text: #
  # 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
  import os
  import threading
  import time
  import unittest
  import warnings
  from distutils.version import LooseVersion
  from typing import cast
  
  from pyspark import SparkContext, SparkConf
  from pyspark.sql import Row, SparkSession
  from pyspark.sql.functions import rand, udf, assert_true, lit
  from pyspark.sql.types import (
      StructType,
      StringType,
      IntegerType,
      LongType,
      FloatType,
      DoubleType,
      DecimalType,
      DateType,
      TimestampType,
      TimestampNTZType,
      BinaryType,
      StructField,
      ArrayType,
      NullType,
      DayTimeIntervalType,
  )
  from pyspark.testing.sqlutils import (
      ReusedSQLTestCase,
      have_pandas,
      have_pyarrow,
      pandas_requirement_message,
      pyarrow_requirement_message,
  )
  from pyspark.testing.utils import QuietTest
  
  if have_pandas:
      import pandas as pd
      from pandas.testing import assert_frame_equal
  
  if have_pyarrow:
      import pyarrow as pa  # noqa: F401
  
  
  @unittest.skipIf(
      not have_pandas or not have_pyarrow,
      cast(str, pandas_requirement_message or pyarrow_requirement_message),
  )
  class ArrowTests(ReusedSQLTestCase):
      @classmethod
      def setUpClass(cls):
          from datetime import date, datetime
          from decimal import Decimal
  
          super(ArrowTests, cls).setUpClass()
          cls.warnings_lock = threading.Lock()
  
          # Synchronize default timezone between Python and Java
          cls.tz_prev = os.environ.get("TZ", None)  # save current tz if set
          tz = "America/Los_Angeles"
          os.environ["TZ"] = tz
          time.tzset()
  
          cls.spark.conf.set("spark.sql.session.timeZone", tz)
  
          # Test fallback
          cls.spark.conf.set("spark.sql.execution.arrow.enabled", "false")
          assert cls.spark.conf.get("spark.sql.execution.arrow.pyspark.enabled") == "false"
          cls.spark.conf.set("spark.sql.execution.arrow.enabled", "true")
          assert cls.spark.conf.get("spark.sql.execution.arrow.pyspark.enabled") == "true"
  
          cls.spark.conf.set("spark.sql.execution.arrow.fallback.enabled", "true")
          assert cls.spark.conf.get("spark.sql.execution.arrow.pyspark.fallback.enabled") == "true"
          cls.spark.conf.set("spark.sql.execution.arrow.fallback.enabled", "false")
          assert cls.spark.conf.get("spark.sql.execution.arrow.pyspark.fallback.enabled") == "false"
  
          # Enable Arrow optimization in this tests.
          cls.spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
          # Disable fallback by default to easily detect the failures.
          cls.spark.conf.set("spark.sql.execution.arrow.pyspark.fallback.enabled", "false")
  
          cls.schema_wo_null = StructType(
              [
                  StructField("1_str_t", StringType(), True),
                  StructField("2_int_t", IntegerType(), True),
                  StructField("3_long_t", LongType(), True),
                  StructField("4_float_t", FloatType(), True),
                  StructField("5_double_t", DoubleType(), True),
                  StructField("6_decimal_t", DecimalType(38, 18), True),
                  StructField("7_date_t", DateType(), True),
                  StructField("8_timestamp_t", TimestampType(), True),
                  StructField("9_binary_t", BinaryType(), True),
              ]
          )
          cls.schema = cls.schema_wo_null.add("10_null_t", NullType(), True)
          cls.data_wo_null = [
              (
                  "a",
                  1,
                  10,
                  0.2,
                  2.0,
                  Decimal("2.0"),
                  date(1969, 1, 1),
                  datetime(1969, 1, 1, 1, 1, 1),
                  bytearray(b"a"),
              ),
              (
                  "b",
                  2,
                  20,
                  0.4,
                  4.0,
                  Decimal("4.0"),
                  date(2012, 2, 2),
                  datetime(2012, 2, 2, 2, 2, 2),
                  bytearray(b"bb"),
              ),
              (
                  "c",
                  3,
                  30,
                  0.8,
                  6.0,
                  Decimal("6.0"),
                  date(2100, 3, 3),
                  datetime(2100, 3, 3, 3, 3, 3),
                  bytearray(b"ccc"),
              ),
              (
                  "d",
                  4,
                  40,
                  1.0,
                  8.0,
                  Decimal("8.0"),
                  date(2262, 4, 12),
                  datetime(2262, 3, 3, 3, 3, 3),
                  bytearray(b"dddd"),
              ),
          ]
          cls.data = [tuple(list(d) + [None]) for d in cls.data_wo_null]
  
      @classmethod
      def tearDownClass(cls):
          del os.environ["TZ"]
          if cls.tz_prev is not None:
              os.environ["TZ"] = cls.tz_prev
          time.tzset()
          super(ArrowTests, cls).tearDownClass()
  
      def create_pandas_data_frame(self):
          import numpy as np
  
          data_dict = {}
          for j, name in enumerate(self.schema.names):
              data_dict[name] = [self.data[i][j] for i in range(len(self.data))]
          # need to convert these to numpy types first
          data_dict["2_int_t"] = np.int32(data_dict["2_int_t"])
          data_dict["4_float_t"] = np.float32(data_dict["4_float_t"])
          return pd.DataFrame(data=data_dict)
  
      def test_toPandas_fallback_enabled(self):
          ts = datetime.datetime(2015, 11, 1, 0, 30)
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.fallback.enabled": True}):
              schema = StructType([StructField("a", ArrayType(TimestampType()), True)])
              df = self.spark.createDataFrame([([ts],)], schema=schema)
              with QuietTest(self.sc):
                  with self.warnings_lock:
                      with warnings.catch_warnings(record=True) as warns:
                          # we want the warnings to appear even if this test is run from a subclass
                          warnings.simplefilter("always")
                          pdf = df.toPandas()
                          # Catch and check the last UserWarning.
                          user_warns = [
                              warn.message for warn in warns if isinstance(warn.message, UserWarning)
                          ]
                          self.assertTrue(len(user_warns) > 0)
                          self.assertTrue("Attempting non-optimization" in str(user_warns[-1]))
                          assert_frame_equal(pdf, pd.DataFrame({"a": [[ts]]}))
  
      def test_toPandas_fallback_disabled(self):
          schema = StructType([StructField("a", ArrayType(TimestampType()), True)])
          df = self.spark.createDataFrame([(None,)], schema=schema)
          with QuietTest(self.sc):
              with self.warnings_lock:
                  with self.assertRaisesRegex(Exception, "Unsupported type"):
                      df.toPandas()
  
      def test_toPandas_empty_df_arrow_enabled(self):
          # SPARK-30537 test that toPandas() on an empty dataframe has the correct dtypes
          # when arrow is enabled
          from datetime import date
          from decimal import Decimal
  
          schema = StructType(
              [
                  StructField("a", StringType(), True),
                  StructField("a", IntegerType(), True),
                  StructField("c", TimestampType(), True),
                  StructField("d", NullType(), True),
                  StructField("e", LongType(), True),
                  StructField("f", FloatType(), True),
                  StructField("g", DateType(), True),
                  StructField("h", BinaryType(), True),
                  StructField("i", DecimalType(38, 18), True),
                  StructField("k", TimestampNTZType(), True),
                  StructField("L", DayTimeIntervalType(0, 3), True),
              ]
          )
          df = self.spark.createDataFrame(self.spark.sparkContext.emptyRDD(), schema=schema)
          non_empty_df = self.spark.createDataFrame(
              [
                  (
                      "a",
                      1,
                      datetime.datetime(1969, 1, 1, 1, 1, 1),
                      None,
                      10,
                      0.2,
                      date(1969, 1, 1),
                      bytearray(b"a"),
                      Decimal("2.0"),
                      datetime.datetime(1969, 1, 1, 1, 1, 1),
                      datetime.timedelta(microseconds=123),
                  )
              ],
              schema=schema,
          )
  
          pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
          pdf_non_empty, pdf_arrow_non_empty = self._toPandas_arrow_toggle(non_empty_df)
          assert_frame_equal(pdf, pdf_arrow)
          self.assertTrue(pdf_arrow.dtypes.equals(pdf_arrow_non_empty.dtypes))
          self.assertTrue(pdf_arrow.dtypes.equals(pdf_non_empty.dtypes))
  
      def test_null_conversion(self):
          df_null = self.spark.createDataFrame(
              [tuple([None for _ in range(len(self.data_wo_null[0]))])] + self.data_wo_null
          )
          pdf = df_null.toPandas()
          null_counts = pdf.isnull().sum().tolist()
          self.assertTrue(all([c == 1 for c in null_counts]))
  
      def _toPandas_arrow_toggle(self, df):
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
              pdf = df.toPandas()
  
          pdf_arrow = df.toPandas()
  
          return pdf, pdf_arrow
  
      def test_toPandas_arrow_toggle(self):
          df = self.spark.createDataFrame(self.data, schema=self.schema)
          pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
          expected = self.create_pandas_data_frame()
          assert_frame_equal(expected, pdf)
          assert_frame_equal(expected, pdf_arrow)
  
      def test_create_data_frame_to_pandas_timestamp_ntz(self):
          # SPARK-36626: Test TimestampNTZ in createDataFrame and toPandas
          with self.sql_conf({"spark.sql.session.timeZone": "America/Los_Angeles"}):
              origin = pd.DataFrame({"a": [datetime.datetime(2012, 2, 2, 2, 2, 2)]})
              df = self.spark.createDataFrame(
                  origin, schema=StructType([StructField("a", TimestampNTZType(), True)])
              )
              df.selectExpr("assert_true('2012-02-02 02:02:02' == CAST(a AS STRING))").collect()
  
              pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
              assert_frame_equal(origin, pdf)
              assert_frame_equal(pdf, pdf_arrow)
  
      def test_create_data_frame_to_pandas_day_time_internal(self):
          # SPARK-37279: Test DayTimeInterval in createDataFrame and toPandas
          origin = pd.DataFrame({"a": [datetime.timedelta(microseconds=123)]})
          df = self.spark.createDataFrame(origin)
          df.select(
              assert_true(lit("INTERVAL '0 00:00:00.000123' DAY TO SECOND") == df.a.cast("string"))
          ).collect()
  
          pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
          assert_frame_equal(origin, pdf)
          assert_frame_equal(pdf, pdf_arrow)
  
      def test_toPandas_respect_session_timezone(self):
          df = self.spark.createDataFrame(self.data, schema=self.schema)
  
          timezone = "America/Los_Angeles"
          with self.sql_conf({"spark.sql.session.timeZone": timezone}):
              pdf_la, pdf_arrow_la = self._toPandas_arrow_toggle(df)
              assert_frame_equal(pdf_arrow_la, pdf_la)
  
          timezone = "America/New_York"
          with self.sql_conf({"spark.sql.session.timeZone": timezone}):
              pdf_ny, pdf_arrow_ny = self._toPandas_arrow_toggle(df)
              assert_frame_equal(pdf_arrow_ny, pdf_ny)
  
              self.assertFalse(pdf_ny.equals(pdf_la))
  
              from pyspark.sql.pandas.types import _check_series_convert_timestamps_local_tz
  
              pdf_la_corrected = pdf_la.copy()
              for field in self.schema:
                  if isinstance(field.dataType, TimestampType):
                      pdf_la_corrected[field.name] = _check_series_convert_timestamps_local_tz(
                          pdf_la_corrected[field.name], timezone
                      )
              assert_frame_equal(pdf_ny, pdf_la_corrected)
  
      def test_pandas_round_trip(self):
          pdf = self.create_pandas_data_frame()
          df = self.spark.createDataFrame(self.data, schema=self.schema)
          pdf_arrow = df.toPandas()
          assert_frame_equal(pdf_arrow, pdf)
  
      def test_pandas_self_destruct(self):
          import pyarrow as pa
  
          rows = 2 ** 10
          cols = 4
          expected_bytes = rows * cols * 8
          df = self.spark.range(0, rows).select(*[rand() for _ in range(cols)])
          # Test the self_destruct behavior by testing _collect_as_arrow directly
          allocation_before = pa.total_allocated_bytes()
          batches = df._collect_as_arrow(split_batches=True)
          table = pa.Table.from_batches(batches)
          del batches
          pdf_split = table.to_pandas(self_destruct=True, split_blocks=True, use_threads=False)
          allocation_after = pa.total_allocated_bytes()
          difference = allocation_after - allocation_before
          # Should be around 1x the data size (table should not hold on to any memory)
          self.assertGreaterEqual(difference, 0.9 * expected_bytes)
          self.assertLessEqual(difference, 1.1 * expected_bytes)
  
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.selfDestruct.enabled": False}):
              no_self_destruct_pdf = df.toPandas()
              # Note while memory usage is 2x data size here (both table and pdf hold on to
              # memory), in this case Arrow still only tracks 1x worth of memory (since the
              # batches are not allocated by Arrow in this case), so we can't make any
              # assertions here
  
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.selfDestruct.enabled": True}):
              self_destruct_pdf = df.toPandas()
  
          assert_frame_equal(pdf_split, no_self_destruct_pdf)
          assert_frame_equal(pdf_split, self_destruct_pdf)
  
      def test_filtered_frame(self):
          df = self.spark.range(3).toDF("i")
          pdf = df.filter("i < 0").toPandas()
          self.assertEqual(len(pdf.columns), 1)
          self.assertEqual(pdf.columns[0], "i")
          self.assertTrue(pdf.empty)
  
      def test_no_partition_frame(self):
          schema = StructType([StructField("field1", StringType(), True)])
          df = self.spark.createDataFrame(self.sc.emptyRDD(), schema)
          pdf = df.toPandas()
          self.assertEqual(len(pdf.columns), 1)
          self.assertEqual(pdf.columns[0], "field1")
          self.assertTrue(pdf.empty)
  
      def test_propagates_spark_exception(self):
          df = self.spark.range(3).toDF("i")
  
          def raise_exception():
              raise RuntimeError("My error")
  
          exception_udf = udf(raise_exception, IntegerType())
          df = df.withColumn("error", exception_udf())
          with QuietTest(self.sc):
              with self.assertRaisesRegex(Exception, "My error"):
                  df.toPandas()
  
      def _createDataFrame_toggle(self, pdf, schema=None):
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
              df_no_arrow = self.spark.createDataFrame(pdf, schema=schema)
  
          df_arrow = self.spark.createDataFrame(pdf, schema=schema)
  
          return df_no_arrow, df_arrow
  
      def test_createDataFrame_toggle(self):
          pdf = self.create_pandas_data_frame()
          df_no_arrow, df_arrow = self._createDataFrame_toggle(pdf, schema=self.schema)
          self.assertEqual(df_no_arrow.collect(), df_arrow.collect())
  
      def test_createDataFrame_respect_session_timezone(self):
          from datetime import timedelta
  
          pdf = self.create_pandas_data_frame()
          timezone = "America/Los_Angeles"
          with self.sql_conf({"spark.sql.session.timeZone": timezone}):
              df_no_arrow_la, df_arrow_la = self._createDataFrame_toggle(pdf, schema=self.schema)
              result_la = df_no_arrow_la.collect()
              result_arrow_la = df_arrow_la.collect()
              self.assertEqual(result_la, result_arrow_la)
  
          timezone = "America/New_York"
          with self.sql_conf({"spark.sql.session.timeZone": timezone}):
              df_no_arrow_ny, df_arrow_ny = self._createDataFrame_toggle(pdf, schema=self.schema)
              result_ny = df_no_arrow_ny.collect()
              result_arrow_ny = df_arrow_ny.collect()
              self.assertEqual(result_ny, result_arrow_ny)
  
              self.assertNotEqual(result_ny, result_la)
  
              # Correct result_la by adjusting 3 hours difference between Los Angeles and New York
              result_la_corrected = [
                  Row(
                      **{
                          k: v - timedelta(hours=3) if k == "8_timestamp_t" else v
                          for k, v in row.asDict().items()
                      }
                  )
                  for row in result_la
              ]
              self.assertEqual(result_ny, result_la_corrected)
  
      def test_createDataFrame_with_schema(self):
          pdf = self.create_pandas_data_frame()
          df = self.spark.createDataFrame(pdf, schema=self.schema)
          self.assertEqual(self.schema, df.schema)
          pdf_arrow = df.toPandas()
          assert_frame_equal(pdf_arrow, pdf)
  
      def test_createDataFrame_with_incorrect_schema(self):
          pdf = self.create_pandas_data_frame()
          fields = list(self.schema)
          fields[5], fields[6] = fields[6], fields[5]  # swap decimal with date
          wrong_schema = StructType(fields)
          with self.sql_conf({"spark.sql.execution.pandas.convertToArrowArraySafely": False}):
              with QuietTest(self.sc):
                  with self.assertRaisesRegex(Exception, "[D|d]ecimal.*got.*date"):
                      self.spark.createDataFrame(pdf, schema=wrong_schema)
  
      def test_createDataFrame_with_names(self):
          pdf = self.create_pandas_data_frame()
          new_names = list(map(str, range(len(self.schema.fieldNames()))))
          # Test that schema as a list of column names gets applied
          df = self.spark.createDataFrame(pdf, schema=list(new_names))
          self.assertEqual(df.schema.fieldNames(), new_names)
          # Test that schema as tuple of column names gets applied
          df = self.spark.createDataFrame(pdf, schema=tuple(new_names))
          self.assertEqual(df.schema.fieldNames(), new_names)
  
      def test_createDataFrame_column_name_encoding(self):
          pdf = pd.DataFrame({"a": [1]})
          columns = self.spark.createDataFrame(pdf).columns
          self.assertTrue(isinstance(columns[0], str))
          self.assertEqual(columns[0], "a")
          columns = self.spark.createDataFrame(pdf, ["b"]).columns
          self.assertTrue(isinstance(columns[0], str))
          self.assertEqual(columns[0], "b")
  
      def test_createDataFrame_with_single_data_type(self):
          with QuietTest(self.sc):
              with self.assertRaisesRegex(ValueError, ".*IntegerType.*not supported.*"):
                  self.spark.createDataFrame(pd.DataFrame({"a": [1]}), schema="int")
  
      def test_createDataFrame_does_not_modify_input(self):
          # Some series get converted for Spark to consume, this makes sure input is unchanged
          pdf = self.create_pandas_data_frame()
          # Use a nanosecond value to make sure it is not truncated
          pdf.iloc[0, 7] = pd.Timestamp(1)
          # Integers with nulls will get NaNs filled with 0 and will be casted
          pdf.iloc[1, 1] = None
          pdf_copy = pdf.copy(deep=True)
          self.spark.createDataFrame(pdf, schema=self.schema)
          self.assertTrue(pdf.equals(pdf_copy))
  
      def test_schema_conversion_roundtrip(self):
          from pyspark.sql.pandas.types import from_arrow_schema, to_arrow_schema
  
          arrow_schema = to_arrow_schema(self.schema)
          schema_rt = from_arrow_schema(arrow_schema)
          self.assertEqual(self.schema, schema_rt)
  
      def test_createDataFrame_with_array_type(self):
          pdf = pd.DataFrame({"a": [[1, 2], [3, 4]], "b": [["x", "y"], ["y", "z"]]})
          df, df_arrow = self._createDataFrame_toggle(pdf)
          result = df.collect()
          result_arrow = df_arrow.collect()
          expected = [tuple(list(e) for e in rec) for rec in pdf.to_records(index=False)]
          for r in range(len(expected)):
              for e in range(len(expected[r])):
                  self.assertTrue(
                      expected[r][e] == result_arrow[r][e] and result[r][e] == result_arrow[r][e]
                  )
  
      def test_toPandas_with_array_type(self):
          expected = [([1, 2], ["x", "y"]), ([3, 4], ["y", "z"])]
          array_schema = StructType(
              [StructField("a", ArrayType(IntegerType())), StructField("b", ArrayType(StringType()))]
          )
          df = self.spark.createDataFrame(expected, schema=array_schema)
          pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
          result = [tuple(list(e) for e in rec) for rec in pdf.to_records(index=False)]
          result_arrow = [tuple(list(e) for e in rec) for rec in pdf_arrow.to_records(index=False)]
          for r in range(len(expected)):
              for e in range(len(expected[r])):
                  self.assertTrue(
                      expected[r][e] == result_arrow[r][e] and result[r][e] == result_arrow[r][e]
                  )
  
      def test_createDataFrame_with_map_type(self):
          map_data = [{"a": 1}, {"b": 2, "c": 3}, {}, None, {"d": None}]
  
          pdf = pd.DataFrame({"id": [0, 1, 2, 3, 4], "m": map_data})
          schema = "id long, m map<string, long>"
  
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
              df = self.spark.createDataFrame(pdf, schema=schema)
  
          if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
              with QuietTest(self.sc):
                  with self.assertRaisesRegex(Exception, "MapType.*only.*pyarrow 2.0.0"):
                      self.spark.createDataFrame(pdf, schema=schema)
          else:
              df_arrow = self.spark.createDataFrame(pdf, schema=schema)
  
              result = df.collect()
              result_arrow = df_arrow.collect()
  
              self.assertEqual(len(result), len(result_arrow))
              for row, row_arrow in zip(result, result_arrow):
                  i, m = row
                  _, m_arrow = row_arrow
                  self.assertEqual(m, map_data[i])
                  self.assertEqual(m_arrow, map_data[i])
  
      def test_toPandas_with_map_type(self):
          pdf = pd.DataFrame(
              {"id": [0, 1, 2, 3], "m": [{}, {"a": 1}, {"a": 1, "b": 2}, {"a": 1, "b": 2, "c": 3}]}
          )
  
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
              df = self.spark.createDataFrame(pdf, schema="id long, m map<string, long>")
  
          if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
              with QuietTest(self.sc):
                  with self.assertRaisesRegex(Exception, "MapType.*only.*pyarrow 2.0.0"):
                      df.toPandas()
          else:
              pdf_non, pdf_arrow = self._toPandas_arrow_toggle(df)
              assert_frame_equal(pdf_arrow, pdf_non)
  
      def test_toPandas_with_map_type_nulls(self):
          pdf = pd.DataFrame(
              {"id": [0, 1, 2, 3, 4], "m": [{"a": 1}, {"b": 2, "c": 3}, {}, None, {"d": None}]}
          )
  
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
              df = self.spark.createDataFrame(pdf, schema="id long, m map<string, long>")
  
          if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
              with QuietTest(self.sc):
                  with self.assertRaisesRegex(Exception, "MapType.*only.*pyarrow 2.0.0"):
                      df.toPandas()
          else:
              pdf_non, pdf_arrow = self._toPandas_arrow_toggle(df)
              assert_frame_equal(pdf_arrow, pdf_non)
  
      def test_createDataFrame_with_int_col_names(self):
          import numpy as np
  
          pdf = pd.DataFrame(np.random.rand(4, 2))
          df, df_arrow = self._createDataFrame_toggle(pdf)
          pdf_col_names = [str(c) for c in pdf.columns]
          self.assertEqual(pdf_col_names, df.columns)
          self.assertEqual(pdf_col_names, df_arrow.columns)
  
      def test_createDataFrame_fallback_enabled(self):
          ts = datetime.datetime(2015, 11, 1, 0, 30)
          with QuietTest(self.sc):
              with self.sql_conf({"spark.sql.execution.arrow.pyspark.fallback.enabled": True}):
                  with warnings.catch_warnings(record=True) as warns:
                      # we want the warnings to appear even if this test is run from a subclass
                      warnings.simplefilter("always")
                      df = self.spark.createDataFrame(
                          pd.DataFrame({"a": [[ts]]}), "a: array<timestamp>"
                      )
                      # Catch and check the last UserWarning.
                      user_warns = [
                          warn.message for warn in warns if isinstance(warn.message, UserWarning)
                      ]
                      self.assertTrue(len(user_warns) > 0)
                      self.assertTrue("Attempting non-optimization" in str(user_warns[-1]))
                      self.assertEqual(df.collect(), [Row(a=[ts])])
  
      def test_createDataFrame_fallback_disabled(self):
          with QuietTest(self.sc):
              with self.assertRaisesRegex(TypeError, "Unsupported type"):
                  self.spark.createDataFrame(
                      pd.DataFrame({"a": [[datetime.datetime(2015, 11, 1, 0, 30)]]}),
                      "a: array<timestamp>",
                  )
  
      # Regression test for SPARK-23314
      def test_timestamp_dst(self):
          # Daylight saving time for Los Angeles for 2015 is Sun, Nov 1 at 2:00 am
          dt = [
              datetime.datetime(2015, 11, 1, 0, 30),
              datetime.datetime(2015, 11, 1, 1, 30),
              datetime.datetime(2015, 11, 1, 2, 30),
          ]
          pdf = pd.DataFrame({"time": dt})
  
          df_from_python = self.spark.createDataFrame(dt, "timestamp").toDF("time")
          df_from_pandas = self.spark.createDataFrame(pdf)
  
          assert_frame_equal(pdf, df_from_python.toPandas())
          assert_frame_equal(pdf, df_from_pandas.toPandas())
  
      # Regression test for SPARK-28003
      def test_timestamp_nat(self):
          dt = [pd.NaT, pd.Timestamp("2019-06-11"), None] * 100
          pdf = pd.DataFrame({"time": dt})
          df_no_arrow, df_arrow = self._createDataFrame_toggle(pdf)
  
          assert_frame_equal(pdf, df_no_arrow.toPandas())
          assert_frame_equal(pdf, df_arrow.toPandas())
  
      def test_toPandas_batch_order(self):
          def delay_first_part(partition_index, iterator):
              if partition_index == 0:
                  time.sleep(0.1)
              return iterator
  
          # Collects Arrow RecordBatches out of order in driver JVM then re-orders in Python
          def run_test(num_records, num_parts, max_records, use_delay=False):
              df = self.spark.range(num_records, numPartitions=num_parts).toDF("a")
              if use_delay:
                  df = df.rdd.mapPartitionsWithIndex(delay_first_part).toDF()
              with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": max_records}):
                  pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
                  assert_frame_equal(pdf, pdf_arrow)
  
          cases = [
              (1024, 512, 2),  # Use large num partitions for more likely collecting out of order
              (64, 8, 2, True),  # Use delay in first partition to force collecting out of order
              (64, 64, 1),  # Test single batch per partition
              (64, 1, 64),  # Test single partition, single batch
              (64, 1, 8),  # Test single partition, multiple batches
              (30, 7, 2),  # Test different sized partitions
          ]
  
          for case in cases:
              run_test(*case)
  
      def test_createDateFrame_with_category_type(self):
          pdf = pd.DataFrame({"A": ["a", "b", "c", "a"]})
          pdf["B"] = pdf["A"].astype("category")
          category_first_element = dict(enumerate(pdf["B"].cat.categories))[0]
  
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": True}):
              arrow_df = self.spark.createDataFrame(pdf)
              arrow_type = arrow_df.dtypes[1][1]
              result_arrow = arrow_df.toPandas()
              arrow_first_category_element = result_arrow["B"][0]
  
          with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
              df = self.spark.createDataFrame(pdf)
              spark_type = df.dtypes[1][1]
              result_spark = df.toPandas()
              spark_first_category_element = result_spark["B"][0]
  
          assert_frame_equal(result_spark, result_arrow)
  
          # ensure original category elements are string
          self.assertIsInstance(category_first_element, str)
          # spark data frame and arrow execution mode enabled data frame type must match pandas
          self.assertEqual(spark_type, "string")
          self.assertEqual(arrow_type, "string")
          self.assertIsInstance(arrow_first_category_element, str)
          self.assertIsInstance(spark_first_category_element, str)
  
      def test_createDataFrame_with_float_index(self):
          # SPARK-32098: float index should not produce duplicated or truncated Spark DataFrame
          self.assertEqual(
              self.spark.createDataFrame(pd.DataFrame({"a": [1, 2, 3]}, index=[2.0, 3.0, 4.0]))
              .distinct()
              .count(),
              3,
          )
  
      def test_no_partition_toPandas(self):
          # SPARK-32301: toPandas should work from a Spark DataFrame with no partitions
          # Forward-ported from SPARK-32300.
          pdf = self.spark.sparkContext.emptyRDD().toDF("col1 int").toPandas()
          self.assertEqual(len(pdf), 0)
          self.assertEqual(list(pdf.columns), ["col1"])
  
      def test_createDataFrame_empty_partition(self):
          pdf = pd.DataFrame({"c1": [1], "c2": ["string"]})
          df = self.spark.createDataFrame(pdf)
          self.assertEqual([Row(c1=1, c2="string")], df.collect())
          self.assertGreater(self.spark.sparkContext.defaultParallelism, len(pdf))
  
  
  @unittest.skipIf(
      not have_pandas or not have_pyarrow,
      cast(str, pandas_requirement_message or pyarrow_requirement_message),
  )
  class MaxResultArrowTests(unittest.TestCase):
      # These tests are separate as 'spark.driver.maxResultSize' configuration
      # is a static configuration to Spark context.
  
      @classmethod
      def setUpClass(cls):
          cls.spark = SparkSession(
              SparkContext(
                  "local[4]", cls.__name__, conf=SparkConf().set("spark.driver.maxResultSize", "10k")
              )
          )
  
          # Explicitly enable Arrow and disable fallback.
          cls.spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
          cls.spark.conf.set("spark.sql.execution.arrow.pyspark.fallback.enabled", "false")
  
      @classmethod
      def tearDownClass(cls):
          if hasattr(cls, "spark"):
              cls.spark.stop()
  
      def test_exception_by_max_results(self):
          with self.assertRaisesRegex(Exception, "is bigger than"):
              self.spark.range(0, 10000, 1, 100).toPandas()
  
  
  class EncryptionArrowTests(ArrowTests):
      @classmethod
      def conf(cls):
          return super(EncryptionArrowTests, cls).conf().set("spark.io.encryption.enabled", "true")
  
  
  if __name__ == "__main__":
      from pyspark.sql.tests.test_arrow import *  # noqa: F401
  
      try:
          import xmlrunner  # type: ignore
  
          testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
      except ImportError:
          testRunner = None
      unittest.main(testRunner=testRunner, verbosity=2)
  