# # 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 import pyspark from pyspark import pandas as ps from pyspark.pandas.testing.utils import ReusedSQLTestCase, SPARK_CONF_ARROW_ENABLED from pyspark.pandas.utils import name_like_string class ReshapeTest(ReusedSQLTestCase): 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")}), ]: kdf_or_kser = ps.from_pandas(pdf_or_ps) self.assert_eq(ps.get_dummies(kdf_or_kser), pd.get_dummies(pdf_or_ps, dtype=np.int8)) kser = ps.Series([1, 1, 1, 2, 2, 1, 3, 4]) with self.assertRaisesRegex( NotImplementedError, "get_dummies currently does not support sparse" ): ps.get_dummies(kser, 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"), } ) kdf = ps.from_pandas(pdf) # Explicitly exclude object columns self.assert_eq( ps.get_dummies(kdf, columns=["a", "c"]), pd.get_dummies(pdf, columns=["a", "c"], dtype=np.int8), ) self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(kdf.b), pd.get_dummies(pdf.b, dtype=np.int8)) self.assert_eq( ps.get_dummies(kdf, columns=["b"]), pd.get_dummies(pdf, columns=["b"], dtype=np.int8) ) self.assertRaises(KeyError, lambda: ps.get_dummies(kdf, columns=("a", "c"))) self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, columns="b")) # non-string names pdf = pd.DataFrame( {10: [1, 2, 3, 4, 4, 3, 2, 1], 20: list("abcdabcd"), 30: list("abcdabcd")} ) kdf = ps.from_pandas(pdf) self.assert_eq( ps.get_dummies(kdf, columns=[10, 30]), pd.get_dummies(pdf, columns=[10, 30], dtype=np.int8), ) self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, 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), ], } ) kdf = ps.from_pandas(pdf) if LooseVersion(pyspark.__version__) >= LooseVersion("2.4"): self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8)) self.assert_eq(ps.get_dummies(kdf.dt), pd.get_dummies(pdf.dt, dtype=np.int8)) else: with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}): self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8)) self.assert_eq(ps.get_dummies(kdf.dt), pd.get_dummies(pdf.dt, dtype=np.int8)) def test_get_dummies_boolean(self): pdf = pd.DataFrame({"b": [True, False, True]}) kdf = ps.from_pandas(pdf) if LooseVersion(pyspark.__version__) >= LooseVersion("2.4"): self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(kdf.b), pd.get_dummies(pdf.b, dtype=np.int8)) else: with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}): self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(kdf.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)]}) kdf = ps.from_pandas(pdf) if LooseVersion(pyspark.__version__) >= LooseVersion("2.4"): self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq(ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8), almost=True) else: with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}): self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8)) self.assert_eq( ps.get_dummies(kdf.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]) kser = ps.from_pandas(pser) self.assert_eq( ps.get_dummies(kser, prefix="X", prefix_sep="-"), pd.get_dummies(pser, prefix="X", prefix_sep="-", dtype=np.int8), ) self.assert_eq( ps.get_dummies(kser, 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]) kser = ps.from_pandas(pser) self.assert_eq(ps.get_dummies(kser), pd.get_dummies(pser, dtype=np.int8), almost=True) # dummy_na self.assert_eq( ps.get_dummies(kser, 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]}) kdf = ps.from_pandas(pdf) self.assert_eq( ps.get_dummies(kdf, prefix=["foo", "bar"]), pd.get_dummies(pdf, prefix=["foo", "bar"], dtype=np.int8), ) self.assert_eq( ps.get_dummies(kdf, prefix=["foo"], columns=["B"]), pd.get_dummies(pdf, prefix=["foo"], columns=["B"], dtype=np.int8), ) self.assert_eq( ps.get_dummies(kdf, prefix={"A": "foo", "B": "bar"}), pd.get_dummies(pdf, prefix={"A": "foo", "B": "bar"}, dtype=np.int8), ) self.assert_eq( ps.get_dummies(kdf, prefix={"B": "foo", "A": "bar"}), pd.get_dummies(pdf, prefix={"B": "foo", "A": "bar"}, dtype=np.int8), ) self.assert_eq( ps.get_dummies(kdf, 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(kdf, prefix="foo") with self.assertRaisesRegex(ValueError, "Length of 'prefix' \\(1\\) .* \\(2\\)"): ps.get_dummies(kdf, prefix=["foo"]) with self.assertRaisesRegex(ValueError, "Length of 'prefix' \\(2\\) .* \\(1\\)"): ps.get_dummies(kdf, prefix=["foo", "bar"], columns=["B"]) pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4], name="A") kser = ps.from_pandas(pser) self.assert_eq( ps.get_dummies(kser, prefix="foo"), pd.get_dummies(pser, prefix="foo", dtype=np.int8) ) # columns are ignored. self.assert_eq( ps.get_dummies(kser, 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], } ) kdf = 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(kdf, 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"), } ) kdf = ps.from_pandas(pdf) self.assert_eq( ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8).rename(columns=name_like_string) ) self.assert_eq( ps.get_dummies(kdf, 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(kdf, columns=["x"]), pd.get_dummies(pdf, columns=["x"], dtype=np.int8).rename(columns=name_like_string), ) self.assert_eq( ps.get_dummies(kdf, 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(kdf, columns=["z"])) self.assertRaises(KeyError, lambda: ps.get_dummies(kdf, columns=("x", "c"))) self.assertRaises(ValueError, lambda: ps.get_dummies(kdf, columns=[("x",), "c"])) self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, 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"), } ) kdf = ps.from_pandas(pdf) self.assert_eq( ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8).rename(columns=name_like_string) ) self.assert_eq( ps.get_dummies(kdf, 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(kdf, columns=["x"]), pd.get_dummies(pdf, columns=["x"], dtype=np.int8).rename(columns=name_like_string), ) self.assert_eq( ps.get_dummies(kdf, 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)