feat(bigquery): make rowIterator._to_dataframe_iterable public

bigquery/google/cloud/bigquery/table.py

@@ -1554,11 +1554,44 @@ def to_arrow(
             arrow_schema = _pandas_helpers.bq_to_arrow_schema(self._schema)
             return pyarrow.Table.from_batches(record_batches, schema=arrow_schema)
 
-    def _to_dataframe_iterable(self, bqstorage_client=None, dtypes=None):
+    def to_dataframe_iterable(self, bqstorage_client=None, dtypes=None):
         """Create an iterable of pandas DataFrames, to process the table as a stream.
 
-        See ``to_dataframe`` for argument descriptions.
+        Args:
+            bqstorage_client (google.cloud.bigquery_storage_v1beta1.BigQueryStorageClient):
+                **Beta Feature** Optional. A BigQuery Storage API client. If
+                supplied, use the faster BigQuery Storage API to fetch rows
+                from BigQuery.
+
+                This method requires the ``pyarrow`` and
+                ``google-cloud-bigquery-storage`` libraries.
+
+                Reading from a specific partition or snapshot is not
+                currently supported by this method.
+
+                **Caution**: There is a known issue reading small anonymous
+                query result tables with the BQ Storage API. When a problem
+                is encountered reading a table, the tabledata.list method
+                from the BigQuery API is used, instead.
+            dtypes (Map[str, Union[str, pandas.Series.dtype]]):
+                Optional. A dictionary of column names pandas ``dtype``s. The
+                provided ``dtype`` is used when constructing the series for
+                the column specified. Otherwise, the default pandas behavior
+                is used.
+
+        Returns:
+            pandas.DataFrame:
+                A generator of :class:`~pandas.DataFrame`.
+
+        Raises:
+            ValueError:
+                If the :mod:`pandas` library cannot be imported.
         """
+        if pandas is None:
+            raise ValueError(_NO_PANDAS_ERROR)
+        if dtypes is None:
+            dtypes = {}
+
         column_names = [field.name for field in self._schema]
         bqstorage_download = functools.partial(
             _pandas_helpers.download_dataframe_bqstorage,
@@ -1683,7 +1716,7 @@ def to_dataframe(
             progress_bar = self._get_progress_bar(progress_bar_type)
 
             frames = []
-            for frame in self._to_dataframe_iterable(
+            for frame in self.to_dataframe_iterable(
                 bqstorage_client=bqstorage_client, dtypes=dtypes
             ):
                 frames.append(frame)

bigquery/tests/unit/test_table.py

@@ -2014,6 +2014,68 @@ def test_to_arrow_w_pyarrow_none(self):
         with self.assertRaises(ValueError):
             row_iterator.to_arrow()
 
+    @unittest.skipIf(pandas is None, "Requires `pandas`")
+    def test_to_dataframe_iterable(self):
+        from google.cloud.bigquery.schema import SchemaField
+        import types
+
+        schema = [
+            SchemaField("name", "STRING", mode="REQUIRED"),
+            SchemaField("age", "INTEGER", mode="REQUIRED"),
+        ]
+
+        path = "/foo"
+        api_request = mock.Mock(
+            side_effect=[
+                {
+                    "rows": [{"f": [{"v": "Bengt"}, {"v": "32"}]}],
+                    "pageToken": "NEXTPAGE",
+                },
+                {"rows": [{"f": [{"v": "Sven"}, {"v": "33"}]}]},
+            ]
+        )
+
+        row_iterator = self._make_one(
+            _mock_client(), api_request, path, schema, page_size=1, max_results=5
+        )
+        dfs = row_iterator.to_dataframe_iterable()
+
+        self.assertIsInstance(dfs, types.GeneratorType)
+
+        df_1 = next(dfs)
+        self.assertIsInstance(df_1, pandas.DataFrame)
+        self.assertEqual(df_1.name.dtype.name, "object")
+        self.assertEqual(df_1.age.dtype.name, "int64")
+        self.assertEqual(len(df_1), 1)  # verify the number of rows
+        self.assertEqual(
+            df_1["name"][0], "Bengt"
+        )  # verify the first value of 'name' column
+        self.assertEqual(df_1["age"][0], 32)  # verify the first value of 'age' column
+
+        df_2 = next(dfs)
+        self.assertEqual(len(df_2), 1)  # verify the number of rows
+        self.assertEqual(df_2["name"][0], "Sven")
+        self.assertEqual(df_2["age"][0], 33)
+
+    @mock.patch("google.cloud.bigquery.table.pandas", new=None)
+    def test_to_dataframe_iterable_error_if_pandas_is_none(self):
+        from google.cloud.bigquery.schema import SchemaField
+
+        schema = [
+            SchemaField("name", "STRING", mode="REQUIRED"),
+            SchemaField("age", "INTEGER", mode="REQUIRED"),
+        ]
+        rows = [
+            {"f": [{"v": "Phred Phlyntstone"}, {"v": "32"}]},
+            {"f": [{"v": "Bharney Rhubble"}, {"v": "33"}]},
+        ]
+        path = "/foo"
+        api_request = mock.Mock(return_value={"rows": rows})
+        row_iterator = self._make_one(_mock_client(), api_request, path, schema)
+
+        with pytest.raises(ValueError, match="pandas"):
+            row_iterator.to_dataframe_iterable()
+
     @unittest.skipIf(pandas is None, "Requires `pandas`")
     def test_to_dataframe(self):
         from google.cloud.bigquery.schema import SchemaField