# # 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 datetime from decimal import Decimal from distutils.version import LooseVersion import numpy as np import pandas as pd from pyspark import pandas as ps from pyspark.pandas.utils import name_like_string from pyspark.testing.pandasutils import PandasOnSparkTestCase class ReshapeTest(PandasOnSparkTestCase): def test_get_dummies(self): for pdf_or_ps in [ pd.Series([1, 1, 1, 2, 2, 1, 3, 4]), # pd.Series([1, 1, 1, 2, 2, 1, 3, 4], dtype='category'), # pd.Series(pd.Categorical([1, 1, 1, 2, 2, 1, 3, 4], # categories=[4, 3, 2, 1])), pd.DataFrame( { "a": [1, 2, 3, 4, 4, 3, 2, 1], # 'b': pd.Categorical(list('abcdabcd')), "b": list("abcdabcd"), } ), pd.DataFrame({10: [1, 2, 3, 4, 4, 3, 2, 1], 20: list("abcdabcd")}), ]: psdf_or_psser = ps.from_pandas(pdf_or_ps) self.assert_eq(ps.get_dummies(psdf_or_psser), pd.get_dummies(pdf_or_ps, dtype=np.int8)) psser = ps.Series([1, 1, 1, 2, 2, 1, 3, 4]) with self.assertRaisesRegex( NotImplementedError, "get_dummies currently does not support sparse" ): ps.get_dummies(psser, sparse=True) def test_get_dummies_object(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 4, 3, 2, 1], # 'a': pd.Categorical([1, 2, 3, 4, 4, 3, 2, 1]), "b": list("abcdabcd"), # 'c': pd.Categorical(list('abcdabcd')), "c": list("abcdabcd"), } ) psdf = ps.from_pandas(pdf) # Explicitly exclude object columns self.assert_eq( ps.get_dummies(psdf, columns=["a", "c"]), pd.get_dummies(pdf, columns=["a", "c"], dtype=np.int8), ) self.assert_eq(ps.get_dummies(psdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(psdf.b), pd.get_dummies(pdf.b, dtype=np.int8)) self.assert_eq( ps.get_dummies(psdf, columns=["b"]), pd.get_dummies(pdf, columns=["b"], dtype=np.int8) ) self.assertRaises(KeyError, lambda: ps.get_dummies(psdf, columns=("a", "c"))) self.assertRaises(TypeError, lambda: ps.get_dummies(psdf, columns="b")) # non-string names pdf = pd.DataFrame( {10: [1, 2, 3, 4, 4, 3, 2, 1], 20: list("abcdabcd"), 30: list("abcdabcd")} ) psdf = ps.from_pandas(pdf) self.assert_eq( ps.get_dummies(psdf, columns=[10, 30]), pd.get_dummies(pdf, columns=[10, 30], dtype=np.int8), ) self.assertRaises(TypeError, lambda: ps.get_dummies(psdf, columns=10)) def test_get_dummies_date_datetime(self): pdf = pd.DataFrame( { "d": [ datetime.date(2019, 1, 1), datetime.date(2019, 1, 2), datetime.date(2019, 1, 1), ], "dt": [ datetime.datetime(2019, 1, 1, 0, 0, 0), datetime.datetime(2019, 1, 1, 0, 0, 1), datetime.datetime(2019, 1, 1, 0, 0, 0), ], } ) psdf = ps.from_pandas(pdf) self.assert_eq(ps.get_dummies(psdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(psdf.d), pd.get_dummies(pdf.d, dtype=np.int8)) self.assert_eq(ps.get_dummies(psdf.dt), pd.get_dummies(pdf.dt, dtype=np.int8)) def test_get_dummies_boolean(self): pdf = pd.DataFrame({"b": [True, False, True]}) psdf = ps.from_pandas(pdf) self.assert_eq(ps.get_dummies(psdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(psdf.b), pd.get_dummies(pdf.b, dtype=np.int8)) def test_get_dummies_decimal(self): pdf = pd.DataFrame({"d": [Decimal(1.0), Decimal(2.0), Decimal(1)]}) psdf = ps.from_pandas(pdf) self.assert_eq(ps.get_dummies(psdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(psdf.d), pd.get_dummies(pdf.d, dtype=np.int8), almost=True) def test_get_dummies_kwargs(self): # pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4], dtype='category') pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4]) psser = ps.from_pandas(pser) self.assert_eq( ps.get_dummies(psser, prefix="X", prefix_sep="-"), pd.get_dummies(pser, prefix="X", prefix_sep="-", dtype=np.int8), ) self.assert_eq( ps.get_dummies(psser, drop_first=True), pd.get_dummies(pser, drop_first=True, dtype=np.int8), ) # nan # pser = pd.Series([1, 1, 1, 2, np.nan, 3, np.nan, 5], dtype='category') pser = pd.Series([1, 1, 1, 2, np.nan, 3, np.nan, 5]) psser = ps.from_pandas(pser) self.assert_eq(ps.get_dummies(psser), pd.get_dummies(pser, dtype=np.int8), almost=True) # dummy_na self.assert_eq( ps.get_dummies(psser, dummy_na=True), pd.get_dummies(pser, dummy_na=True, dtype=np.int8) ) def test_get_dummies_prefix(self): pdf = pd.DataFrame({"A": ["a", "b", "a"], "B": ["b", "a", "c"], "D": [0, 0, 1]}) psdf = ps.from_pandas(pdf) self.assert_eq( ps.get_dummies(psdf, prefix=["foo", "bar"]), pd.get_dummies(pdf, prefix=["foo", "bar"], dtype=np.int8), ) self.assert_eq( ps.get_dummies(psdf, prefix=["foo"], columns=["B"]), pd.get_dummies(pdf, prefix=["foo"], columns=["B"], dtype=np.int8), ) self.assert_eq( ps.get_dummies(psdf, prefix={"A": "foo", "B": "bar"}), pd.get_dummies(pdf, prefix={"A": "foo", "B": "bar"}, dtype=np.int8), ) self.assert_eq( ps.get_dummies(psdf, prefix={"B": "foo", "A": "bar"}), pd.get_dummies(pdf, prefix={"B": "foo", "A": "bar"}, dtype=np.int8), ) self.assert_eq( ps.get_dummies(psdf, prefix={"A": "foo", "B": "bar"}, columns=["A", "B"]), pd.get_dummies(pdf, prefix={"A": "foo", "B": "bar"}, columns=["A", "B"], dtype=np.int8), ) with self.assertRaisesRegex(NotImplementedError, "string types"): ps.get_dummies(psdf, prefix="foo") with self.assertRaisesRegex(ValueError, "Length of 'prefix' \\(1\\) .* \\(2\\)"): ps.get_dummies(psdf, prefix=["foo"]) with self.assertRaisesRegex(ValueError, "Length of 'prefix' \\(2\\) .* \\(1\\)"): ps.get_dummies(psdf, prefix=["foo", "bar"], columns=["B"]) pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4], name="A") psser = ps.from_pandas(pser) self.assert_eq( ps.get_dummies(psser, prefix="foo"), pd.get_dummies(pser, prefix="foo", dtype=np.int8) ) # columns are ignored. self.assert_eq( ps.get_dummies(psser, prefix=["foo"], columns=["B"]), pd.get_dummies(pser, prefix=["foo"], columns=["B"], dtype=np.int8), ) def test_get_dummies_dtype(self): pdf = pd.DataFrame( { # "A": pd.Categorical(['a', 'b', 'a'], categories=['a', 'b', 'c']), "A": ["a", "b", "a"], "B": [0, 0, 1], } ) psdf = ps.from_pandas(pdf) if LooseVersion("0.23.0") <= LooseVersion(pd.__version__): exp = pd.get_dummies(pdf, dtype="float64") else: exp = pd.get_dummies(pdf) exp = exp.astype({"A_a": "float64", "A_b": "float64"}) res = ps.get_dummies(psdf, dtype="float64") self.assert_eq(res, exp) def test_get_dummies_multiindex_columns(self): pdf = pd.DataFrame( { ("x", "a", "1"): [1, 2, 3, 4, 4, 3, 2, 1], ("x", "b", "2"): list("abcdabcd"), ("y", "c", "3"): list("abcdabcd"), } ) psdf = ps.from_pandas(pdf) self.assert_eq( ps.get_dummies(psdf), pd.get_dummies(pdf, dtype=np.int8).rename(columns=name_like_string), ) self.assert_eq( ps.get_dummies(psdf, columns=[("y", "c", "3"), ("x", "a", "1")]), pd.get_dummies(pdf, columns=[("y", "c", "3"), ("x", "a", "1")], dtype=np.int8).rename( columns=name_like_string ), ) self.assert_eq( ps.get_dummies(psdf, columns=["x"]), pd.get_dummies(pdf, columns=["x"], dtype=np.int8).rename(columns=name_like_string), ) self.assert_eq( ps.get_dummies(psdf, columns=("x", "a")), pd.get_dummies(pdf, columns=("x", "a"), dtype=np.int8).rename(columns=name_like_string), ) self.assertRaises(KeyError, lambda: ps.get_dummies(psdf, columns=["z"])) self.assertRaises(KeyError, lambda: ps.get_dummies(psdf, columns=("x", "c"))) self.assertRaises(ValueError, lambda: ps.get_dummies(psdf, columns=[("x",), "c"])) self.assertRaises(TypeError, lambda: ps.get_dummies(psdf, columns="x")) # non-string names pdf = pd.DataFrame( { ("x", 1, "a"): [1, 2, 3, 4, 4, 3, 2, 1], ("x", 2, "b"): list("abcdabcd"), ("y", 3, "c"): list("abcdabcd"), } ) psdf = ps.from_pandas(pdf) self.assert_eq( ps.get_dummies(psdf), pd.get_dummies(pdf, dtype=np.int8).rename(columns=name_like_string), ) self.assert_eq( ps.get_dummies(psdf, columns=[("y", 3, "c"), ("x", 1, "a")]), pd.get_dummies(pdf, columns=[("y", 3, "c"), ("x", 1, "a")], dtype=np.int8).rename( columns=name_like_string ), ) self.assert_eq( ps.get_dummies(psdf, columns=["x"]), pd.get_dummies(pdf, columns=["x"], dtype=np.int8).rename(columns=name_like_string), ) self.assert_eq( ps.get_dummies(psdf, columns=("x", 1)), pd.get_dummies(pdf, columns=("x", 1), dtype=np.int8).rename(columns=name_like_string), ) if __name__ == "__main__": import unittest from pyspark.pandas.tests.test_reshape import * # noqa: F401 try: import xmlrunner # type: ignore[import] testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)