| # -*- encoding: utf-8 -*- |
| # |
| # 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. |
| # |
| |
| from pyspark.sql import Column, Row |
| from pyspark.sql.types import StructType, StructField, LongType |
| from pyspark.sql.utils import AnalysisException |
| from pyspark.testing.sqlutils import ReusedSQLTestCase |
| |
| |
| class ColumnTests(ReusedSQLTestCase): |
| |
| def test_column_name_encoding(self): |
| """Ensure that created columns has `str` type consistently.""" |
| columns = self.spark.createDataFrame([('Alice', 1)], ['name', u'age']).columns |
| self.assertEqual(columns, ['name', 'age']) |
| self.assertTrue(isinstance(columns[0], str)) |
| self.assertTrue(isinstance(columns[1], str)) |
| |
| def test_and_in_expression(self): |
| self.assertEqual(4, self.df.filter((self.df.key <= 10) & (self.df.value <= "2")).count()) |
| self.assertRaises(ValueError, lambda: (self.df.key <= 10) and (self.df.value <= "2")) |
| self.assertEqual(14, self.df.filter((self.df.key <= 3) | (self.df.value < "2")).count()) |
| self.assertRaises(ValueError, lambda: self.df.key <= 3 or self.df.value < "2") |
| self.assertEqual(99, self.df.filter(~(self.df.key == 1)).count()) |
| self.assertRaises(ValueError, lambda: not self.df.key == 1) |
| |
| def test_validate_column_types(self): |
| from pyspark.sql.functions import udf, to_json |
| from pyspark.sql.column import _to_java_column |
| |
| self.assertTrue("Column" in _to_java_column("a").getClass().toString()) |
| self.assertTrue("Column" in _to_java_column(u"a").getClass().toString()) |
| self.assertTrue("Column" in _to_java_column(self.spark.range(1).id).getClass().toString()) |
| |
| self.assertRaisesRegex( |
| TypeError, |
| "Invalid argument, not a string or column", |
| lambda: _to_java_column(1)) |
| |
| class A(): |
| pass |
| |
| self.assertRaises(TypeError, lambda: _to_java_column(A())) |
| self.assertRaises(TypeError, lambda: _to_java_column([])) |
| |
| self.assertRaisesRegex( |
| TypeError, |
| "Invalid argument, not a string or column", |
| lambda: udf(lambda x: x)(None)) |
| self.assertRaises(TypeError, lambda: to_json(1)) |
| |
| def test_column_operators(self): |
| ci = self.df.key |
| cs = self.df.value |
| c = ci == cs |
| self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column)) |
| rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci), (1 ** ci), (ci ** 1) |
| self.assertTrue(all(isinstance(c, Column) for c in rcc)) |
| cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7] |
| self.assertTrue(all(isinstance(c, Column) for c in cb)) |
| cbool = (ci & ci), (ci | ci), (~ci) |
| self.assertTrue(all(isinstance(c, Column) for c in cbool)) |
| css = cs.contains('a'), cs.like('a'), cs.rlike('a'), cs.asc(), cs.desc(),\ |
| cs.startswith('a'), cs.endswith('a'), ci.eqNullSafe(cs) |
| self.assertTrue(all(isinstance(c, Column) for c in css)) |
| self.assertTrue(isinstance(ci.cast(LongType()), Column)) |
| self.assertRaisesRegex(ValueError, |
| "Cannot apply 'in' operator against a column", |
| lambda: 1 in cs) |
| |
| def test_column_accessor(self): |
| from pyspark.sql.functions import col |
| |
| self.assertIsInstance(col("foo")[1:3], Column) |
| self.assertIsInstance(col("foo")[0], Column) |
| self.assertIsInstance(col("foo")["bar"], Column) |
| self.assertRaises(ValueError, lambda: col("foo")[0:10:2]) |
| |
| def test_column_select(self): |
| df = self.df |
| self.assertEqual(self.testData, df.select("*").collect()) |
| self.assertEqual(self.testData, df.select(df.key, df.value).collect()) |
| self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect()) |
| |
| def test_access_column(self): |
| df = self.df |
| self.assertTrue(isinstance(df.key, Column)) |
| self.assertTrue(isinstance(df['key'], Column)) |
| self.assertTrue(isinstance(df[0], Column)) |
| self.assertRaises(IndexError, lambda: df[2]) |
| self.assertRaises(AnalysisException, lambda: df["bad_key"]) |
| self.assertRaises(TypeError, lambda: df[{}]) |
| |
| def test_column_name_with_non_ascii(self): |
| columnName = "数量" |
| self.assertTrue(isinstance(columnName, str)) |
| schema = StructType([StructField(columnName, LongType(), True)]) |
| df = self.spark.createDataFrame([(1,)], schema) |
| self.assertEqual(schema, df.schema) |
| self.assertEqual("DataFrame[数量: bigint]", str(df)) |
| self.assertEqual([("数量", 'bigint')], df.dtypes) |
| self.assertEqual(1, df.select("数量").first()[0]) |
| self.assertEqual(1, df.select(df["数量"]).first()[0]) |
| self.assertTrue(columnName in repr(df[columnName])) |
| |
| def test_field_accessor(self): |
| df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF() |
| self.assertEqual(1, df.select(df.l[0]).first()[0]) |
| self.assertEqual(1, df.select(df.r["a"]).first()[0]) |
| self.assertEqual(1, df.select(df["r.a"]).first()[0]) |
| self.assertEqual("b", df.select(df.r["b"]).first()[0]) |
| self.assertEqual("b", df.select(df["r.b"]).first()[0]) |
| self.assertEqual("v", df.select(df.d["k"]).first()[0]) |
| |
| def test_bitwise_operations(self): |
| from pyspark.sql import functions |
| row = Row(a=170, b=75) |
| df = self.spark.createDataFrame([row]) |
| result = df.select(df.a.bitwiseAND(df.b)).collect()[0].asDict() |
| self.assertEqual(170 & 75, result['(a & b)']) |
| result = df.select(df.a.bitwiseOR(df.b)).collect()[0].asDict() |
| self.assertEqual(170 | 75, result['(a | b)']) |
| result = df.select(df.a.bitwiseXOR(df.b)).collect()[0].asDict() |
| self.assertEqual(170 ^ 75, result['(a ^ b)']) |
| result = df.select(functions.bitwiseNOT(df.b)).collect()[0].asDict() |
| self.assertEqual(~75, result['~b']) |
| |
| def test_with_field(self): |
| from pyspark.sql.functions import lit, col |
| df = self.spark.createDataFrame([Row(a=Row(b=1, c=2))]) |
| self.assertIsInstance(df['a'].withField('b', lit(3)), Column) |
| self.assertIsInstance(df['a'].withField('d', lit(3)), Column) |
| result = df.withColumn('a', df['a'].withField('d', lit(3))).collect()[0].asDict() |
| self.assertEqual(3, result['a']['d']) |
| result = df.withColumn('a', df['a'].withField('b', lit(3))).collect()[0].asDict() |
| self.assertEqual(3, result['a']['b']) |
| |
| self.assertRaisesRegex(TypeError, |
| 'col should be a Column', |
| lambda: df['a'].withField('b', 3)) |
| self.assertRaisesRegex(TypeError, |
| 'fieldName should be a string', |
| lambda: df['a'].withField(col('b'), lit(3))) |
| |
| def test_drop_fields(self): |
| df = self.spark.createDataFrame([Row(a=Row(b=1, c=2, d=Row(e=3, f=4)))]) |
| self.assertIsInstance(df["a"].dropFields("b"), Column) |
| self.assertIsInstance(df["a"].dropFields("b", "c"), Column) |
| self.assertIsInstance(df["a"].dropFields("d.e"), Column) |
| |
| result = df.select( |
| df["a"].dropFields("b").alias("a1"), |
| df["a"].dropFields("d.e").alias("a2"), |
| ).first().asDict(True) |
| |
| self.assertTrue( |
| "b" not in result["a1"] and |
| "c" in result["a1"] and |
| "d" in result["a1"] |
| ) |
| |
| self.assertTrue( |
| "e" not in result["a2"]["d"] and |
| "f" in result["a2"]["d"] |
| ) |
| |
| if __name__ == "__main__": |
| import unittest |
| from pyspark.sql.tests.test_column 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) |
