| # |
| # 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 unittest |
| |
| import pandas as pd |
| import numpy as np |
| import pyspark.pandas as ps |
| from pyspark.testing.pandasutils import PandasOnSparkTestCase |
| |
| |
| class ValueCountsTestsMixin: |
| def test_value_counts(self): |
| pdf = pd.DataFrame( |
| {"A": [np.nan, 2, 2, 3, 3, 3], "B": [1, 1, 2, 3, 3, np.nan]}, columns=["A", "B"] |
| ) |
| psdf = ps.from_pandas(pdf) |
| self.assert_eq( |
| psdf.groupby("A")["B"].value_counts().sort_index(), |
| pdf.groupby("A")["B"].value_counts().sort_index(), |
| ) |
| self.assert_eq( |
| psdf.groupby("A")["B"].value_counts(dropna=False).sort_index(), |
| pdf.groupby("A")["B"].value_counts(dropna=False).sort_index(), |
| almost=True, |
| ) |
| self.assert_eq( |
| psdf.groupby("A", dropna=False)["B"].value_counts(dropna=False).sort_index(), |
| pdf.groupby("A", dropna=False)["B"].value_counts(dropna=False).sort_index(), |
| # Returns are the same considering values and types, |
| # disable check_exact to pass the assert_eq |
| check_exact=False, |
| ) |
| self.assert_eq( |
| psdf.groupby("A")["B"].value_counts(sort=True, ascending=False).sort_index(), |
| pdf.groupby("A")["B"].value_counts(sort=True, ascending=False).sort_index(), |
| ) |
| self.assert_eq( |
| psdf.groupby("A")["B"] |
| .value_counts(sort=True, ascending=False, dropna=False) |
| .sort_index(), |
| pdf.groupby("A")["B"] |
| .value_counts(sort=True, ascending=False, dropna=False) |
| .sort_index(), |
| almost=True, |
| ) |
| self.assert_eq( |
| psdf.groupby("A")["B"] |
| .value_counts(sort=True, ascending=True, dropna=False) |
| .sort_index(), |
| pdf.groupby("A")["B"] |
| .value_counts(sort=True, ascending=True, dropna=False) |
| .sort_index(), |
| almost=True, |
| ) |
| self.assert_eq( |
| psdf.B.rename().groupby(psdf.A).value_counts().sort_index(), |
| pdf.B.rename().groupby(pdf.A).value_counts().sort_index(), |
| ) |
| self.assert_eq( |
| psdf.B.rename().groupby(psdf.A, dropna=False).value_counts().sort_index(), |
| pdf.B.rename().groupby(pdf.A, dropna=False).value_counts().sort_index(), |
| # Returns are the same considering values and types, |
| # disable check_exact to pass the assert_eq |
| check_exact=False, |
| ) |
| self.assert_eq( |
| psdf.B.groupby(psdf.A.rename()).value_counts().sort_index(), |
| pdf.B.groupby(pdf.A.rename()).value_counts().sort_index(), |
| ) |
| self.assert_eq( |
| psdf.B.rename().groupby(psdf.A.rename()).value_counts().sort_index(), |
| pdf.B.rename().groupby(pdf.A.rename()).value_counts().sort_index(), |
| ) |
| |
| |
| class ValueCountsTests( |
| ValueCountsTestsMixin, |
| PandasOnSparkTestCase, |
| ): |
| pass |
| |
| |
| if __name__ == "__main__": |
| from pyspark.pandas.tests.groupby.test_value_counts import * # noqa: F401 |
| |
| try: |
| import xmlrunner |
| |
| testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) |
| except ImportError: |
| testRunner = None |
| unittest.main(testRunner=testRunner, verbosity=2) |