refactor: decouple pandas postprocessing operator (#18710)

superset/utils/pandas_postprocessing/rolling.py

@@ -0,0 +1,115 @@
+# 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 Any, Dict, Optional, Union
+
+from flask_babel import gettext as _
+from pandas import DataFrame
+
+from superset.exceptions import QueryObjectValidationError
+from superset.utils.pandas_postprocessing.utils import (
+    _append_columns,
+    _flatten_column_after_pivot,
+    DENYLIST_ROLLING_FUNCTIONS,
+    validate_column_args,
+)
+
+
+@validate_column_args("columns")
+def rolling(  # pylint: disable=too-many-arguments
+    df: DataFrame,
+    rolling_type: str,
+    columns: Optional[Dict[str, str]] = None,
+    window: Optional[int] = None,
+    rolling_type_options: Optional[Dict[str, Any]] = None,
+    center: bool = False,
+    win_type: Optional[str] = None,
+    min_periods: Optional[int] = None,
+    is_pivot_df: bool = False,
+) -> DataFrame:
+    """
+    Apply a rolling window on the dataset. See the Pandas docs for further details:
+    https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
+
+    :param df: DataFrame on which the rolling period will be based.
+    :param columns: columns on which to perform rolling, mapping source column to
+           target column. For instance, `{'y': 'y'}` will replace the column `y` with
+           the rolling value in `y`, while `{'y': 'y2'}` will add a column `y2` based
+           on rolling values calculated from `y`, leaving the original column `y`
+           unchanged.
+    :param rolling_type: Type of rolling window. Any numpy function will work.
+    :param window: Size of the window.
+    :param rolling_type_options: Optional options to pass to rolling method. Needed
+           for e.g. quantile operation.
+    :param center: Should the label be at the center of the window.
+    :param win_type: Type of window function.
+    :param min_periods: The minimum amount of periods required for a row to be included
+                        in the result set.
+    :param is_pivot_df: Dataframe is pivoted or not
+    :return: DataFrame with the rolling columns
+    :raises QueryObjectValidationError: If the request in incorrect
+    """
+    rolling_type_options = rolling_type_options or {}
+    columns = columns or {}
+    if is_pivot_df:
+        df_rolling = df
+    else:
+        df_rolling = df[columns.keys()]
+    kwargs: Dict[str, Union[str, int]] = {}
+    if window is None:
+        raise QueryObjectValidationError(_("Undefined window for rolling operation"))
+    if window == 0:
+        raise QueryObjectValidationError(_("Window must be > 0"))
+
+    kwargs["window"] = window
+    if min_periods is not None:
+        kwargs["min_periods"] = min_periods
+    if center is not None:
+        kwargs["center"] = center
+    if win_type is not None:
+        kwargs["win_type"] = win_type
+
+    df_rolling = df_rolling.rolling(**kwargs)
+    if rolling_type not in DENYLIST_ROLLING_FUNCTIONS or not hasattr(
+        df_rolling, rolling_type
+    ):
+        raise QueryObjectValidationError(
+            _("Invalid rolling_type: %(type)s", type=rolling_type)
+        )
+    try:
+        df_rolling = getattr(df_rolling, rolling_type)(**rolling_type_options)
+    except TypeError as ex:
+        raise QueryObjectValidationError(
+            _(
+                "Invalid options for %(rolling_type)s: %(options)s",
+                rolling_type=rolling_type,
+                options=rolling_type_options,
+            )
+        ) from ex
+
+    if is_pivot_df:
+        agg_in_pivot_df = df.columns.get_level_values(0).drop_duplicates().to_list()
+        agg: Dict[str, Dict[str, Any]] = {col: {} for col in agg_in_pivot_df}
+        df_rolling.columns = [
+            _flatten_column_after_pivot(col, agg) for col in df_rolling.columns
+        ]
+        df_rolling.reset_index(level=0, inplace=True)
+    else:
+        df_rolling = _append_columns(df, df_rolling, columns)
+
+    if min_periods:
+        df_rolling = df_rolling[min_periods:]
+    return df_rolling