| # |
| # 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 numpy as np |
| import pandas as pd |
| |
| from pyspark import pandas as ps |
| from pyspark.testing.pandasutils import PandasOnSparkTestCase |
| from pyspark.testing.sqlutils import SQLTestUtils |
| |
| |
| class PivotTableMixin: |
| def test_pivot_table(self): |
| pdf = pd.DataFrame( |
| { |
| "a": [4, 2, 3, 4, 8, 6], |
| "b": [1, 2, 2, 4, 2, 4], |
| "e": [10, 20, 20, 40, 20, 40], |
| "c": [1, 2, 9, 4, 7, 4], |
| "d": [-1, -2, -3, -4, -5, -6], |
| }, |
| index=np.random.rand(6), |
| ) |
| psdf = ps.from_pandas(pdf) |
| |
| self.assert_eq( |
| psdf.pivot_table(columns="a", values="b").sort_index(), |
| pdf.pivot_table(columns="a", values="b").sort_index(), |
| almost=True, |
| ) |
| |
| self.assert_eq( |
| psdf.pivot_table(index=["c"], columns="a", values="b").sort_index(), |
| pdf.pivot_table(index=["c"], columns="a", values="b").sort_index(), |
| almost=True, |
| ) |
| |
| self.assert_eq( |
| psdf.pivot_table(index=["c"], columns="a", values="b", aggfunc="sum").sort_index(), |
| pdf.pivot_table(index=["c"], columns="a", values="b", aggfunc="sum").sort_index(), |
| almost=True, |
| ) |
| |
| self.assert_eq( |
| psdf.pivot_table(index=["c"], columns="a", values=["b"], aggfunc="sum").sort_index(), |
| pdf.pivot_table(index=["c"], columns="a", values=["b"], aggfunc="sum").sort_index(), |
| almost=True, |
| ) |
| |
| self.assert_eq( |
| psdf.pivot_table( |
| index=["c"], columns="a", values=["b", "e"], aggfunc="sum" |
| ).sort_index(), |
| pdf.pivot_table( |
| index=["c"], columns="a", values=["b", "e"], aggfunc="sum" |
| ).sort_index(), |
| almost=True, |
| ) |
| |
| |
| class PivotTableTests( |
| PivotTableMixin, |
| PandasOnSparkTestCase, |
| SQLTestUtils, |
| ): |
| pass |
| |
| |
| if __name__ == "__main__": |
| from pyspark.pandas.tests.computation.test_pivot_table 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) |