refactor: decouple pandas postprocessing operator (#18710)

superset/utils/pandas_postprocessing/resample.py

@@ -0,0 +1,61 @@
+# 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.
+from typing import Optional, Tuple, Union
+
+from pandas import DataFrame
+
+from superset.utils.pandas_postprocessing.utils import validate_column_args
+
+
+@validate_column_args("groupby_columns")
+def resample(  # pylint: disable=too-many-arguments
+    df: DataFrame,
+    rule: str,
+    method: str,
+    time_column: str,
+    groupby_columns: Optional[Tuple[Optional[str], ...]] = None,
+    fill_value: Optional[Union[float, int]] = None,
+) -> DataFrame:
+    """
+    support upsampling in resample
+
+    :param df: DataFrame to resample.
+    :param rule: The offset string representing target conversion.
+    :param method: How to fill the NaN value after resample.
+    :param time_column: existing columns in DataFrame.
+    :param groupby_columns: columns except time_column in dataframe
+    :param fill_value: What values do fill missing.
+    :return: DataFrame after resample
+    :raises QueryObjectValidationError: If the request in incorrect
+    """
+
+    def _upsampling(_df: DataFrame) -> DataFrame:
+        _df = _df.set_index(time_column)
+        if method == "asfreq" and fill_value is not None:
+            return _df.resample(rule).asfreq(fill_value=fill_value)
+        return getattr(_df.resample(rule), method)()
+
+    if groupby_columns:
+        df = (
+            df.set_index(keys=list(groupby_columns))
+            .groupby(by=list(groupby_columns))
+            .apply(_upsampling)
+        )
+        df = df.reset_index().set_index(time_column).sort_index()
+    else:
+        df = _upsampling(df)
+    return df.reset_index()