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47 lines
1.7 KiB
Python
47 lines
1.7 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, List
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from pandas import DataFrame
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from superset.utils.pandas_postprocessing.utils import (
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_get_aggregate_funcs,
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validate_column_args,
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)
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@validate_column_args("groupby")
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def aggregate(
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df: DataFrame, groupby: List[str], aggregates: Dict[str, Dict[str, Any]]
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) -> DataFrame:
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"""
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Apply aggregations to a DataFrame.
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:param df: Object to aggregate.
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:param groupby: columns to aggregate
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:param aggregates: A mapping from metric column to the function used to
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aggregate values.
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:raises QueryObjectValidationError: If the request in incorrect
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"""
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aggregates = aggregates or {}
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aggregate_funcs = _get_aggregate_funcs(df, aggregates)
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if groupby:
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df_groupby = df.groupby(by=groupby)
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else:
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df_groupby = df.groupby(lambda _: True)
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return df_groupby.agg(**aggregate_funcs).reset_index(drop=not groupby)
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