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59 lines
2.2 KiB
Python
59 lines
2.2 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 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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ALLOWLIST_CUMULATIVE_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 cum(
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df: DataFrame,
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operator: str,
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columns: dict[str, str],
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) -> DataFrame:
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"""
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Calculate cumulative sum/product/min/max for select columns.
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:param df: DataFrame on which the cumulative operation will be based.
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:param columns: columns on which to perform a cumulative operation, mapping source
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column to target column. For instance, `{'y': 'y'}` will replace the column
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`y` with the cumulative value in `y`, while `{'y': 'y2'}` will add a column
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`y2` based on cumulative values calculated from `y`, leaving the original
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column `y` unchanged.
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:param operator: cumulative operator, e.g. `sum`, `prod`, `min`, `max`
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:return: DataFrame with cumulated columns
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"""
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columns = columns or {}
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df_cum = df.loc[:, columns.keys()]
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df_cum = df_cum.fillna(0)
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operation = "cum" + operator
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if operation not in ALLOWLIST_CUMULATIVE_FUNCTIONS or not hasattr(
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df_cum, operation
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):
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raise InvalidPostProcessingError(
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_("Invalid cumulative operator: %(operator)s", operator=operator)
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)
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df_cum = _append_columns(df, getattr(df_cum, operation)(), columns)
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return df_cum
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