/python-bigquery/issues/1815
+    dataframe["int64_col"] = pandas.Series(
+        [-11, -22, pandas.NA, -44, -55, -66], dtype="Int64"
+    )
+    dataframe["boolean_col"] = pandas.Series(
+        [True, False, True, pandas.NA, True, False], dtype="boolean"
+    )
+
     table_id = f"{bigquery_client.project}.{dataset_id}.test_insert_rows_from_dataframe"
     table_arg = bigquery.Table(table_id, schema=schema)
     table = helpers.retry_403(bigquery_client.create_table)(table_arg)
@@ -910,7 +921,7 @@ def test_insert_rows_from_dataframe(bigquery_client, dataset_id):
     expected = [
         # Pandas often represents NULL values as NaN. Convert to None for
         # easier comparison.
-        tuple(None if col != col else col for col in data_row)
+        tuple(None if pandas.isna(col) else col for col in data_row)
         for data_row in dataframe.itertuples(index=False)
     ]
 

tests/unit/test__pandas_helpers.py

@@ -808,29 +808,60 @@ def test_list_columns_and_indexes_with_named_index_same_as_column_name(
 @pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
 def test_dataframe_to_json_generator(module_under_test):
     utcnow = datetime.datetime.utcnow()
-    df_data = collections.OrderedDict(
-        [
-            ("a_series", [pandas.NA, 2, 3, 4]),
-            ("b_series", [0.1, float("NaN"), 0.3, 0.4]),
-            ("c_series", ["a", "b", pandas.NA, "d"]),
-            ("d_series", [utcnow, utcnow, utcnow, pandas.NaT]),
-            ("e_series", [True, False, True, None]),
-        ]
-    )
     dataframe = pandas.DataFrame(
-        df_data, index=pandas.Index([4, 5, 6, 7], name="a_index")
+        {
+            "a_series": [1, 2, 3, 4],
+            "b_series": [0.1, float("NaN"), 0.3, 0.4],
+            "c_series": ["a", "b", pandas.NA, "d"],
+            "d_series": [utcnow, utcnow, utcnow, pandas.NaT],
+            "e_series": [True, False, True, None],
+            # Support nullable dtypes.
+            # https://github.com/googleapis/python-bigquery/issues/1815
+            "boolean_series": pandas.Series(
+                [True, False, pandas.NA, False], dtype="boolean"
+            ),
+            "int64_series": pandas.Series([-1, pandas.NA, -3, -4], dtype="Int64"),
+        }
     )
 
-    dataframe = dataframe.astype({"a_series": pandas.Int64Dtype()})
+    # Index is not included, even if it is not the default and has a name.
+    dataframe = dataframe.rename(index=lambda idx: idx + 4)
+    dataframe.index.name = "a_index"
 
-    rows = module_under_test.dataframe_to_json_generator(dataframe)
+    rows = list(module_under_test.dataframe_to_json_generator(dataframe))
     expected = [
-        {"b_series": 0.1, "c_series": "a", "d_series": utcnow, "e_series": True},
-        {"a_series": 2, "c_series": "b", "d_series": utcnow, "e_series": False},
-        {"a_series": 3, "b_series": 0.3, "d_series": utcnow, "e_series": True},
-        {"a_series": 4, "b_series": 0.4, "c_series": "d"},
+        {
+            "a_series": 1,
+            "b_series": 0.1,
+            "c_series": "a",
+            "d_series": utcnow,
+            "e_series": True,
+            "boolean_series": True,
+            "int64_series": -1,
+        },
+        {
+            "a_series": 2,
+            "c_series": "b",
+            "d_series": utcnow,
+            "e_series": False,
+            "boolean_series": False,
+        },
+        {
+            "a_series": 3,
+            "b_series": 0.3,
+            "d_series": utcnow,
+            "e_series": True,
+            "int64_series": -3,
+        },
+        {
+            "a_series": 4,
+            "b_series": 0.4,
+            "c_series": "d",
+            "boolean_series": False,
+            "int64_series": -4,
+        },
     ]
-    assert list(rows) == expected
+    assert rows == expected
 
 
 @pytest.mark.skipif(pandas is None, reason="Requires `pandas`")