superset rolling 源码

superset rolling 代码

文件路径：/superset/utils/pandas_postprocessing/rolling.py

# 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 InvalidPostProcessingError
from superset.utils.pandas_postprocessing.utils import (
    _append_columns,
    DENYLIST_ROLLING_FUNCTIONS,
    validate_column_args,
)


@validate_column_args("columns")
def rolling(  # pylint: disable=too-many-arguments
    df: DataFrame,
    rolling_type: str,
    columns: Dict[str, str],
    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,
) -> 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.
    :return: DataFrame with the rolling columns
    :raises InvalidPostProcessingError: If the request in incorrect
    """
    rolling_type_options = rolling_type_options or {}
    df_rolling = df.loc[:, columns.keys()]

    kwargs: Dict[str, Union[str, int]] = {}
    if window is None:
        raise InvalidPostProcessingError(_("Undefined window for rolling operation"))
    if window == 0:
        raise InvalidPostProcessingError(_("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 InvalidPostProcessingError(
            _("Invalid rolling_type: %(type)s", type=rolling_type)
        )
    try:
        df_rolling = getattr(df_rolling, rolling_type)(**rolling_type_options)
    except TypeError as ex:
        raise InvalidPostProcessingError(
            _(
                "Invalid options for %(rolling_type)s: %(options)s",
                rolling_type=rolling_type,
                options=rolling_type_options,
            )
        ) from ex

    df_rolling = _append_columns(df, df_rolling, columns)

    if min_periods:
        df_rolling = df_rolling[min_periods:]
    return df_rolling

相关信息

superset 源码目录

相关文章

superset init 源码

superset aggregate 源码

superset boxplot 源码

superset compare 源码

superset contribution 源码

superset cum 源码

superset diff 源码

superset flatten 源码

superset geography 源码

superset pivot 源码