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102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from typing import Any, Optional, Union
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from flask_babel import gettext as _
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from pandas import DataFrame
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from superset.exceptions import InvalidPostProcessingError
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from superset.utils.pandas_postprocessing.utils import (
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_append_columns,
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DENYLIST_ROLLING_FUNCTIONS,
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validate_column_args,
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)
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@validate_column_args("columns")
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def rolling( # pylint: disable=too-many-arguments
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df: DataFrame,
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rolling_type: str,
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columns: dict[str, str],
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window: Optional[int] = None,
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rolling_type_options: Optional[dict[str, Any]] = None,
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center: bool = False,
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win_type: Optional[str] = None,
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min_periods: Optional[int] = None,
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) -> DataFrame:
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"""
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Apply a rolling window on the dataset. See the Pandas docs for further details:
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https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
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:param df: DataFrame on which the rolling period will be based.
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:param columns: columns on which to perform rolling, mapping source column to
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target column. For instance, `{'y': 'y'}` will replace the column `y` with
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the rolling value in `y`, while `{'y': 'y2'}` will add a column `y2` based
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on rolling values calculated from `y`, leaving the original column `y`
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unchanged.
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:param rolling_type: Type of rolling window. Any numpy function will work.
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:param window: Size of the window.
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:param rolling_type_options: Optional options to pass to rolling method. Needed
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for e.g. quantile operation.
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:param center: Should the label be at the center of the window.
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:param win_type: Type of window function.
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:param min_periods: The minimum amount of periods required for a row to be included
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in the result set.
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:return: DataFrame with the rolling columns
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:raises InvalidPostProcessingError: If the request in incorrect
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"""
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rolling_type_options = rolling_type_options or {}
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df_rolling = df.loc[:, columns.keys()]
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kwargs: dict[str, Union[str, int]] = {}
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if window is None:
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raise InvalidPostProcessingError(_("Undefined window for rolling operation"))
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if window == 0:
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raise InvalidPostProcessingError(_("Window must be > 0"))
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kwargs["window"] = window
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if min_periods is not None:
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kwargs["min_periods"] = min_periods
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if center is not None:
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kwargs["center"] = center
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if win_type is not None:
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kwargs["win_type"] = win_type
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df_rolling = df_rolling.rolling(**kwargs)
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if rolling_type not in DENYLIST_ROLLING_FUNCTIONS or not hasattr(
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df_rolling, rolling_type
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):
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raise InvalidPostProcessingError(
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_("Invalid rolling_type: %(type)s", type=rolling_type)
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)
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try:
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df_rolling = getattr(df_rolling, rolling_type)(**rolling_type_options)
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except TypeError as ex:
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raise InvalidPostProcessingError(
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_(
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"Invalid options for %(rolling_type)s: %(options)s",
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rolling_type=rolling_type,
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options=rolling_type_options,
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)
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) from ex
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df_rolling = _append_columns(df, df_rolling, columns)
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if min_periods:
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df_rolling = df_rolling[min_periods - 1 :]
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return df_rolling
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