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| # 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. | |
| import warnings | |
| import pytest | |
| from superset.exceptions import InvalidPostProcessingError | |
| from superset.utils.core import PostProcessingBoxplotWhiskerType | |
| from superset.utils.pandas_postprocessing import boxplot | |
| from tests.unit_tests.fixtures.dataframes import names_df | |
| from tests.unit_tests.pandas_postprocessing.utils import series_to_list | |
| def test_boxplot_tukey(): | |
| df = boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.TUKEY, | |
| metrics=["cars"], | |
| ) | |
| columns = {column for column in df.columns} # noqa: C416 | |
| assert columns == { | |
| "cars__mean", | |
| "cars__median", | |
| "cars__q1", | |
| "cars__q3", | |
| "cars__max", | |
| "cars__min", | |
| "cars__count", | |
| "cars__outliers", | |
| "region", | |
| } | |
| assert len(df) == 4 | |
| def test_boxplot_mean_median_no_future_warning(): | |
| """mean/median must be passed as strings (not np.mean/np.median) to | |
| GroupBy.agg, else pandas raises a FutureWarning. Also verify the values | |
| match a plain pandas groupby, since the string and callable forms could | |
| silently diverge on a future pandas version.""" | |
| expected = names_df.groupby("region")["cars"].agg(["mean", "median"]) | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter("error", FutureWarning) | |
| df = boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.TUKEY, | |
| metrics=["cars"], | |
| ) | |
| df = df.set_index("region") | |
| assert series_to_list(df["cars__mean"]) == series_to_list(expected["mean"]) | |
| assert series_to_list(df["cars__median"]) == series_to_list(expected["median"]) | |
| def test_boxplot_minmax_no_future_warning(): | |
| """Under MINMAX, whisker_high/whisker_low are plain np.max/np.min, which | |
| must be passed as strings (not the callables) to GroupBy.agg, else pandas | |
| raises a FutureWarning. Also verify the values match a plain pandas | |
| groupby, since the string and callable forms could silently diverge on a | |
| future pandas version.""" | |
| expected = names_df.groupby("region")["cars"].agg(["max", "min"]) | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter("error", FutureWarning) | |
| df = boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.MINMAX, | |
| metrics=["cars"], | |
| ) | |
| df = df.set_index("region") | |
| assert series_to_list(df["cars__max"]) == series_to_list(expected["max"]) | |
| assert series_to_list(df["cars__min"]) == series_to_list(expected["min"]) | |
| def test_boxplot_min_max(): | |
| df = boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.MINMAX, | |
| metrics=["cars"], | |
| ) | |
| columns = {column for column in df.columns} # noqa: C416 | |
| assert columns == { | |
| "cars__mean", | |
| "cars__median", | |
| "cars__q1", | |
| "cars__q3", | |
| "cars__max", | |
| "cars__min", | |
| "cars__count", | |
| "cars__outliers", | |
| "region", | |
| } | |
| assert len(df) == 4 | |
| def test_boxplot_percentile(): | |
| df = boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE, | |
| metrics=["cars"], | |
| percentiles=[1, 99], | |
| ) | |
| columns = {column for column in df.columns} # noqa: C416 | |
| assert columns == { | |
| "cars__mean", | |
| "cars__median", | |
| "cars__q1", | |
| "cars__q3", | |
| "cars__max", | |
| "cars__min", | |
| "cars__count", | |
| "cars__outliers", | |
| "region", | |
| } | |
| assert len(df) == 4 | |
| def test_boxplot_percentile_incorrect_params(): | |
| with pytest.raises(InvalidPostProcessingError): | |
| boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE, | |
| metrics=["cars"], | |
| ) | |
| with pytest.raises(InvalidPostProcessingError): | |
| boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE, | |
| metrics=["cars"], | |
| percentiles=[10], | |
| ) | |
| with pytest.raises(InvalidPostProcessingError): | |
| boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE, | |
| metrics=["cars"], | |
| percentiles=[90, 10], | |
| ) | |
| with pytest.raises(InvalidPostProcessingError): | |
| boxplot( | |
| df=names_df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE, | |
| metrics=["cars"], | |
| percentiles=[10, 90, 10], | |
| ) | |
| def test_boxplot_type_coercion(): | |
| df = names_df | |
| df["cars"] = df["cars"].astype(str) | |
| df = boxplot( | |
| df=df, | |
| groupby=["region"], | |
| whisker_type=PostProcessingBoxplotWhiskerType.TUKEY, | |
| metrics=["cars"], | |
| ) | |
| columns = {column for column in df.columns} # noqa: C416 | |
| assert columns == { | |
| "cars__mean", | |
| "cars__median", | |
| "cars__q1", | |
| "cars__q3", | |
| "cars__max", | |
| "cars__min", | |
| "cars__count", | |
| "cars__outliers", | |
| "region", | |
| } | |
| assert len(df) == 4 | |