text: # -*- 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 *
  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.assertRaisesRegexp(
              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.assertRaisesRegexp(
              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.assertRaisesRegexp(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])
  
      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'])
  
  
  if __name__ == "__main__":
      import unittest
      from pyspark.sql.tests.test_column 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)
  