superset select 源码
superset select 代码
文件路径：/superset/utils/pandas_postprocessing/select.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 Dict, List, Optional
from pandas import DataFrame
from superset.utils.pandas_postprocessing.utils import validate_column_args
@validate_column_args("columns", "drop", "rename")
def select(
df: DataFrame,
columns: Optional[List[str]] = None,
exclude: Optional[List[str]] = None,
rename: Optional[Dict[str, str]] = None,
) -> DataFrame:
"""
Only select a subset of columns in the original dataset. Can be useful for
removing unnecessary intermediate results, renaming and reordering columns.
:param df: DataFrame on which the rolling period will be based.
:param columns: Columns which to select from the DataFrame, in the desired order.
If left undefined, all columns will be selected. If columns are
renamed, the original column name should be referenced here.
:param exclude: columns to exclude from selection. If columns are renamed, the new
column name should be referenced here.
:param rename: columns which to rename, mapping source column to target column.
For instance, `{'y': 'y2'}` will rename the column `y` to
`y2`.
:return: Subset of columns in original DataFrame
:raises InvalidPostProcessingError: If the request in incorrect
"""
df_select = df.copy(deep=False)
if columns:
df_select = df_select[columns]
if exclude:
df_select = df_select.drop(exclude, axis=1)
if rename is not None:
df_select = df_select.rename(columns=rename)
return df_select
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