fix: datetime.data in series

superset/common/query_context_processor.py

@@ -32,7 +32,7 @@
 from superset.common.chart_data import ChartDataResultFormat
 from superset.common.db_query_status import QueryStatus
 from superset.common.query_actions import get_query_results
-from superset.common.utils import dataframe_utils as df_utils
+from superset.common.utils import dataframe_utils
 from superset.common.utils.query_cache_manager import QueryCacheManager
 from superset.connectors.base.models import BaseDatasource
 from superset.constants import CacheRegion
@@ -231,7 +231,7 @@ def normalize_df(self, df: pd.DataFrame, query_object: QueryObject) -> pd.DataFr
         )
 
         if self.enforce_numerical_metrics:
-            df_utils.df_metrics_to_num(df, query_object)
+            dataframe_utils.df_metrics_to_num(df, query_object)
 
         df.replace([np.inf, -np.inf], np.nan, inplace=True)
 
@@ -322,9 +322,7 @@ def processing_time_offsets(  # pylint: disable=too-many-locals
                 #  multi-dimensional charts
                 granularity = query_object.granularity
                 index = granularity if granularity in df.columns else DTTM_ALIAS
-                if not pd.api.types.is_datetime64_any_dtype(
-                    offset_metrics_df.get(index)
-                ):
+                if not dataframe_utils.is_datetime_series(offset_metrics_df.get(index)):
                     raise QueryObjectValidationError(
                         _(
                             "A time column must be specified "
@@ -337,7 +335,7 @@ def processing_time_offsets(  # pylint: disable=too-many-locals
                 )
 
             # df left join `offset_metrics_df`
-            offset_df = df_utils.left_join_df(
+            offset_df = dataframe_utils.left_join_df(
                 left_df=df,
                 right_df=offset_metrics_df,
                 join_keys=join_keys,

superset/common/utils/dataframe_utils.py

@@ -16,7 +16,8 @@
 # under the License.
 from __future__ import annotations
 
-from typing import List, TYPE_CHECKING
+import datetime
+from typing import Any, List, TYPE_CHECKING
 
 import numpy as np
 import pandas as pd
@@ -42,3 +43,15 @@ def df_metrics_to_num(df: pd.DataFrame, query_object: QueryObject) -> None:
             # soft-convert a metric column to numeric
             # will stay as strings if conversion fails
             df[col] = df[col].infer_objects()
+
+
+def is_datetime_series(series: Any) -> bool:
+    if series is None or not isinstance(series, pd.Series):
+        return False
+
+    if series.isnull().all():
+        return False
+
+    return pd.api.types.is_datetime64_any_dtype(series) or (
+        series.apply(lambda x: isinstance(x, datetime.date) or x is None).all()
+    )

tests/unit_tests/common/test_dataframe_utils.py

@@ -0,0 +1,50 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import datetime
+
+import pandas as pd
+
+from superset.common.utils import dataframe_utils
+
+
+def test_is_datetime_series():
+    assert not dataframe_utils.is_datetime_series(None)
+    assert not dataframe_utils.is_datetime_series(pd.DataFrame({"foo": [1]}))
+    assert not dataframe_utils.is_datetime_series(pd.Series([1, 2, 3]))
+    assert not dataframe_utils.is_datetime_series(pd.Series(["1", "2", "3"]))
+    assert not dataframe_utils.is_datetime_series(pd.Series())
+    assert not dataframe_utils.is_datetime_series(pd.Series([None, None]))
+    assert dataframe_utils.is_datetime_series(
+        pd.Series([datetime.date(2018, 1, 1), datetime.date(2018, 1, 2), None])
+    )
+    assert dataframe_utils.is_datetime_series(
+        pd.Series([datetime.date(2018, 1, 1), datetime.date(2018, 1, 2)])
+    )
+    assert dataframe_utils.is_datetime_series(
+        pd.Series([datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 2), None])
+    )
+    assert dataframe_utils.is_datetime_series(
+        pd.Series([datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 2)])
+    )
+    assert dataframe_utils.is_datetime_series(
+        pd.date_range(datetime.date(2018, 1, 1), datetime.date(2018, 2, 1)).to_series()
+    )
+    assert dataframe_utils.is_datetime_series(
+        pd.date_range(
+            datetime.datetime(2018, 1, 1), datetime.datetime(2018, 2, 1)
+        ).to_series()
+    )