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56 lines
2.1 KiB
Python
56 lines
2.1 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.pandas_postprocessing.select import select
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from tests.unit_tests.fixtures.dataframes import timeseries_df
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def test_select():
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# reorder columns
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post_df = select(df=timeseries_df, columns=["y", "label"])
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assert post_df.columns.tolist() == ["y", "label"]
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# one column
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post_df = select(df=timeseries_df, columns=["label"])
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assert post_df.columns.tolist() == ["label"]
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# rename and select one column
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post_df = select(df=timeseries_df, columns=["y"], rename={"y": "y1"})
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assert post_df.columns.tolist() == ["y1"]
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# rename one and leave one unchanged
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post_df = select(df=timeseries_df, rename={"y": "y1"})
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assert post_df.columns.tolist() == ["label", "y1"]
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# drop one column
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post_df = select(df=timeseries_df, exclude=["label"])
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assert post_df.columns.tolist() == ["y"]
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# rename and drop one column
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post_df = select(df=timeseries_df, rename={"y": "y1"}, exclude=["label"])
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assert post_df.columns.tolist() == ["y1"]
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# invalid columns
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with pytest.raises(InvalidPostProcessingError):
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select(df=timeseries_df, columns=["abc"], rename={"abc": "qwerty"})
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# select renamed column by new name
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with pytest.raises(InvalidPostProcessingError):
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select(df=timeseries_df, columns=["label_new"], rename={"label": "label_new"})
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