superset compare 源码
superset compare 代码
文件路径:/superset/utils/pandas_postprocessing/compare.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 List, Optional
import pandas as pd
from flask_babel import gettext as _
from pandas import DataFrame
from superset.constants import PandasPostprocessingCompare
from superset.exceptions import InvalidPostProcessingError
from superset.utils.core import TIME_COMPARISON
from superset.utils.pandas_postprocessing.utils import validate_column_args
@validate_column_args("source_columns", "compare_columns")
def compare( # pylint: disable=too-many-arguments
df: DataFrame,
source_columns: List[str],
compare_columns: List[str],
compare_type: PandasPostprocessingCompare,
drop_original_columns: Optional[bool] = False,
precision: Optional[int] = 4,
) -> DataFrame:
"""
Calculate column-by-column changing for select columns.
:param df: DataFrame on which the compare will be based.
:param source_columns: Main query columns
:param compare_columns: Columns being compared
:param compare_type: Type of compare. Choice of `absolute`, `percentage` or `ratio`
:param drop_original_columns: Whether to remove the source columns and
compare columns.
:param precision: Round a change rate to a variable number of decimal places.
:return: DataFrame with compared columns.
:raises InvalidPostProcessingError: If the request in incorrect.
"""
if len(source_columns) != len(compare_columns):
raise InvalidPostProcessingError(
_("`compare_columns` must have the same length as `source_columns`.")
)
if compare_type not in tuple(PandasPostprocessingCompare):
raise InvalidPostProcessingError(
_("`compare_type` must be `difference`, `percentage` or `ratio`")
)
if len(source_columns) == 0:
return df
for s_col, c_col in zip(source_columns, compare_columns):
s_df = df.loc[:, [s_col]]
s_df.rename(columns={s_col: "__intermediate"}, inplace=True)
c_df = df.loc[:, [c_col]]
c_df.rename(columns={c_col: "__intermediate"}, inplace=True)
if compare_type == PandasPostprocessingCompare.DIFF:
diff_df = s_df - c_df
elif compare_type == PandasPostprocessingCompare.PCT:
diff_df = ((s_df - c_df) / c_df).astype(float).round(precision)
else:
# compare_type == "ratio"
diff_df = (s_df / c_df).astype(float).round(precision)
diff_df.rename(
columns={
"__intermediate": TIME_COMPARISON.join([compare_type, s_col, c_col])
},
inplace=True,
)
df = pd.concat([df, diff_df], axis=1)
if drop_original_columns:
df = df.drop(source_columns + compare_columns, axis=1)
return df
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