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62 lines
2.3 KiB
Python
62 lines
2.3 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 Optional, Tuple, Union
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from pandas import DataFrame
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from superset.utils.pandas_postprocessing.utils import validate_column_args
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@validate_column_args("groupby_columns")
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def resample( # pylint: disable=too-many-arguments
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df: DataFrame,
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rule: str,
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method: str,
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time_column: str,
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groupby_columns: Optional[Tuple[Optional[str], ...]] = None,
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fill_value: Optional[Union[float, int]] = None,
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) -> DataFrame:
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"""
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support upsampling in resample
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:param df: DataFrame to resample.
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:param rule: The offset string representing target conversion.
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:param method: How to fill the NaN value after resample.
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:param time_column: existing columns in DataFrame.
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:param groupby_columns: columns except time_column in dataframe
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:param fill_value: What values do fill missing.
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:return: DataFrame after resample
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:raises QueryObjectValidationError: If the request in incorrect
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"""
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def _upsampling(_df: DataFrame) -> DataFrame:
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_df = _df.set_index(time_column)
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if method == "asfreq" and fill_value is not None:
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return _df.resample(rule).asfreq(fill_value=fill_value)
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return getattr(_df.resample(rule), method)()
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if groupby_columns:
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df = (
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df.set_index(keys=list(groupby_columns))
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.groupby(by=list(groupby_columns))
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.apply(_upsampling)
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)
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df = df.reset_index().set_index(time_column).sort_index()
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else:
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df = _upsampling(df)
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return df.reset_index()
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