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python/pyspark/pandas/tests/resample/test_missing.py
134 строки
5 KB
Tian Gao
[SPARK-54906][PYTHON][TESTS] Unify test entry for all pyspark tests
07 янв 2026, 04:21
07 янв 2026, 04:21
c27ede3
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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 inspect import datetime import numpy as np import pandas as pd from pyspark import pandas as ps from pyspark.pandas.exceptions import PandasNotImplementedError from pyspark.pandas.missing.resample import ( MissingPandasLikeDataFrameResampler, MissingPandasLikeSeriesResampler, ) from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils class ResampleMissingMixin: @property def pdf1(self): np.random.seed(11) dates = [ pd.NaT, datetime.datetime(2011, 12, 31), datetime.datetime(2011, 12, 31, 0, 0, 1), datetime.datetime(2011, 12, 31, 23, 59, 59), datetime.datetime(2012, 1, 1), datetime.datetime(2012, 1, 1, 0, 0, 1), pd.NaT, datetime.datetime(2012, 1, 1, 23, 59, 59), datetime.datetime(2012, 1, 2), pd.NaT, datetime.datetime(2012, 1, 30, 23, 59, 59), datetime.datetime(2012, 1, 31), datetime.datetime(2012, 1, 31, 0, 0, 1), datetime.datetime(2012, 3, 31), datetime.datetime(2013, 5, 3), datetime.datetime(2022, 5, 3), ] return pd.DataFrame( np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates), columns=list("AB") ) @property def psdf1(self): return ps.from_pandas(self.pdf1) def test_missing(self): pdf_r = self.psdf1.resample("3D") pser_r = self.psdf1.A.resample("3D") # DataFrameResampler functions missing_functions = inspect.getmembers( MissingPandasLikeDataFrameResampler, inspect.isfunction ) unsupported_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "unsupported_function" ] for name in unsupported_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*Resampler.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(pdf_r, name)() # SeriesResampler functions missing_functions = inspect.getmembers(MissingPandasLikeSeriesResampler, inspect.isfunction) unsupported_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "unsupported_function" ] for name in unsupported_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*Resampler.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(pser_r, name)() # DataFrameResampler properties missing_properties = inspect.getmembers( MissingPandasLikeDataFrameResampler, lambda o: isinstance(o, property) ) unsupported_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "unsupported_property" ] for name in unsupported_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*Resampler.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(pdf_r, name) # SeriesResampler properties missing_properties = inspect.getmembers( MissingPandasLikeSeriesResampler, lambda o: isinstance(o, property) ) unsupported_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "unsupported_property" ] for name in unsupported_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*Resampler.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(pser_r, name) class ResampleMissingTests(ResampleMissingMixin, PandasOnSparkTestCase, TestUtils): pass if __name__ == "__main__": from pyspark.testing import main main()