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74 lines
2.8 KiB
Python
74 lines
2.8 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, Dict, Optional
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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 QueryObjectValidationError
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from superset.utils.pandas_postprocessing.utils import (
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_append_columns,
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_flatten_column_after_pivot,
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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: Optional[Dict[str, str]] = None,
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is_pivot_df: bool = False,
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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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:param is_pivot_df: Dataframe is pivoted or not
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:return: DataFrame with cumulated columns
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"""
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columns = columns or {}
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if is_pivot_df:
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df_cum = df
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else:
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df_cum = df[columns.keys()]
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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 QueryObjectValidationError(
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_("Invalid cumulative operator: %(operator)s", operator=operator)
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)
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if is_pivot_df:
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df_cum = getattr(df_cum, operation)()
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agg_in_pivot_df = df.columns.get_level_values(0).drop_duplicates().to_list()
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agg: Dict[str, Dict[str, Any]] = {col: {} for col in agg_in_pivot_df}
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df_cum.columns = [
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_flatten_column_after_pivot(col, agg) for col in df_cum.columns
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]
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df_cum.reset_index(level=0, inplace=True)
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else:
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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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