| # |
| # 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 GroupbyIndexMixin: |
| def test_groupby_multiindex_columns(self): |
| pdf = pd.DataFrame( |
| { |
| (10, "a"): [1, 2, 6, 4, 4, 6, 4, 3, 7], |
| (10, "b"): [4, 2, 7, 3, 3, 1, 1, 1, 2], |
| (20, "c"): [4, 2, 7, 3, None, 1, 1, 1, 2], |
| (30, "d"): list("abcdefght"), |
| }, |
| index=[0, 1, 3, 5, 6, 8, 9, 9, 9], |
| ) |
| psdf = ps.from_pandas(pdf) |
| |
| self.assert_eq( |
| psdf.groupby((10, "a")).sum().sort_index(), pdf.groupby((10, "a")).sum().sort_index() |
| ) |
| self.assert_eq( |
| psdf.groupby((10, "a"), as_index=False) |
| .sum() |
| .sort_values((10, "a")) |
| .reset_index(drop=True), |
| pdf.groupby((10, "a"), as_index=False) |
| .sum() |
| .sort_values((10, "a")) |
| .reset_index(drop=True), |
| ) |
| self.assert_eq( |
| psdf.groupby((10, "a"))[[(20, "c")]].sum().sort_index(), |
| pdf.groupby((10, "a"))[[(20, "c")]].sum().sort_index(), |
| ) |
| |
| # TODO: a pandas bug? |
| # expected = pdf.groupby((10, "a"))[(20, "c")].sum().sort_index() |
| expected = pd.Series( |
| [4.0, 2.0, 1.0, 4.0, 8.0, 2.0], |
| name=(20, "c"), |
| index=pd.Index([1, 2, 3, 4, 6, 7], name=(10, "a")), |
| ) |
| |
| self.assert_eq(psdf.groupby((10, "a"))[(20, "c")].sum().sort_index(), expected) |
| |
| self.assert_eq( |
| psdf[(20, "c")].groupby(psdf[(10, "a")]).sum().sort_index(), |
| pdf[(20, "c")].groupby(pdf[(10, "a")]).sum().sort_index(), |
| ) |
| |
| def test_idxmax(self): |
| pdf = pd.DataFrame( |
| {"a": [1, 1, 2, 2, 3] * 3, "b": [1, 2, 3, 4, 5] * 3, "c": [5, 4, 3, 2, 1] * 3} |
| ) |
| psdf = ps.from_pandas(pdf) |
| |
| self.assert_eq( |
| pdf.groupby(["a"]).idxmax().sort_index(), psdf.groupby(["a"]).idxmax().sort_index() |
| ) |
| self.assert_eq( |
| pdf.groupby(["a"]).idxmax(skipna=False).sort_index(), |
| psdf.groupby(["a"]).idxmax(skipna=False).sort_index(), |
| ) |
| self.assert_eq( |
| pdf.groupby(["a"])["b"].idxmax().sort_index(), |
| psdf.groupby(["a"])["b"].idxmax().sort_index(), |
| ) |
| self.assert_eq( |
| pdf.b.rename().groupby(pdf.a).idxmax().sort_index(), |
| psdf.b.rename().groupby(psdf.a).idxmax().sort_index(), |
| ) |
| self.assert_eq( |
| pdf.b.groupby(pdf.a.rename()).idxmax().sort_index(), |
| psdf.b.groupby(psdf.a.rename()).idxmax().sort_index(), |
| ) |
| self.assert_eq( |
| pdf.b.rename().groupby(pdf.a.rename()).idxmax().sort_index(), |
| psdf.b.rename().groupby(psdf.a.rename()).idxmax().sort_index(), |
| ) |
| |
| with self.assertRaisesRegex(ValueError, "idxmax only support one-level index now"): |
| psdf.set_index(["a", "b"]).groupby(["c"]).idxmax() |
| |
| # multi-index columns |
| columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) |
| pdf.columns = columns |
| psdf.columns = columns |
| |
| self.assert_eq( |
| pdf.groupby(("x", "a")).idxmax().sort_index(), |
| psdf.groupby(("x", "a")).idxmax().sort_index(), |
| ) |
| self.assert_eq( |
| pdf.groupby(("x", "a")).idxmax(skipna=False).sort_index(), |
| psdf.groupby(("x", "a")).idxmax(skipna=False).sort_index(), |
| ) |
| |
| def test_idxmin(self): |
| pdf = pd.DataFrame( |
| {"a": [1, 1, 2, 2, 3] * 3, "b": [1, 2, 3, 4, 5] * 3, "c": [5, 4, 3, 2, 1] * 3} |
| ) |
| psdf = ps.from_pandas(pdf) |
| |
| self.assert_eq( |
| pdf.groupby(["a"]).idxmin().sort_index(), psdf.groupby(["a"]).idxmin().sort_index() |
| ) |
| self.assert_eq( |
| pdf.groupby(["a"]).idxmin(skipna=False).sort_index(), |
| psdf.groupby(["a"]).idxmin(skipna=False).sort_index(), |
| ) |
| self.assert_eq( |
| pdf.groupby(["a"])["b"].idxmin().sort_index(), |
| psdf.groupby(["a"])["b"].idxmin().sort_index(), |
| ) |
| self.assert_eq( |
| pdf.b.rename().groupby(pdf.a).idxmin().sort_index(), |
| psdf.b.rename().groupby(psdf.a).idxmin().sort_index(), |
| ) |
| self.assert_eq( |
| pdf.b.groupby(pdf.a.rename()).idxmin().sort_index(), |
| psdf.b.groupby(psdf.a.rename()).idxmin().sort_index(), |
| ) |
| self.assert_eq( |
| pdf.b.rename().groupby(pdf.a.rename()).idxmin().sort_index(), |
| psdf.b.rename().groupby(psdf.a.rename()).idxmin().sort_index(), |
| ) |
| |
| with self.assertRaisesRegex(ValueError, "idxmin only support one-level index now"): |
| psdf.set_index(["a", "b"]).groupby(["c"]).idxmin() |
| |
| # multi-index columns |
| columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) |
| pdf.columns = columns |
| psdf.columns = columns |
| |
| self.assert_eq( |
| pdf.groupby(("x", "a")).idxmin().sort_index(), |
| psdf.groupby(("x", "a")).idxmin().sort_index(), |
| ) |
| self.assert_eq( |
| pdf.groupby(("x", "a")).idxmin(skipna=False).sort_index(), |
| psdf.groupby(("x", "a")).idxmin(skipna=False).sort_index(), |
| ) |
| |
| |
| class GroupbyIndexTests( |
| GroupbyIndexMixin, |
| PandasOnSparkTestCase, |
| SQLTestUtils, |
| ): |
| pass |
| |
| |
| if __name__ == "__main__": |
| from pyspark.pandas.tests.groupby.test_index 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) |