text: # 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 Dict
  
  from flask_babel import gettext as _
  from pandas import DataFrame
  
  from superset.exceptions import InvalidPostProcessingError
  from superset.utils.pandas_postprocessing.utils import (
      _append_columns,
      ALLOWLIST_CUMULATIVE_FUNCTIONS,
      validate_column_args,
  )
  
  
  @validate_column_args("columns")
  def cum(df: DataFrame, operator: str, columns: Dict[str, str],) -> DataFrame:
      """
      Calculate cumulative sum/product/min/max for select columns.
  
      :param df: DataFrame on which the cumulative operation will be based.
      :param columns: columns on which to perform a cumulative operation, mapping source
             column to target column. For instance, `{'y': 'y'}` will replace the column
             `y` with the cumulative value in `y`, while `{'y': 'y2'}` will add a column
             `y2` based on cumulative values calculated from `y`, leaving the original
             column `y` unchanged.
      :param operator: cumulative operator, e.g. `sum`, `prod`, `min`, `max`
      :return: DataFrame with cumulated columns
      """
      columns = columns or {}
      df_cum = df.loc[:, columns.keys()]
      operation = "cum" + operator
      if operation not in ALLOWLIST_CUMULATIVE_FUNCTIONS or not hasattr(
          df_cum, operation
      ):
          raise InvalidPostProcessingError(
              _("Invalid cumulative operator: %(operator)s", operator=operator)
          )
      df_cum = _append_columns(df, getattr(df_cum, operation)(), columns)
      return df_cum
  