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* fix: Box Plot Chart throws an error when the average (AVG) / SUM is being calculated on the Metrics * add test
152 lines
4.1 KiB
Python
152 lines
4.1 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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import pytest
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from superset.exceptions import InvalidPostProcessingError
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from superset.utils.core import PostProcessingBoxplotWhiskerType
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from superset.utils.pandas_postprocessing import boxplot
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from tests.unit_tests.fixtures.dataframes import names_df
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def test_boxplot_tukey():
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df = boxplot(
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df=names_df,
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groupby=["region"],
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whisker_type=PostProcessingBoxplotWhiskerType.TUKEY,
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metrics=["cars"],
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)
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columns = {column for column in df.columns}
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assert columns == {
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"cars__mean",
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"cars__median",
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"cars__q1",
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"cars__q3",
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"cars__max",
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"cars__min",
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"cars__count",
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"cars__outliers",
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"region",
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}
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assert len(df) == 4
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def test_boxplot_min_max():
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df = boxplot(
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df=names_df,
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groupby=["region"],
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whisker_type=PostProcessingBoxplotWhiskerType.MINMAX,
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metrics=["cars"],
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)
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columns = {column for column in df.columns}
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assert columns == {
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"cars__mean",
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"cars__median",
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"cars__q1",
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"cars__q3",
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"cars__max",
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"cars__min",
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"cars__count",
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"cars__outliers",
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"region",
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}
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assert len(df) == 4
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def test_boxplot_percentile():
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df = boxplot(
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df=names_df,
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groupby=["region"],
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whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE,
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metrics=["cars"],
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percentiles=[1, 99],
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)
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columns = {column for column in df.columns}
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assert columns == {
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"cars__mean",
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"cars__median",
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"cars__q1",
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"cars__q3",
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"cars__max",
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"cars__min",
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"cars__count",
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"cars__outliers",
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"region",
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}
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assert len(df) == 4
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def test_boxplot_percentile_incorrect_params():
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with pytest.raises(InvalidPostProcessingError):
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boxplot(
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df=names_df,
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groupby=["region"],
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whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE,
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metrics=["cars"],
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)
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with pytest.raises(InvalidPostProcessingError):
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boxplot(
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df=names_df,
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groupby=["region"],
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whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE,
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metrics=["cars"],
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percentiles=[10],
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)
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with pytest.raises(InvalidPostProcessingError):
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boxplot(
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df=names_df,
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groupby=["region"],
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whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE,
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metrics=["cars"],
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percentiles=[90, 10],
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)
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with pytest.raises(InvalidPostProcessingError):
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boxplot(
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df=names_df,
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groupby=["region"],
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whisker_type=PostProcessingBoxplotWhiskerType.PERCENTILE,
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metrics=["cars"],
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percentiles=[10, 90, 10],
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)
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def test_boxplot_type_coercion():
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df = names_df
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df["cars"] = df["cars"].astype(str)
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df = boxplot(
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df=df,
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groupby=["region"],
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whisker_type=PostProcessingBoxplotWhiskerType.TUKEY,
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metrics=["cars"],
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)
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columns = {column for column in df.columns}
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assert columns == {
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"cars__mean",
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"cars__median",
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"cars__q1",
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"cars__q3",
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"cars__max",
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"cars__min",
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"cars__count",
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"cars__outliers",
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"region",
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}
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assert len(df) == 4
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