/
githubmirror
/
spark
Обзор
Документация
Войти
/
githubmirror
/
spark
Код
Запросы
0
Пакеты
0
Релизы
0
Аналитика
Безопасность
master
python/pyspark/pandas/tests/computation/test_binary_ops.py
328 строк
12 KB
Ruifeng Zheng
[SPARK-56863][PYTHON][PS][TESTS] Drop redundant `SQLTestUtils`
19 май 2026, 03:55
19 май 2026, 03:55
369d502
Код
Авторство
О чём код?
# # 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 numpy as np import pandas as pd from pyspark import pandas as ps from pyspark.testing.pandasutils import PandasOnSparkTestCase # This file contains test cases for 'Binary operator functions' # https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/frame.html#binary-operator-functions class FrameBinaryOpsMixin: @property def pdf(self): return pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]}, index=np.random.rand(9), ) @property def psdf(self): return ps.from_pandas(self.pdf) def test_binary_operators(self): pdf = pd.DataFrame( {"A": [0, 2, 4], "B": [4, 2, 0], "X": [-1, 10, 0]}, index=np.random.rand(3) ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf + psdf.copy(), pdf + pdf.copy()) self.assert_eq(psdf + psdf.loc[:, ["A", "B"]], pdf + pdf.loc[:, ["A", "B"]]) self.assert_eq(psdf.loc[:, ["A", "B"]] + psdf, pdf.loc[:, ["A", "B"]] + pdf) with ps.option_context("compute.ops_on_diff_frames", False): self.assertRaisesRegex( ValueError, "it comes from a different dataframe", lambda: ps.range(10).add(ps.range(10)), ) self.assertRaisesRegex( TypeError, "add with a sequence is currently not supported", lambda: ps.range(10).add(ps.range(10).id), ) psdf_other = psdf.copy() psdf_other.columns = pd.MultiIndex.from_tuples([("A", "Z"), ("B", "X"), ("C", "C")]) self.assertRaisesRegex( ValueError, "cannot join with no overlapping index names", lambda: psdf.add(psdf_other), ) def test_mixed_dataframe_ops_dispatch_to_pandas_on_spark(self): pdf = pd.DataFrame({"a": [1, 2], "b": [3, 4]}) psdf = ps.from_pandas(pdf) with self.assertRaisesRegex( TypeError, "add with a sequence is currently not supported; however, got DataFrame." ): psdf + pdf with self.assertRaisesRegex( TypeError, "radd with a sequence is currently not supported; however, got DataFrame." ): pdf + psdf def test_binary_operator_add(self): # Positive pdf = pd.DataFrame({"a": ["x"], "b": ["y"], "c": [1], "d": [2]}) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] + psdf["b"], pdf["a"] + pdf["b"]) self.assert_eq(psdf["c"] + psdf["d"], pdf["c"] + pdf["d"]) # Negative ks_err_msg = "Addition can not be applied to given types" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] + psdf["c"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["c"] + psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["c"] + "literal") self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" + psdf["c"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 + psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] + 1) def test_binary_operator_sub(self): # Positive pdf = pd.DataFrame({"a": [2], "b": [1]}) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] - psdf["b"], pdf["a"] - pdf["b"]) # Negative psdf = ps.DataFrame({"a": ["x"], "b": [1]}) ks_err_msg = "Subtraction can not be applied to given types" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] - psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] - "literal") self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" - psdf["b"]) ks_err_msg = "Subtraction can not be applied to strings" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] - psdf["b"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 - psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] - 1) psdf = ps.DataFrame({"a": ["x"], "b": ["y"]}) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] - psdf["b"]) def test_divide_by_zero_behavior(self): # float / float # np.float32 pdf = pd.DataFrame( { "a": [1.0, -1.0, 0.0, np.nan], "b": [0.0, 0.0, 0.0, 0.0], }, dtype=np.float32, ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"]) # np.float64 pdf = pd.DataFrame( { "a": [1.0, -1.0, 0.0, np.nan], "b": [0.0, 0.0, 0.0, 0.0], }, dtype=np.float64, ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"]) # int / int for dtype in [np.int32, np.int64]: pdf = pd.DataFrame( { "a": [1, -1, 0], "b": [0, 0, 0], }, dtype=dtype, ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"]) # float / int pdf = pd.DataFrame( { "a": pd.Series([1.0, -1.0, 0.0, np.nan]), "b": pd.Series([0, 0, 0, 0]), } ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"]) # int / float pdf = pd.DataFrame( { "a": pd.Series([1, -1, 0]), "b": pd.Series([0.0, 0.0, 0.0]), } ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"]) # bool pdf = pd.DataFrame( { "a": pd.Series([True, False]), "b": pd.Series([0, 0]), } ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"]) pdf = pd.DataFrame( { "a": pd.Series([True, False]), "b": pd.Series([0.0, 0.0]), } ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"]) def test_binary_operator_truediv(self): # Positive pdf = pd.DataFrame({"a": [3], "b": [2]}) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"]) pser = pd.Series([1.1, 2.2, 3.3], dtype=np.float32) psser = ps.from_pandas(pser) self.assert_eq(psser / 1, pser / 1) self.assert_eq(psser / 0, pser / 0) # Negative psdf = ps.DataFrame({"a": ["x"], "b": [1]}) ks_err_msg = "True division can not be applied to given types" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] / psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] / "literal") self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" / psdf["b"]) ks_err_msg = "True division can not be applied to strings" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] / psdf["b"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 / psdf["a"]) def test_binary_operator_floordiv(self): pdf = pd.DataFrame({"a": ["x"], "b": [1], "c": [1.0], "d": [0]}) psdf = ps.from_pandas(pdf) self.assert_eq(pdf["b"] // 0, psdf["b"] // 0) self.assert_eq(pdf["c"] // 0, psdf["c"] // 0) self.assert_eq(pdf["d"] // 0, psdf["d"] // 0) pser = pd.Series([1.1, 2.2, 3.3], dtype=np.float32) psser = ps.from_pandas(pser) self.assert_eq(psser // 1, pser // 1) ks_err_msg = "Floor division can not be applied to strings" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] // psdf["b"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 // psdf["a"]) ks_err_msg = "Floor division can not be applied to given types" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] // psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] // "literal") self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" // psdf["b"]) def test_binary_operator_mod(self): # Positive pdf = pd.DataFrame({"a": [3], "b": [2], "c": [0]}) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["a"] % psdf["b"], pdf["a"] % pdf["b"]) self.assert_eq(psdf["a"] % 0, pdf["a"] % 0) self.assert_eq(1 % psdf["c"], 1 % pdf["c"]) # Negative psdf = ps.DataFrame({"a": ["x"], "b": [1]}) ks_err_msg = "Modulo can not be applied to given types" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] % psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] % "literal") ks_err_msg = "Modulo can not be applied to strings" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] % psdf["b"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 % psdf["a"]) def test_binary_operator_multiply(self): # Positive pdf = pd.DataFrame({"a": ["x", "y"], "b": [1, 2], "c": [3, 4]}) psdf = ps.from_pandas(pdf) self.assert_eq(psdf["b"] * psdf["c"], pdf["b"] * pdf["c"]) self.assert_eq(psdf["c"] * psdf["b"], pdf["c"] * pdf["b"]) self.assert_eq(psdf["a"] * psdf["b"], pdf["a"] * pdf["b"]) self.assert_eq(psdf["b"] * psdf["a"], pdf["b"] * pdf["a"]) self.assert_eq(psdf["a"] * 2, pdf["a"] * 2) self.assert_eq(psdf["b"] * 2, pdf["b"] * 2) self.assert_eq(2 * psdf["a"], 2 * pdf["a"]) self.assert_eq(2 * psdf["b"], 2 * pdf["b"]) # Negative psdf = ps.DataFrame({"a": ["x"], "b": [2]}) ks_err_msg = "Multiplication can not be applied to given types" self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] * "literal") self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" * psdf["b"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] * "literal") self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] * psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] * 0.1) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 0.1 * psdf["a"]) self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" * psdf["a"]) def test_combine_first(self): pdf = pd.DataFrame( {("X", "A"): [None, 0], ("X", "B"): [4, None], ("Y", "C"): [3, 3], ("Y", "B"): [1, 1]} ) pdf1, pdf2 = pdf["X"], pdf["Y"] psdf = ps.from_pandas(pdf) psdf1, psdf2 = psdf["X"], psdf["Y"] self.assert_eq(pdf1.combine_first(pdf2), psdf1.combine_first(psdf2)) def test_dot(self): psdf = self.psdf with self.assertRaisesRegex(TypeError, "Unsupported type DataFrame"): psdf.dot(psdf) def test_rfloordiv(self): pdf = pd.DataFrame( {"angles": [0, 3, 4], "degrees": [360, 180, 360]}, index=["circle", "triangle", "rectangle"], columns=["angles", "degrees"], ) psdf = ps.from_pandas(pdf) expected_result = pdf.rfloordiv(10) self.assert_eq(psdf.rfloordiv(10), expected_result) class FrameBinaryOpsTests( FrameBinaryOpsMixin, PandasOnSparkTestCase, ): pass if __name__ == "__main__": from pyspark.testing import main main()