update to work with db-dtypes 0.2.0+

google/cloud/bigquery/_pandas_helpers.py

@@ -29,7 +29,10 @@
 else:
     import numpy
 
-    from db_dtypes import date_dtype_name, time_dtype_name
+    from db_dtypes import DateDtype, TimeDtype
+
+    date_dtype_name = DateDtype.name
+    time_dtype_name = TimeDtype.name
 
 import pyarrow
 import pyarrow.parquet

testing/constraints-3.6.txt

@@ -5,6 +5,7 @@
 #
 # e.g., if setup.py has "foo >= 1.14.0, < 2.0.0dev",
 # Then this file should have foo==1.14.0
+db-dtypes==0.2.0
 geopandas==0.9.0
 google-api-core==1.29.0
 google-cloud-bigquery-storage==2.0.0

tests/unit/job/test_query_pandas.py

@@ -630,7 +630,7 @@ def test_to_dataframe_column_dtypes():
     assert df.km.dtype.name == "float16"
     assert df.payment_type.dtype.name == "object"
     assert df.complete.dtype.name == "boolean"
-    assert df.date.dtype.name == "date"
+    assert df.date.dtype.name == "dbdate"
 
 
 def test_to_dataframe_column_date_dtypes():
@@ -661,7 +661,7 @@ def test_to_dataframe_column_date_dtypes():
     assert len(df) == 1  # verify the number of rows
     exp_columns = [field["name"] for field in query_resource["schema"]["fields"]]
     assert list(df) == exp_columns  # verify the column names
-    assert df.date.dtype.name == "date"
+    assert df.date.dtype.name == "dbdate"
 
 
 @pytest.mark.skipif(tqdm is None, reason="Requires `tqdm`")

tests/unit/test_client.py

@@ -5227,8 +5227,8 @@ def test_insert_rows_from_dataframe(self):
             },
         ]
         dataframe = pandas.DataFrame(data)
-        for dtype in "date", "time":
-            dataframe["b" + dtype] = pandas.Series(dataframe["b" + dtype], dtype=dtype)
+        dataframe["bdate"] = dataframe["bdate"].astype("dbdate")
+        dataframe["btime"] = dataframe["btime"].astype("dbtime")
 
         # create client
         creds = _make_credentials()
@@ -7091,7 +7091,7 @@ def test_load_table_from_dataframe_w_automatic_schema(self):
                             datetime.date(2011, 2, 3),
                             datetime.date(2012, 3, 14),
                         ],
-                        dtype="date",
+                        dtype="dbdate",
                     ),
                 ),
                 (
@@ -7102,7 +7102,7 @@ def test_load_table_from_dataframe_w_automatic_schema(self):
                             datetime.time(14, 50, 59),
                             datetime.time(15, 16),
                         ],
-                        dtype="time",
+                        dtype="dbtime",
                     ),
                 ),
             ]

tests/unit/test_table.py

@@ -3115,7 +3115,7 @@ def test_to_dataframe_column_dtypes(self):
         self.assertEqual(df.km.dtype.name, "float16")
         self.assertEqual(df.payment_type.dtype.name, "object")
         self.assertEqual(df.complete.dtype.name, "boolean")
-        self.assertEqual(df.date.dtype.name, "date")
+        self.assertEqual(df.date.dtype.name, "dbdate")
 
     @unittest.skipIf(pandas is None, "Requires `pandas`")
     def test_to_dataframe_datetime_objects(self):

tests/unit/test_table_pandas.py

@@ -101,13 +101,13 @@ def test_to_dataframe_nullable_scalars(monkeypatch, class_under_test):
     assert df.dtypes["bignumeric_col"].name == "object"
     assert df.dtypes["bool_col"].name == "boolean"
     assert df.dtypes["bytes_col"].name == "object"
-    assert df.dtypes["date_col"].name == "date"
+    assert df.dtypes["date_col"].name == "dbdate"
     assert df.dtypes["datetime_col"].name == "datetime64[ns]"
     assert df.dtypes["float64_col"].name == "float64"
     assert df.dtypes["int64_col"].name == "Int64"
     assert df.dtypes["numeric_col"].name == "object"
     assert df.dtypes["string_col"].name == "object"
-    assert df.dtypes["time_col"].name == "time"
+    assert df.dtypes["time_col"].name == "dbtime"
     assert df.dtypes["timestamp_col"].name == "datetime64[ns, UTC]"
 
     # Check for expected values.