| # |
| # 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 |
| |
| from pyspark import pandas as ps |
| from pyspark.testing.pandasutils import PandasOnSparkTestCase |
| from pyspark.testing.sqlutils import SQLTestUtils |
| |
| |
| class SeriesCumulativeMixin: |
| def test_cummin(self): |
| pser = pd.Series([1.0, None, 0.0, 4.0, 9.0]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cummin(), psser.cummin()) |
| self.assert_eq(pser.cummin(skipna=False), psser.cummin(skipna=False)) |
| self.assert_eq(pser.cummin().sum(), psser.cummin().sum()) |
| |
| # with reversed index |
| pser.index = [4, 3, 2, 1, 0] |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cummin(), psser.cummin()) |
| self.assert_eq(pser.cummin(skipna=False), psser.cummin(skipna=False)) |
| |
| def test_cummax(self): |
| pser = pd.Series([1.0, None, 0.0, 4.0, 9.0]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cummax(), psser.cummax()) |
| self.assert_eq(pser.cummax(skipna=False), psser.cummax(skipna=False)) |
| self.assert_eq(pser.cummax().sum(), psser.cummax().sum()) |
| |
| # with reversed index |
| pser.index = [4, 3, 2, 1, 0] |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cummax(), psser.cummax()) |
| self.assert_eq(pser.cummax(skipna=False), psser.cummax(skipna=False)) |
| |
| def test_cumsum(self): |
| pser = pd.Series([1.0, None, 0.0, 4.0, 9.0]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumsum(), psser.cumsum()) |
| self.assert_eq(pser.cumsum(skipna=False), psser.cumsum(skipna=False)) |
| self.assert_eq(pser.cumsum().sum(), psser.cumsum().sum()) |
| |
| # with reversed index |
| pser.index = [4, 3, 2, 1, 0] |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumsum(), psser.cumsum()) |
| self.assert_eq(pser.cumsum(skipna=False), psser.cumsum(skipna=False)) |
| |
| # bool |
| pser = pd.Series([True, True, False, True]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumsum().astype(int), psser.cumsum()) |
| self.assert_eq(pser.cumsum(skipna=False).astype(int), psser.cumsum(skipna=False)) |
| |
| with self.assertRaisesRegex(TypeError, r"Could not convert object \(string\) to numeric"): |
| ps.Series(["a", "b", "c", "d"]).cumsum() |
| |
| def test_cumprod(self): |
| pser = pd.Series([1.0, None, 1.0, 4.0, 9.0]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumprod(), psser.cumprod()) |
| self.assert_eq(pser.cumprod(skipna=False), psser.cumprod(skipna=False)) |
| self.assert_eq(pser.cumprod().sum(), psser.cumprod().sum()) |
| |
| # with integer type |
| pser = pd.Series([1, 10, 1, 4, 9]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumprod(), psser.cumprod()) |
| self.assert_eq(pser.cumprod(skipna=False), psser.cumprod(skipna=False)) |
| self.assert_eq(pser.cumprod().sum(), psser.cumprod().sum()) |
| |
| # with reversed index |
| pser.index = [4, 3, 2, 1, 0] |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumprod(), psser.cumprod()) |
| self.assert_eq(pser.cumprod(skipna=False), psser.cumprod(skipna=False)) |
| |
| # including zero |
| pser = pd.Series([1, 2, 0, 3]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumprod(), psser.cumprod()) |
| self.assert_eq(pser.cumprod(skipna=False), psser.cumprod(skipna=False)) |
| |
| # including negative values |
| pser = pd.Series([1, -1, -2]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumprod(), psser.cumprod()) |
| self.assert_eq(pser.cumprod(skipna=False), psser.cumprod(skipna=False)) |
| |
| # bool |
| pser = pd.Series([True, True, False, True]) |
| psser = ps.from_pandas(pser) |
| self.assert_eq(pser.cumprod(), psser.cumprod()) |
| self.assert_eq(pser.cumprod(skipna=False).astype(int), psser.cumprod(skipna=False)) |
| |
| with self.assertRaisesRegex(TypeError, r"Could not convert object \(string\) to numeric"): |
| ps.Series(["a", "b", "c", "d"]).cumprod() |
| |
| |
| class SeriesCumulativeTests( |
| SeriesCumulativeMixin, |
| PandasOnSparkTestCase, |
| SQLTestUtils, |
| ): |
| pass |
| |
| |
| if __name__ == "__main__": |
| from pyspark.pandas.tests.series.test_cumulative 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) |