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 gc
  import decimal
  import json
  import multiprocessing as mp
  import sys
  
  from collections import OrderedDict
  from datetime import date, datetime, time, timedelta, timezone
  from distutils.version import LooseVersion
  
  import hypothesis as h
  import hypothesis.extra.pytz as tzst
  import hypothesis.strategies as st
  import numpy as np
  import numpy.testing as npt
  import pytest
  import pytz
  
  from pyarrow.pandas_compat import get_logical_type, _pandas_api
  from pyarrow.tests.util import invoke_script, random_ascii, rands
  import pyarrow.tests.strategies as past
  
  import pyarrow as pa
  try:
      from pyarrow import parquet as pq
  except ImportError:
      pass
  
  try:
      import pandas as pd
      import pandas.testing as tm
      from .pandas_examples import dataframe_with_arrays, dataframe_with_lists
  except ImportError:
      pass
  
  
  # Marks all of the tests in this module
  pytestmark = pytest.mark.pandas
  
  
  def _alltypes_example(size=100):
      return pd.DataFrame({
          'uint8': np.arange(size, dtype=np.uint8),
          'uint16': np.arange(size, dtype=np.uint16),
          'uint32': np.arange(size, dtype=np.uint32),
          'uint64': np.arange(size, dtype=np.uint64),
          'int8': np.arange(size, dtype=np.int16),
          'int16': np.arange(size, dtype=np.int16),
          'int32': np.arange(size, dtype=np.int32),
          'int64': np.arange(size, dtype=np.int64),
          'float32': np.arange(size, dtype=np.float32),
          'float64': np.arange(size, dtype=np.float64),
          'bool': np.random.randn(size) > 0,
          # TODO(wesm): Pandas only support ns resolution, Arrow supports s, ms,
          # us, ns
          'datetime': np.arange("2016-01-01T00:00:00.001", size,
                                dtype='datetime64[ms]'),
          'str': [str(x) for x in range(size)],
          'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None],
          'empty_str': [''] * size
      })
  
  
  def _check_pandas_roundtrip(df, expected=None, use_threads=False,
                              expected_schema=None,
                              check_dtype=True, schema=None,
                              preserve_index=False,
                              as_batch=False):
      klass = pa.RecordBatch if as_batch else pa.Table
      table = klass.from_pandas(df, schema=schema,
                                preserve_index=preserve_index,
                                nthreads=2 if use_threads else 1)
      result = table.to_pandas(use_threads=use_threads)
  
      if expected_schema:
          # all occurrences of _check_pandas_roundtrip passes expected_schema
          # without the pandas generated key-value metadata
          assert table.schema.equals(expected_schema)
  
      if expected is None:
          expected = df
  
      tm.assert_frame_equal(result, expected, check_dtype=check_dtype,
                            check_index_type=('equiv' if preserve_index
                                              else False))
  
  
  def _check_series_roundtrip(s, type_=None, expected_pa_type=None):
      arr = pa.array(s, from_pandas=True, type=type_)
  
      if type_ is not None and expected_pa_type is None:
          expected_pa_type = type_
  
      if expected_pa_type is not None:
          assert arr.type == expected_pa_type
  
      result = pd.Series(arr.to_pandas(), name=s.name)
      tm.assert_series_equal(s, result)
  
  
  def _check_array_roundtrip(values, expected=None, mask=None,
                             type=None):
      arr = pa.array(values, from_pandas=True, mask=mask, type=type)
      result = arr.to_pandas()
  
      values_nulls = pd.isnull(values)
      if mask is None:
          assert arr.null_count == values_nulls.sum()
      else:
          assert arr.null_count == (mask | values_nulls).sum()
  
      if expected is None:
          if mask is None:
              expected = pd.Series(values)
          else:
              expected = pd.Series(np.ma.masked_array(values, mask=mask))
  
      tm.assert_series_equal(pd.Series(result), expected, check_names=False)
  
  
  def _check_array_from_pandas_roundtrip(np_array, type=None):
      arr = pa.array(np_array, from_pandas=True, type=type)
      result = arr.to_pandas()
      npt.assert_array_equal(result, np_array)
  
  
  class TestConvertMetadata:
      """
      Conversion tests for Pandas metadata & indices.
      """
  
      def test_non_string_columns(self):
          df = pd.DataFrame({0: [1, 2, 3]})
          table = pa.Table.from_pandas(df)
          assert table.field(0).name == '0'
  
      def test_from_pandas_with_columns(self):
          df = pd.DataFrame({0: [1, 2, 3], 1: [1, 3, 3], 2: [2, 4, 5]},
                            columns=[1, 0])
  
          table = pa.Table.from_pandas(df, columns=[0, 1])
          expected = pa.Table.from_pandas(df[[0, 1]])
          assert expected.equals(table)
  
          record_batch_table = pa.RecordBatch.from_pandas(df, columns=[0, 1])
          record_batch_expected = pa.RecordBatch.from_pandas(df[[0, 1]])
          assert record_batch_expected.equals(record_batch_table)
  
      def test_column_index_names_are_preserved(self):
          df = pd.DataFrame({'data': [1, 2, 3]})
          df.columns.names = ['a']
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_range_index_shortcut(self):
          # ARROW-1639
          index_name = 'foo'
          df = pd.DataFrame({'a': [1, 2, 3, 4]},
                            index=pd.RangeIndex(0, 8, step=2, name=index_name))
  
          df2 = pd.DataFrame({'a': [4, 5, 6, 7]},
                             index=pd.RangeIndex(0, 4))
  
          table = pa.Table.from_pandas(df)
          table_no_index_name = pa.Table.from_pandas(df2)
  
          # The RangeIndex is tracked in the metadata only
          assert len(table.schema) == 1
  
          result = table.to_pandas()
          tm.assert_frame_equal(result, df)
          assert isinstance(result.index, pd.RangeIndex)
          assert _pandas_api.get_rangeindex_attribute(result.index, 'step') == 2
          assert result.index.name == index_name
  
          result2 = table_no_index_name.to_pandas()
          tm.assert_frame_equal(result2, df2)
          assert isinstance(result2.index, pd.RangeIndex)
          assert _pandas_api.get_rangeindex_attribute(result2.index, 'step') == 1
          assert result2.index.name is None
  
      def test_range_index_force_serialization(self):
          # ARROW-5427: preserve_index=True will force the RangeIndex to
          # be serialized as a column rather than tracked more
          # efficiently as metadata
          df = pd.DataFrame({'a': [1, 2, 3, 4]},
                            index=pd.RangeIndex(0, 8, step=2, name='foo'))
  
          table = pa.Table.from_pandas(df, preserve_index=True)
          assert table.num_columns == 2
          assert 'foo' in table.column_names
  
          restored = table.to_pandas()
          tm.assert_frame_equal(restored, df)
  
      def test_rangeindex_doesnt_warn(self):
          # ARROW-5606: pandas 0.25 deprecated private _start/stop/step
          # attributes -> can be removed if support < pd 0.25 is dropped
          df = pd.DataFrame(np.random.randn(4, 2), columns=['a', 'b'])
  
          with pytest.warns(None) as record:
              _check_pandas_roundtrip(df, preserve_index=True)
  
          assert len(record) == 0
  
      def test_multiindex_columns(self):
          columns = pd.MultiIndex.from_arrays([
              ['one', 'two'], ['X', 'Y']
          ])
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns)
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_multiindex_columns_with_dtypes(self):
          columns = pd.MultiIndex.from_arrays(
              [
                  ['one', 'two'],
                  pd.DatetimeIndex(['2017-08-01', '2017-08-02']),
              ],
              names=['level_1', 'level_2'],
          )
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns)
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_multiindex_with_column_dtype_object(self):
          # ARROW-3651 & ARROW-9096
          # Bug when dtype of the columns is object.
  
          # uinderlying dtype: integer
          df = pd.DataFrame([1], columns=pd.Index([1], dtype=object))
          _check_pandas_roundtrip(df, preserve_index=True)
  
          # underlying dtype: floating
          df = pd.DataFrame([1], columns=pd.Index([1.1], dtype=object))
          _check_pandas_roundtrip(df, preserve_index=True)
  
          # underlying dtype: datetime
          # ARROW-9096: a simple roundtrip now works
          df = pd.DataFrame([1], columns=pd.Index(
              [datetime(2018, 1, 1)], dtype="object"))
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_multiindex_columns_unicode(self):
          columns = pd.MultiIndex.from_arrays([['あ', 'い'], ['X', 'Y']])
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns)
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_multiindex_doesnt_warn(self):
          # ARROW-3953: pandas 0.24 rename of MultiIndex labels to codes
          columns = pd.MultiIndex.from_arrays([['one', 'two'], ['X', 'Y']])
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns)
  
          with pytest.warns(None) as record:
              _check_pandas_roundtrip(df, preserve_index=True)
  
          assert len(record) == 0
  
      def test_integer_index_column(self):
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')])
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_index_metadata_field_name(self):
          # test None case, and strangely named non-index columns
          df = pd.DataFrame(
              [(1, 'a', 3.1), (2, 'b', 2.2), (3, 'c', 1.3)],
              index=pd.MultiIndex.from_arrays(
                  [['c', 'b', 'a'], [3, 2, 1]],
                  names=[None, 'foo']
              ),
              columns=['a', None, '__index_level_0__'],
          )
          with pytest.warns(UserWarning):
              t = pa.Table.from_pandas(df, preserve_index=True)
          js = t.schema.pandas_metadata
  
          col1, col2, col3, idx0, foo = js['columns']
  
          assert col1['name'] == 'a'
          assert col1['name'] == col1['field_name']
  
          assert col2['name'] is None
          assert col2['field_name'] == 'None'
  
          assert col3['name'] == '__index_level_0__'
          assert col3['name'] == col3['field_name']
  
          idx0_descr, foo_descr = js['index_columns']
          assert idx0_descr == '__index_level_0__'
          assert idx0['field_name'] == idx0_descr
          assert idx0['name'] is None
  
          assert foo_descr == 'foo'
          assert foo['field_name'] == foo_descr
          assert foo['name'] == foo_descr
  
      def test_categorical_column_index(self):
          df = pd.DataFrame(
              [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)],
              columns=pd.Index(list('def'), dtype='category')
          )
          t = pa.Table.from_pandas(df, preserve_index=True)
          js = t.schema.pandas_metadata
  
          column_indexes, = js['column_indexes']
          assert column_indexes['name'] is None
          assert column_indexes['pandas_type'] == 'categorical'
          assert column_indexes['numpy_type'] == 'int8'
  
          md = column_indexes['metadata']
          assert md['num_categories'] == 3
          assert md['ordered'] is False
  
      def test_string_column_index(self):
          df = pd.DataFrame(
              [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)],
              columns=pd.Index(list('def'), name='stringz')
          )
          t = pa.Table.from_pandas(df, preserve_index=True)
          js = t.schema.pandas_metadata
  
          column_indexes, = js['column_indexes']
          assert column_indexes['name'] == 'stringz'
          assert column_indexes['name'] == column_indexes['field_name']
          assert column_indexes['numpy_type'] == 'object'
          assert column_indexes['pandas_type'] == 'unicode'
  
          md = column_indexes['metadata']
  
          assert len(md) == 1
          assert md['encoding'] == 'UTF-8'
  
      def test_datetimetz_column_index(self):
          df = pd.DataFrame(
              [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)],
              columns=pd.date_range(
                  start='2017-01-01', periods=3, tz='America/New_York'
              )
          )
          t = pa.Table.from_pandas(df, preserve_index=True)
          js = t.schema.pandas_metadata
  
          column_indexes, = js['column_indexes']
          assert column_indexes['name'] is None
          assert column_indexes['pandas_type'] == 'datetimetz'
          assert column_indexes['numpy_type'] == 'datetime64[ns]'
  
          md = column_indexes['metadata']
          assert md['timezone'] == 'America/New_York'
  
      def test_datetimetz_row_index(self):
          df = pd.DataFrame({
              'a': pd.date_range(
                  start='2017-01-01', periods=3, tz='America/New_York'
              )
          })
          df = df.set_index('a')
  
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_categorical_row_index(self):
          df = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]})
          df['a'] = df.a.astype('category')
          df = df.set_index('a')
  
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_duplicate_column_names_does_not_crash(self):
          df = pd.DataFrame([(1, 'a'), (2, 'b')], columns=list('aa'))
          with pytest.raises(ValueError):
              pa.Table.from_pandas(df)
  
      def test_dictionary_indices_boundscheck(self):
          # ARROW-1658. No validation of indices leads to segfaults in pandas
          indices = [[0, 1], [0, -1]]
  
          for inds in indices:
              arr = pa.DictionaryArray.from_arrays(inds, ['a'], safe=False)
              batch = pa.RecordBatch.from_arrays([arr], ['foo'])
              table = pa.Table.from_batches([batch, batch, batch])
  
              with pytest.raises(IndexError):
                  arr.to_pandas()
  
              with pytest.raises(IndexError):
                  table.to_pandas()
  
      def test_unicode_with_unicode_column_and_index(self):
          df = pd.DataFrame({'あ': ['い']}, index=['う'])
  
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_mixed_column_names(self):
          # mixed type column names are not reconstructed exactly
          df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
  
          for cols in [['あ', b'a'], [1, '2'], [1, 1.5]]:
              df.columns = pd.Index(cols, dtype=object)
  
              # assert that the from_pandas raises the warning
              with pytest.warns(UserWarning):
                  pa.Table.from_pandas(df)
  
              expected = df.copy()
              expected.columns = df.columns.values.astype(str)
              with pytest.warns(UserWarning):
                  _check_pandas_roundtrip(df, expected=expected,
                                          preserve_index=True)
  
      def test_binary_column_name(self):
          column_data = ['い']
          key = 'あ'.encode()
          data = {key: column_data}
          df = pd.DataFrame(data)
  
          # we can't use _check_pandas_roundtrip here because our metadata
          # is always decoded as utf8: even if binary goes in, utf8 comes out
          t = pa.Table.from_pandas(df, preserve_index=True)
          df2 = t.to_pandas()
          assert df.values[0] == df2.values[0]
          assert df.index.values[0] == df2.index.values[0]
          assert df.columns[0] == key
  
      def test_multiindex_duplicate_values(self):
          num_rows = 3
          numbers = list(range(num_rows))
          index = pd.MultiIndex.from_arrays(
              [['foo', 'foo', 'bar'], numbers],
              names=['foobar', 'some_numbers'],
          )
  
          df = pd.DataFrame({'numbers': numbers}, index=index)
  
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_metadata_with_mixed_types(self):
          df = pd.DataFrame({'data': [b'some_bytes', 'some_unicode']})
          table = pa.Table.from_pandas(df)
          js = table.schema.pandas_metadata
          assert 'mixed' not in js
          data_column = js['columns'][0]
          assert data_column['pandas_type'] == 'bytes'
          assert data_column['numpy_type'] == 'object'
  
      def test_ignore_metadata(self):
          df = pd.DataFrame({'a': [1, 2, 3], 'b': ['foo', 'bar', 'baz']},
                            index=['one', 'two', 'three'])
          table = pa.Table.from_pandas(df)
  
          result = table.to_pandas(ignore_metadata=True)
          expected = (table.cast(table.schema.remove_metadata())
                      .to_pandas())
  
          tm.assert_frame_equal(result, expected)
  
      def test_list_metadata(self):
          df = pd.DataFrame({'data': [[1], [2, 3, 4], [5] * 7]})
          schema = pa.schema([pa.field('data', type=pa.list_(pa.int64()))])
          table = pa.Table.from_pandas(df, schema=schema)
          js = table.schema.pandas_metadata
          assert 'mixed' not in js
          data_column = js['columns'][0]
          assert data_column['pandas_type'] == 'list[int64]'
          assert data_column['numpy_type'] == 'object'
  
      def test_struct_metadata(self):
          df = pd.DataFrame({'dicts': [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]})
          table = pa.Table.from_pandas(df)
          pandas_metadata = table.schema.pandas_metadata
          assert pandas_metadata['columns'][0]['pandas_type'] == 'object'
  
      def test_decimal_metadata(self):
          expected = pd.DataFrame({
              'decimals': [
                  decimal.Decimal('394092382910493.12341234678'),
                  -decimal.Decimal('314292388910493.12343437128'),
              ]
          })
          table = pa.Table.from_pandas(expected)
          js = table.schema.pandas_metadata
          assert 'mixed' not in js
          data_column = js['columns'][0]
          assert data_column['pandas_type'] == 'decimal'
          assert data_column['numpy_type'] == 'object'
          assert data_column['metadata'] == {'precision': 26, 'scale': 11}
  
      def test_table_column_subset_metadata(self):
          # ARROW-1883
          # non-default index
          for index in [
                  pd.Index(['a', 'b', 'c'], name='index'),
                  pd.date_range("2017-01-01", periods=3, tz='Europe/Brussels')]:
              df = pd.DataFrame({'a': [1, 2, 3],
                                 'b': [.1, .2, .3]}, index=index)
              table = pa.Table.from_pandas(df)
  
              table_subset = table.remove_column(1)
              result = table_subset.to_pandas()
              expected = df[['a']]
              if isinstance(df.index, pd.DatetimeIndex):
                  df.index.freq = None
              tm.assert_frame_equal(result, expected)
  
              table_subset2 = table_subset.remove_column(1)
              result = table_subset2.to_pandas()
              tm.assert_frame_equal(result, df[['a']].reset_index(drop=True))
  
      def test_to_pandas_column_subset_multiindex(self):
          # ARROW-10122
          df = pd.DataFrame(
              {"first": list(range(5)),
               "second": list(range(5)),
               "value": np.arange(5)}
          )
          table = pa.Table.from_pandas(df.set_index(["first", "second"]))
  
          subset = table.select(["first", "value"])
          result = subset.to_pandas()
          expected = df[["first", "value"]].set_index("first")
          tm.assert_frame_equal(result, expected)
  
      def test_empty_list_metadata(self):
          # Create table with array of empty lists, forced to have type
          # list(string) in pyarrow
          c1 = [["test"], ["a", "b"], None]
          c2 = [[], [], []]
          arrays = OrderedDict([
              ('c1', pa.array(c1, type=pa.list_(pa.string()))),
              ('c2', pa.array(c2, type=pa.list_(pa.string()))),
          ])
          rb = pa.RecordBatch.from_arrays(
              list(arrays.values()),
              list(arrays.keys())
          )
          tbl = pa.Table.from_batches([rb])
  
          # First roundtrip changes schema, because pandas cannot preserve the
          # type of empty lists
          df = tbl.to_pandas()
          tbl2 = pa.Table.from_pandas(df)
          md2 = tbl2.schema.pandas_metadata
  
          # Second roundtrip
          df2 = tbl2.to_pandas()
          expected = pd.DataFrame(OrderedDict([('c1', c1), ('c2', c2)]))
  
          tm.assert_frame_equal(df2, expected)
  
          assert md2['columns'] == [
              {
                  'name': 'c1',
                  'field_name': 'c1',
                  'metadata': None,
                  'numpy_type': 'object',
                  'pandas_type': 'list[unicode]',
              },
              {
                  'name': 'c2',
                  'field_name': 'c2',
                  'metadata': None,
                  'numpy_type': 'object',
                  'pandas_type': 'list[empty]',
              }
          ]
  
      def test_metadata_pandas_version(self):
          df = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]})
          table = pa.Table.from_pandas(df)
          assert table.schema.pandas_metadata['pandas_version'] is not None
  
      def test_mismatch_metadata_schema(self):
          # ARROW-10511
          # It is possible that the metadata and actual schema is not fully
          # matching (eg no timezone information for tz-aware column)
          # -> to_pandas() conversion should not fail on that
          df = pd.DataFrame({"datetime": pd.date_range("2020-01-01", periods=3)})
  
          # OPTION 1: casting after conversion
          table = pa.Table.from_pandas(df)
          # cast the "datetime" column to be tz-aware
          new_col = table["datetime"].cast(pa.timestamp('ns', tz="UTC"))
          new_table1 = table.set_column(
              0, pa.field("datetime", new_col.type), new_col
          )
  
          # OPTION 2: specify schema during conversion
          schema = pa.schema([("datetime", pa.timestamp('ns', tz="UTC"))])
          new_table2 = pa.Table.from_pandas(df, schema=schema)
  
          expected = df.copy()
          expected["datetime"] = expected["datetime"].dt.tz_localize("UTC")
  
          for new_table in [new_table1, new_table2]:
              # ensure the new table still has the pandas metadata
              assert new_table.schema.pandas_metadata is not None
              # convert to pandas
              result = new_table.to_pandas()
              tm.assert_frame_equal(result, expected)
  
  
  class TestConvertPrimitiveTypes:
      """
      Conversion tests for primitive (e.g. numeric) types.
      """
  
      def test_float_no_nulls(self):
          data = {}
          fields = []
          dtypes = [('f2', pa.float16()),
                    ('f4', pa.float32()),
                    ('f8', pa.float64())]
          num_values = 100
  
          for numpy_dtype, arrow_dtype in dtypes:
              values = np.random.randn(num_values)
              data[numpy_dtype] = values.astype(numpy_dtype)
              fields.append(pa.field(numpy_dtype, arrow_dtype))
  
          df = pd.DataFrame(data)
          schema = pa.schema(fields)
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_float_nulls(self):
          num_values = 100
  
          null_mask = np.random.randint(0, 10, size=num_values) < 3
          dtypes = [('f2', pa.float16()),
                    ('f4', pa.float32()),
                    ('f8', pa.float64())]
          names = ['f2', 'f4', 'f8']
          expected_cols = []
  
          arrays = []
          fields = []
          for name, arrow_dtype in dtypes:
              values = np.random.randn(num_values).astype(name)
  
              arr = pa.array(values, from_pandas=True, mask=null_mask)
              arrays.append(arr)
              fields.append(pa.field(name, arrow_dtype))
              values[null_mask] = np.nan
  
              expected_cols.append(values)
  
          ex_frame = pd.DataFrame(dict(zip(names, expected_cols)),
                                  columns=names)
  
          table = pa.Table.from_arrays(arrays, names)
          assert table.schema.equals(pa.schema(fields))
          result = table.to_pandas()
          tm.assert_frame_equal(result, ex_frame)
  
      def test_float_nulls_to_ints(self):
          # ARROW-2135
          df = pd.DataFrame({"a": [1.0, 2.0, np.NaN]})
          schema = pa.schema([pa.field("a", pa.int16(), nullable=True)])
          table = pa.Table.from_pandas(df, schema=schema, safe=False)
          assert table[0].to_pylist() == [1, 2, None]
          tm.assert_frame_equal(df, table.to_pandas())
  
      def test_float_nulls_to_boolean(self):
          s = pd.Series([0.0, 1.0, 2.0, None, -3.0])
          expected = pd.Series([False, True, True, None, True])
          _check_array_roundtrip(s, expected=expected, type=pa.bool_())
  
      def test_series_from_pandas_false_respected(self):
          # Check that explicit from_pandas=False is respected
          s = pd.Series([0.0, np.nan])
          arr = pa.array(s, from_pandas=False)
          assert arr.null_count == 0
          assert np.isnan(arr[1].as_py())
  
      def test_integer_no_nulls(self):
          data = OrderedDict()
          fields = []
  
          numpy_dtypes = [
              ('i1', pa.int8()), ('i2', pa.int16()),
              ('i4', pa.int32()), ('i8', pa.int64()),
              ('u1', pa.uint8()), ('u2', pa.uint16()),
              ('u4', pa.uint32()), ('u8', pa.uint64()),
              ('longlong', pa.int64()), ('ulonglong', pa.uint64())
          ]
          num_values = 100
  
          for dtype, arrow_dtype in numpy_dtypes:
              info = np.iinfo(dtype)
              values = np.random.randint(max(info.min, np.iinfo(np.int_).min),
                                         min(info.max, np.iinfo(np.int_).max),
                                         size=num_values)
              data[dtype] = values.astype(dtype)
              fields.append(pa.field(dtype, arrow_dtype))
  
          df = pd.DataFrame(data)
          schema = pa.schema(fields)
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_all_integer_types(self):
          # Test all Numpy integer aliases
          data = OrderedDict()
          numpy_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8',
                          'byte', 'ubyte', 'short', 'ushort', 'intc', 'uintc',
                          'int_', 'uint', 'longlong', 'ulonglong']
          for dtype in numpy_dtypes:
              data[dtype] = np.arange(12, dtype=dtype)
          df = pd.DataFrame(data)
          _check_pandas_roundtrip(df)
  
          # Do the same with pa.array()
          # (for some reason, it doesn't use the same code paths at all)
          for np_arr in data.values():
              arr = pa.array(np_arr)
              assert arr.to_pylist() == np_arr.tolist()
  
      def test_integer_byteorder(self):
          # Byteswapped arrays are not supported yet
          int_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8']
          for dt in int_dtypes:
              for order in '=<>':
                  data = np.array([1, 2, 42], dtype=order + dt)
                  for np_arr in (data, data[::2]):
                      if data.dtype.isnative:
                          arr = pa.array(data)
                          assert arr.to_pylist() == data.tolist()
                      else:
                          with pytest.raises(NotImplementedError):
                              arr = pa.array(data)
  
      def test_integer_with_nulls(self):
          # pandas requires upcast to float dtype
  
          int_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8']
          num_values = 100
  
          null_mask = np.random.randint(0, 10, size=num_values) < 3
  
          expected_cols = []
          arrays = []
          for name in int_dtypes:
              values = np.random.randint(0, 100, size=num_values)
  
              arr = pa.array(values, mask=null_mask)
              arrays.append(arr)
  
              expected = values.astype('f8')
              expected[null_mask] = np.nan
  
              expected_cols.append(expected)
  
          ex_frame = pd.DataFrame(dict(zip(int_dtypes, expected_cols)),
                                  columns=int_dtypes)
  
          table = pa.Table.from_arrays(arrays, int_dtypes)
          result = table.to_pandas()
  
          tm.assert_frame_equal(result, ex_frame)
  
      def test_array_from_pandas_type_cast(self):
          arr = np.arange(10, dtype='int64')
  
          target_type = pa.int8()
  
          result = pa.array(arr, type=target_type)
          expected = pa.array(arr.astype('int8'))
          assert result.equals(expected)
  
      def test_boolean_no_nulls(self):
          num_values = 100
  
          np.random.seed(0)
  
          df = pd.DataFrame({'bools': np.random.randn(num_values) > 0})
          field = pa.field('bools', pa.bool_())
          schema = pa.schema([field])
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_boolean_nulls(self):
          # pandas requires upcast to object dtype
          num_values = 100
          np.random.seed(0)
  
          mask = np.random.randint(0, 10, size=num_values) < 3
          values = np.random.randint(0, 10, size=num_values) < 5
  
          arr = pa.array(values, mask=mask)
  
          expected = values.astype(object)
          expected[mask] = None
  
          field = pa.field('bools', pa.bool_())
          schema = pa.schema([field])
          ex_frame = pd.DataFrame({'bools': expected})
  
          table = pa.Table.from_arrays([arr], ['bools'])
          assert table.schema.equals(schema)
          result = table.to_pandas()
  
          tm.assert_frame_equal(result, ex_frame)
  
      def test_boolean_to_int(self):
          # test from dtype=bool
          s = pd.Series([True, True, False, True, True] * 2)
          expected = pd.Series([1, 1, 0, 1, 1] * 2)
          _check_array_roundtrip(s, expected=expected, type=pa.int64())
  
      def test_boolean_objects_to_int(self):
          # test from dtype=object
          s = pd.Series([True, True, False, True, True] * 2, dtype=object)
          expected = pd.Series([1, 1, 0, 1, 1] * 2)
          expected_msg = 'Expected integer, got bool'
          with pytest.raises(pa.ArrowTypeError, match=expected_msg):
              _check_array_roundtrip(s, expected=expected, type=pa.int64())
  
      def test_boolean_nulls_to_float(self):
          # test from dtype=object
          s = pd.Series([True, True, False, None, True] * 2)
          expected = pd.Series([1.0, 1.0, 0.0, None, 1.0] * 2)
          _check_array_roundtrip(s, expected=expected, type=pa.float64())
  
      def test_boolean_multiple_columns(self):
          # ARROW-6325 (multiple columns resulting in strided conversion)
          df = pd.DataFrame(np.ones((3, 2), dtype='bool'), columns=['a', 'b'])
          _check_pandas_roundtrip(df)
  
      def test_float_object_nulls(self):
          arr = np.array([None, 1.5, np.float64(3.5)] * 5, dtype=object)
          df = pd.DataFrame({'floats': arr})
          expected = pd.DataFrame({'floats': pd.to_numeric(arr)})
          field = pa.field('floats', pa.float64())
          schema = pa.schema([field])
          _check_pandas_roundtrip(df, expected=expected,
                                  expected_schema=schema)
  
      def test_float_with_null_as_integer(self):
          # ARROW-2298
          s = pd.Series([np.nan, 1., 2., np.nan])
  
          types = [pa.int8(), pa.int16(), pa.int32(), pa.int64(),
                   pa.uint8(), pa.uint16(), pa.uint32(), pa.uint64()]
          for ty in types:
              result = pa.array(s, type=ty)
              expected = pa.array([None, 1, 2, None], type=ty)
              assert result.equals(expected)
  
              df = pd.DataFrame({'has_nulls': s})
              schema = pa.schema([pa.field('has_nulls', ty)])
              result = pa.Table.from_pandas(df, schema=schema,
                                            preserve_index=False)
              assert result[0].chunk(0).equals(expected)
  
      def test_int_object_nulls(self):
          arr = np.array([None, 1, np.int64(3)] * 5, dtype=object)
          df = pd.DataFrame({'ints': arr})
          expected = pd.DataFrame({'ints': pd.to_numeric(arr)})
          field = pa.field('ints', pa.int64())
          schema = pa.schema([field])
          _check_pandas_roundtrip(df, expected=expected,
                                  expected_schema=schema)
  
      def test_boolean_object_nulls(self):
          arr = np.array([False, None, True] * 100, dtype=object)
          df = pd.DataFrame({'bools': arr})
          field = pa.field('bools', pa.bool_())
          schema = pa.schema([field])
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_all_nulls_cast_numeric(self):
          arr = np.array([None], dtype=object)
  
          def _check_type(t):
              a2 = pa.array(arr, type=t)
              assert a2.type == t
              assert a2[0].as_py() is None
  
          _check_type(pa.int32())
          _check_type(pa.float64())
  
      def test_half_floats_from_numpy(self):
          arr = np.array([1.5, np.nan], dtype=np.float16)
          a = pa.array(arr, type=pa.float16())
          x, y = a.to_pylist()
          assert isinstance(x, np.float16)
          assert x == 1.5
          assert isinstance(y, np.float16)
          assert np.isnan(y)
  
          a = pa.array(arr, type=pa.float16(), from_pandas=True)
          x, y = a.to_pylist()
          assert isinstance(x, np.float16)
          assert x == 1.5
          assert y is None
  
  
  @pytest.mark.parametrize('dtype',
                           ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8'])
  def test_array_integer_object_nulls_option(dtype):
      num_values = 100
  
      null_mask = np.random.randint(0, 10, size=num_values) < 3
      values = np.random.randint(0, 100, size=num_values, dtype=dtype)
  
      array = pa.array(values, mask=null_mask)
  
      if null_mask.any():
          expected = values.astype('O')
          expected[null_mask] = None
      else:
          expected = values
  
      result = array.to_pandas(integer_object_nulls=True)
  
      np.testing.assert_equal(result, expected)
  
  
  @pytest.mark.parametrize('dtype',
                           ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8'])
  def test_table_integer_object_nulls_option(dtype):
      num_values = 100
  
      null_mask = np.random.randint(0, 10, size=num_values) < 3
      values = np.random.randint(0, 100, size=num_values, dtype=dtype)
  
      array = pa.array(values, mask=null_mask)
  
      if null_mask.any():
          expected = values.astype('O')
          expected[null_mask] = None
      else:
          expected = values
  
      expected = pd.DataFrame({dtype: expected})
  
      table = pa.Table.from_arrays([array], [dtype])
      result = table.to_pandas(integer_object_nulls=True)
  
      tm.assert_frame_equal(result, expected)
  
  
  class TestConvertDateTimeLikeTypes:
      """
      Conversion tests for datetime- and timestamp-like types (date64, etc.).
      """
  
      def test_timestamps_notimezone_no_nulls(self):
          df = pd.DataFrame({
              'datetime64': np.array([
                  '2007-07-13T01:23:34.123456789',
                  '2006-01-13T12:34:56.432539784',
                  '2010-08-13T05:46:57.437699912'],
                  dtype='datetime64[ns]')
          })
          field = pa.field('datetime64', pa.timestamp('ns'))
          schema = pa.schema([field])
          _check_pandas_roundtrip(
              df,
              expected_schema=schema,
          )
  
      def test_timestamps_notimezone_nulls(self):
          df = pd.DataFrame({
              'datetime64': np.array([
                  '2007-07-13T01:23:34.123456789',
                  None,
                  '2010-08-13T05:46:57.437699912'],
                  dtype='datetime64[ns]')
          })
          field = pa.field('datetime64', pa.timestamp('ns'))
          schema = pa.schema([field])
          _check_pandas_roundtrip(
              df,
              expected_schema=schema,
          )
  
      def test_timestamps_with_timezone(self):
          df = pd.DataFrame({
              'datetime64': np.array([
                  '2007-07-13T01:23:34.123',
                  '2006-01-13T12:34:56.432',
                  '2010-08-13T05:46:57.437'],
                  dtype='datetime64[ms]')
          })
          df['datetime64'] = df['datetime64'].dt.tz_localize('US/Eastern')
          _check_pandas_roundtrip(df)
  
          _check_series_roundtrip(df['datetime64'])
  
          # drop-in a null and ns instead of ms
          df = pd.DataFrame({
              'datetime64': np.array([
                  '2007-07-13T01:23:34.123456789',
                  None,
                  '2006-01-13T12:34:56.432539784',
                  '2010-08-13T05:46:57.437699912'],
                  dtype='datetime64[ns]')
          })
          df['datetime64'] = df['datetime64'].dt.tz_localize('US/Eastern')
  
          _check_pandas_roundtrip(df)
  
      def test_python_datetime(self):
          # ARROW-2106
          date_array = [datetime.today() + timedelta(days=x) for x in range(10)]
          df = pd.DataFrame({
              'datetime': pd.Series(date_array, dtype=object)
          })
  
          table = pa.Table.from_pandas(df)
          assert isinstance(table[0].chunk(0), pa.TimestampArray)
  
          result = table.to_pandas()
          expected_df = pd.DataFrame({
              'datetime': date_array
          })
          tm.assert_frame_equal(expected_df, result)
  
      def test_python_datetime_with_pytz_tzinfo(self):
          for tz in [pytz.utc, pytz.timezone('US/Eastern'), pytz.FixedOffset(1)]:
              values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz)]
              df = pd.DataFrame({'datetime': values})
              _check_pandas_roundtrip(df)
  
      @h.given(st.none() | tzst.timezones())
      def test_python_datetime_with_pytz_timezone(self, tz):
          values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz)]
          df = pd.DataFrame({'datetime': values})
          _check_pandas_roundtrip(df)
  
      def test_python_datetime_with_timezone_tzinfo(self):
          from datetime import timezone
  
          if LooseVersion(pd.__version__) > "0.25.0":
              # older pandas versions fail on datetime.timezone.utc (as in input)
              # vs pytz.UTC (as in result)
              values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=timezone.utc)]
              # also test with index to ensure both paths roundtrip (ARROW-9962)
              df = pd.DataFrame({'datetime': values}, index=values)
              _check_pandas_roundtrip(df, preserve_index=True)
  
          # datetime.timezone is going to be pytz.FixedOffset
          hours = 1
          tz_timezone = timezone(timedelta(hours=hours))
          tz_pytz = pytz.FixedOffset(hours * 60)
          values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz_timezone)]
          values_exp = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz_pytz)]
          df = pd.DataFrame({'datetime': values}, index=values)
          df_exp = pd.DataFrame({'datetime': values_exp}, index=values_exp)
          _check_pandas_roundtrip(df, expected=df_exp, preserve_index=True)
  
      def test_python_datetime_subclass(self):
  
          class MyDatetime(datetime):
              # see https://github.com/pandas-dev/pandas/issues/21142
              nanosecond = 0.0
  
          date_array = [MyDatetime(2000, 1, 1, 1, 1, 1)]
          df = pd.DataFrame({"datetime": pd.Series(date_array, dtype=object)})
  
          table = pa.Table.from_pandas(df)
          assert isinstance(table[0].chunk(0), pa.TimestampArray)
  
          result = table.to_pandas()
          expected_df = pd.DataFrame({"datetime": date_array})
  
          # https://github.com/pandas-dev/pandas/issues/21142
          expected_df["datetime"] = pd.to_datetime(expected_df["datetime"])
  
          tm.assert_frame_equal(expected_df, result)
  
      def test_python_date_subclass(self):
  
          class MyDate(date):
              pass
  
          date_array = [MyDate(2000, 1, 1)]
          df = pd.DataFrame({"date": pd.Series(date_array, dtype=object)})
  
          table = pa.Table.from_pandas(df)
          assert isinstance(table[0].chunk(0), pa.Date32Array)
  
          result = table.to_pandas()
          expected_df = pd.DataFrame(
              {"date": np.array([date(2000, 1, 1)], dtype=object)}
          )
          tm.assert_frame_equal(expected_df, result)
  
      def test_datetime64_to_date32(self):
          # ARROW-1718
          arr = pa.array([date(2017, 10, 23), None])
          c = pa.chunked_array([arr])
          s = c.to_pandas()
  
          arr2 = pa.Array.from_pandas(s, type=pa.date32())
  
          assert arr2.equals(arr.cast('date32'))
  
      @pytest.mark.parametrize('mask', [
          None,
          np.array([True, False, False]),
      ])
      def test_pandas_datetime_to_date64(self, mask):
          s = pd.to_datetime([
              '2018-05-10T00:00:00',
              '2018-05-11T00:00:00',
              '2018-05-12T00:00:00',
          ])
          arr = pa.Array.from_pandas(s, type=pa.date64(), mask=mask)
  
          data = np.array([
              date(2018, 5, 10),
              date(2018, 5, 11),
              date(2018, 5, 12)
          ])
          expected = pa.array(data, mask=mask, type=pa.date64())
  
          assert arr.equals(expected)
  
      @pytest.mark.parametrize('mask', [
          None,
          np.array([True, False, False])
      ])
      def test_pandas_datetime_to_date64_failures(self, mask):
          s = pd.to_datetime([
              '2018-05-10T10:24:01',
              '2018-05-11T10:24:01',
              '2018-05-12T10:24:01',
          ])
  
          expected_msg = 'Timestamp value had non-zero intraday milliseconds'
          with pytest.raises(pa.ArrowInvalid, match=expected_msg):
              pa.Array.from_pandas(s, type=pa.date64(), mask=mask)
  
      def test_array_types_date_as_object(self):
          data = [date(2000, 1, 1),
                  None,
                  date(1970, 1, 1),
                  date(2040, 2, 26)]
          expected_d = np.array(['2000-01-01', None, '1970-01-01',
                                 '2040-02-26'], dtype='datetime64[D]')
  
          expected_ns = np.array(['2000-01-01', None, '1970-01-01',
                                  '2040-02-26'], dtype='datetime64[ns]')
  
          objects = [pa.array(data),
                     pa.chunked_array([data])]
  
          for obj in objects:
              result = obj.to_pandas()
              expected_obj = expected_d.astype(object)
              assert result.dtype == expected_obj.dtype
              npt.assert_array_equal(result, expected_obj)
  
              result = obj.to_pandas(date_as_object=False)
              assert result.dtype == expected_ns.dtype
              npt.assert_array_equal(result, expected_ns)
  
      def test_table_convert_date_as_object(self):
          df = pd.DataFrame({
              'date': [date(2000, 1, 1),
                       None,
                       date(1970, 1, 1),
                       date(2040, 2, 26)]})
  
          table = pa.Table.from_pandas(df, preserve_index=False)
  
          df_datetime = table.to_pandas(date_as_object=False)
          df_object = table.to_pandas()
  
          tm.assert_frame_equal(df.astype('datetime64[ns]'), df_datetime,
                                check_dtype=True)
          tm.assert_frame_equal(df, df_object, check_dtype=True)
  
      def test_date_infer(self):
          df = pd.DataFrame({
              'date': [date(2000, 1, 1),
                       None,
                       date(1970, 1, 1),
                       date(2040, 2, 26)]})
          table = pa.Table.from_pandas(df, preserve_index=False)
          field = pa.field('date', pa.date32())
  
          # schema's metadata is generated by from_pandas conversion
          expected_schema = pa.schema([field], metadata=table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
          result = table.to_pandas()
          tm.assert_frame_equal(result, df)
  
      def test_date_mask(self):
          arr = np.array([date(2017, 4, 3), date(2017, 4, 4)],
                         dtype='datetime64[D]')
          mask = [True, False]
          result = pa.array(arr, mask=np.array(mask))
          expected = np.array([None, date(2017, 4, 4)], dtype='datetime64[D]')
          expected = pa.array(expected, from_pandas=True)
          assert expected.equals(result)
  
      def test_date_objects_typed(self):
          arr = np.array([
              date(2017, 4, 3),
              None,
              date(2017, 4, 4),
              date(2017, 4, 5)], dtype=object)
  
          arr_i4 = np.array([17259, -1, 17260, 17261], dtype='int32')
          arr_i8 = arr_i4.astype('int64') * 86400000
          mask = np.array([False, True, False, False])
  
          t32 = pa.date32()
          t64 = pa.date64()
  
          a32 = pa.array(arr, type=t32)
          a64 = pa.array(arr, type=t64)
  
          a32_expected = pa.array(arr_i4, mask=mask, type=t32)
          a64_expected = pa.array(arr_i8, mask=mask, type=t64)
  
          assert a32.equals(a32_expected)
          assert a64.equals(a64_expected)
  
          # Test converting back to pandas
          colnames = ['date32', 'date64']
          table = pa.Table.from_arrays([a32, a64], colnames)
  
          ex_values = (np.array(['2017-04-03', '2017-04-04', '2017-04-04',
                                 '2017-04-05'],
                                dtype='datetime64[D]'))
          ex_values[1] = pd.NaT.value
  
          ex_datetime64ns = ex_values.astype('datetime64[ns]')
          expected_pandas = pd.DataFrame({'date32': ex_datetime64ns,
                                          'date64': ex_datetime64ns},
                                         columns=colnames)
          table_pandas = table.to_pandas(date_as_object=False)
          tm.assert_frame_equal(table_pandas, expected_pandas)
  
          table_pandas_objects = table.to_pandas()
          ex_objects = ex_values.astype('object')
          expected_pandas_objects = pd.DataFrame({'date32': ex_objects,
                                                  'date64': ex_objects},
                                                 columns=colnames)
          tm.assert_frame_equal(table_pandas_objects,
                                expected_pandas_objects)
  
      def test_pandas_null_values(self):
          # ARROW-842
          pd_NA = getattr(pd, 'NA', None)
          values = np.array([datetime(2000, 1, 1), pd.NaT, pd_NA], dtype=object)
          values_with_none = np.array([datetime(2000, 1, 1), None, None],
                                      dtype=object)
          result = pa.array(values, from_pandas=True)
          expected = pa.array(values_with_none, from_pandas=True)
          assert result.equals(expected)
          assert result.null_count == 2
  
          # ARROW-9407
          assert pa.array([pd.NaT], from_pandas=True).type == pa.null()
          assert pa.array([pd_NA], from_pandas=True).type == pa.null()
  
      def test_dates_from_integers(self):
          t1 = pa.date32()
          t2 = pa.date64()
  
          arr = np.array([17259, 17260, 17261], dtype='int32')
          arr2 = arr.astype('int64') * 86400000
  
          a1 = pa.array(arr, type=t1)
          a2 = pa.array(arr2, type=t2)
  
          expected = date(2017, 4, 3)
          assert a1[0].as_py() == expected
          assert a2[0].as_py() == expected
  
      def test_pytime_from_pandas(self):
          pytimes = [time(1, 2, 3, 1356),
                     time(4, 5, 6, 1356)]
  
          # microseconds
          t1 = pa.time64('us')
  
          aobjs = np.array(pytimes + [None], dtype=object)
          parr = pa.array(aobjs)
          assert parr.type == t1
          assert parr[0].as_py() == pytimes[0]
          assert parr[1].as_py() == pytimes[1]
          assert parr[2].as_py() is None
  
          # DataFrame
          df = pd.DataFrame({'times': aobjs})
          batch = pa.RecordBatch.from_pandas(df)
          assert batch[0].equals(parr)
  
          # Test ndarray of int64 values
          arr = np.array([_pytime_to_micros(v) for v in pytimes],
                         dtype='int64')
  
          a1 = pa.array(arr, type=pa.time64('us'))
          assert a1[0].as_py() == pytimes[0]
  
          a2 = pa.array(arr * 1000, type=pa.time64('ns'))
          assert a2[0].as_py() == pytimes[0]
  
          a3 = pa.array((arr / 1000).astype('i4'),
                        type=pa.time32('ms'))
          assert a3[0].as_py() == pytimes[0].replace(microsecond=1000)
  
          a4 = pa.array((arr / 1000000).astype('i4'),
                        type=pa.time32('s'))
          assert a4[0].as_py() == pytimes[0].replace(microsecond=0)
  
      def test_arrow_time_to_pandas(self):
          pytimes = [time(1, 2, 3, 1356),
                     time(4, 5, 6, 1356),
                     time(0, 0, 0)]
  
          expected = np.array(pytimes[:2] + [None])
          expected_ms = np.array([x.replace(microsecond=1000)
                                  for x in pytimes[:2]] +
                                 [None])
          expected_s = np.array([x.replace(microsecond=0)
                                 for x in pytimes[:2]] +
                                [None])
  
          arr = np.array([_pytime_to_micros(v) for v in pytimes],
                         dtype='int64')
          arr = np.array([_pytime_to_micros(v) for v in pytimes],
                         dtype='int64')
  
          null_mask = np.array([False, False, True], dtype=bool)
  
          a1 = pa.array(arr, mask=null_mask, type=pa.time64('us'))
          a2 = pa.array(arr * 1000, mask=null_mask,
                        type=pa.time64('ns'))
  
          a3 = pa.array((arr / 1000).astype('i4'), mask=null_mask,
                        type=pa.time32('ms'))
          a4 = pa.array((arr / 1000000).astype('i4'), mask=null_mask,
                        type=pa.time32('s'))
  
          names = ['time64[us]', 'time64[ns]', 'time32[ms]', 'time32[s]']
          batch = pa.RecordBatch.from_arrays([a1, a2, a3, a4], names)
  
          for arr, expected_values in [(a1, expected),
                                       (a2, expected),
                                       (a3, expected_ms),
                                       (a4, expected_s)]:
              result_pandas = arr.to_pandas()
              assert (result_pandas.values == expected_values).all()
  
          df = batch.to_pandas()
          expected_df = pd.DataFrame({'time64[us]': expected,
                                      'time64[ns]': expected,
                                      'time32[ms]': expected_ms,
                                      'time32[s]': expected_s},
                                     columns=names)
  
          tm.assert_frame_equal(df, expected_df)
  
      def test_numpy_datetime64_columns(self):
          datetime64_ns = np.array([
              '2007-07-13T01:23:34.123456789',
              None,
              '2006-01-13T12:34:56.432539784',
              '2010-08-13T05:46:57.437699912'],
              dtype='datetime64[ns]')
          _check_array_from_pandas_roundtrip(datetime64_ns)
  
          datetime64_us = np.array([
              '2007-07-13T01:23:34.123456',
              None,
              '2006-01-13T12:34:56.432539',
              '2010-08-13T05:46:57.437699'],
              dtype='datetime64[us]')
          _check_array_from_pandas_roundtrip(datetime64_us)
  
          datetime64_ms = np.array([
              '2007-07-13T01:23:34.123',
              None,
              '2006-01-13T12:34:56.432',
              '2010-08-13T05:46:57.437'],
              dtype='datetime64[ms]')
          _check_array_from_pandas_roundtrip(datetime64_ms)
  
          datetime64_s = np.array([
              '2007-07-13T01:23:34',
              None,
              '2006-01-13T12:34:56',
              '2010-08-13T05:46:57'],
              dtype='datetime64[s]')
          _check_array_from_pandas_roundtrip(datetime64_s)
  
      def test_timestamp_to_pandas_ns(self):
          # non-ns timestamp gets cast to ns on conversion to pandas
          arr = pa.array([1, 2, 3], pa.timestamp('ms'))
          expected = pd.Series(pd.to_datetime([1, 2, 3], unit='ms'))
          s = arr.to_pandas()
          tm.assert_series_equal(s, expected)
          arr = pa.chunked_array([arr])
          s = arr.to_pandas()
          tm.assert_series_equal(s, expected)
  
      def test_timestamp_to_pandas_out_of_bounds(self):
          # ARROW-7758 check for out of bounds timestamps for non-ns timestamps
  
          for unit in ['s', 'ms', 'us']:
              for tz in [None, 'America/New_York']:
                  arr = pa.array([datetime(1, 1, 1)], pa.timestamp(unit, tz=tz))
                  table = pa.table({'a': arr})
  
                  msg = "would result in out of bounds timestamp"
                  with pytest.raises(ValueError, match=msg):
                      arr.to_pandas()
  
                  with pytest.raises(ValueError, match=msg):
                      table.to_pandas()
  
                  with pytest.raises(ValueError, match=msg):
                      # chunked array
                      table.column('a').to_pandas()
  
                  # just ensure those don't give an error, but do not
                  # check actual garbage output
                  arr.to_pandas(safe=False)
                  table.to_pandas(safe=False)
                  table.column('a').to_pandas(safe=False)
  
      def test_timestamp_to_pandas_empty_chunked(self):
          # ARROW-7907 table with chunked array with 0 chunks
          table = pa.table({'a': pa.chunked_array([], type=pa.timestamp('us'))})
          result = table.to_pandas()
          expected = pd.DataFrame({'a': pd.Series([], dtype="datetime64[ns]")})
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize('dtype', [pa.date32(), pa.date64()])
      def test_numpy_datetime64_day_unit(self, dtype):
          datetime64_d = np.array([
              '2007-07-13',
              None,
              '2006-01-15',
              '2010-08-19'],
              dtype='datetime64[D]')
          _check_array_from_pandas_roundtrip(datetime64_d, type=dtype)
  
      def test_array_from_pandas_date_with_mask(self):
          m = np.array([True, False, True])
          data = pd.Series([
              date(1990, 1, 1),
              date(1991, 1, 1),
              date(1992, 1, 1)
          ])
  
          result = pa.Array.from_pandas(data, mask=m)
  
          expected = pd.Series([None, date(1991, 1, 1), None])
          assert pa.Array.from_pandas(expected).equals(result)
  
      @pytest.mark.skipif('1.16.0' <= LooseVersion(np.__version__) < '1.16.1',
                          reason='Until numpy/numpy#12745 is resolved')
      def test_fixed_offset_timezone(self):
          df = pd.DataFrame({
              'a': [
                  pd.Timestamp('2012-11-11 00:00:00+01:00'),
                  pd.NaT
              ]
          })
          _check_pandas_roundtrip(df)
          _check_serialize_components_roundtrip(df)
  
      def test_timedeltas_no_nulls(self):
          df = pd.DataFrame({
              'timedelta64': np.array([0, 3600000000000, 7200000000000],
                                      dtype='timedelta64[ns]')
          })
          field = pa.field('timedelta64', pa.duration('ns'))
          schema = pa.schema([field])
          _check_pandas_roundtrip(
              df,
              expected_schema=schema,
          )
  
      def test_timedeltas_nulls(self):
          df = pd.DataFrame({
              'timedelta64': np.array([0, None, 7200000000000],
                                      dtype='timedelta64[ns]')
          })
          field = pa.field('timedelta64', pa.duration('ns'))
          schema = pa.schema([field])
          _check_pandas_roundtrip(
              df,
              expected_schema=schema,
          )
  
  
  # ----------------------------------------------------------------------
  # Conversion tests for string and binary types.
  
  
  class TestConvertStringLikeTypes:
  
      def test_pandas_unicode(self):
          repeats = 1000
          values = ['foo', None, 'bar', 'mañana', np.nan]
          df = pd.DataFrame({'strings': values * repeats})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
  
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_bytes_to_binary(self):
          values = ['qux', b'foo', None, bytearray(b'barz'), 'qux', np.nan]
          df = pd.DataFrame({'strings': values})
  
          table = pa.Table.from_pandas(df)
          assert table[0].type == pa.binary()
  
          values2 = [b'qux', b'foo', None, b'barz', b'qux', np.nan]
          expected = pd.DataFrame({'strings': values2})
          _check_pandas_roundtrip(df, expected)
  
      @pytest.mark.large_memory
      def test_bytes_exceed_2gb(self):
          v1 = b'x' * 100000000
          v2 = b'x' * 147483646
  
          # ARROW-2227, hit exactly 2GB on the nose
          df = pd.DataFrame({
              'strings': [v1] * 20 + [v2] + ['x'] * 20
          })
          arr = pa.array(df['strings'])
          assert isinstance(arr, pa.ChunkedArray)
          assert arr.num_chunks == 2
          arr = None
  
          table = pa.Table.from_pandas(df)
          assert table[0].num_chunks == 2
  
      @pytest.mark.large_memory
      @pytest.mark.parametrize('char', ['x', b'x'])
      def test_auto_chunking_pandas_series_of_strings(self, char):
          # ARROW-2367
          v1 = char * 100000000
          v2 = char * 147483646
  
          df = pd.DataFrame({
              'strings': [[v1]] * 20 + [[v2]] + [[b'x']]
          })
          arr = pa.array(df['strings'], from_pandas=True)
          assert isinstance(arr, pa.ChunkedArray)
          assert arr.num_chunks == 2
          assert len(arr.chunk(0)) == 21
          assert len(arr.chunk(1)) == 1
  
      def test_fixed_size_bytes(self):
          values = [b'foo', None, bytearray(b'bar'), None, None, b'hey']
          df = pd.DataFrame({'strings': values})
          schema = pa.schema([pa.field('strings', pa.binary(3))])
          table = pa.Table.from_pandas(df, schema=schema)
          assert table.schema[0].type == schema[0].type
          assert table.schema[0].name == schema[0].name
          result = table.to_pandas()
          tm.assert_frame_equal(result, df)
  
      def test_fixed_size_bytes_does_not_accept_varying_lengths(self):
          values = [b'foo', None, b'ba', None, None, b'hey']
          df = pd.DataFrame({'strings': values})
          schema = pa.schema([pa.field('strings', pa.binary(3))])
          with pytest.raises(pa.ArrowInvalid):
              pa.Table.from_pandas(df, schema=schema)
  
      def test_variable_size_bytes(self):
          s = pd.Series([b'123', b'', b'a', None])
          _check_series_roundtrip(s, type_=pa.binary())
  
      def test_binary_from_bytearray(self):
          s = pd.Series([bytearray(b'123'), bytearray(b''), bytearray(b'a'),
                         None])
          # Explicitly set type
          _check_series_roundtrip(s, type_=pa.binary())
          # Infer type from bytearrays
          _check_series_roundtrip(s, expected_pa_type=pa.binary())
  
      def test_large_binary(self):
          s = pd.Series([b'123', b'', b'a', None])
          _check_series_roundtrip(s, type_=pa.large_binary())
          df = pd.DataFrame({'a': s})
          _check_pandas_roundtrip(
              df, schema=pa.schema([('a', pa.large_binary())]))
  
      def test_large_string(self):
          s = pd.Series(['123', '', 'a', None])
          _check_series_roundtrip(s, type_=pa.large_string())
          df = pd.DataFrame({'a': s})
          _check_pandas_roundtrip(
              df, schema=pa.schema([('a', pa.large_string())]))
  
      def test_table_empty_str(self):
          values = ['', '', '', '', '']
          df = pd.DataFrame({'strings': values})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
          table = pa.Table.from_pandas(df, schema=schema)
  
          result1 = table.to_pandas(strings_to_categorical=False)
          expected1 = pd.DataFrame({'strings': values})
          tm.assert_frame_equal(result1, expected1, check_dtype=True)
  
          result2 = table.to_pandas(strings_to_categorical=True)
          expected2 = pd.DataFrame({'strings': pd.Categorical(values)})
          tm.assert_frame_equal(result2, expected2, check_dtype=True)
  
      def test_selective_categoricals(self):
          values = ['', '', '', '', '']
          df = pd.DataFrame({'strings': values})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
          table = pa.Table.from_pandas(df, schema=schema)
          expected_str = pd.DataFrame({'strings': values})
          expected_cat = pd.DataFrame({'strings': pd.Categorical(values)})
  
          result1 = table.to_pandas(categories=['strings'])
          tm.assert_frame_equal(result1, expected_cat, check_dtype=True)
          result2 = table.to_pandas(categories=[])
          tm.assert_frame_equal(result2, expected_str, check_dtype=True)
          result3 = table.to_pandas(categories=('strings',))
          tm.assert_frame_equal(result3, expected_cat, check_dtype=True)
          result4 = table.to_pandas(categories=tuple())
          tm.assert_frame_equal(result4, expected_str, check_dtype=True)
  
      def test_to_pandas_categorical_zero_length(self):
          # ARROW-3586
          array = pa.array([], type=pa.int32())
          table = pa.Table.from_arrays(arrays=[array], names=['col'])
          # This would segfault under 0.11.0
          table.to_pandas(categories=['col'])
  
      def test_to_pandas_categories_already_dictionary(self):
          # Showed up in ARROW-6434, ARROW-6435
          array = pa.array(['foo', 'foo', 'foo', 'bar']).dictionary_encode()
          table = pa.Table.from_arrays(arrays=[array], names=['col'])
          result = table.to_pandas(categories=['col'])
          assert table.to_pandas().equals(result)
  
      def test_table_str_to_categorical_without_na(self):
          values = ['a', 'a', 'b', 'b', 'c']
          df = pd.DataFrame({'strings': values})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
          table = pa.Table.from_pandas(df, schema=schema)
  
          result = table.to_pandas(strings_to_categorical=True)
          expected = pd.DataFrame({'strings': pd.Categorical(values)})
          tm.assert_frame_equal(result, expected, check_dtype=True)
  
          with pytest.raises(pa.ArrowInvalid):
              table.to_pandas(strings_to_categorical=True,
                              zero_copy_only=True)
  
      def test_table_str_to_categorical_with_na(self):
          values = [None, 'a', 'b', np.nan]
          df = pd.DataFrame({'strings': values})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
          table = pa.Table.from_pandas(df, schema=schema)
  
          result = table.to_pandas(strings_to_categorical=True)
          expected = pd.DataFrame({'strings': pd.Categorical(values)})
          tm.assert_frame_equal(result, expected, check_dtype=True)
  
          with pytest.raises(pa.ArrowInvalid):
              table.to_pandas(strings_to_categorical=True,
                              zero_copy_only=True)
  
      # Regression test for ARROW-2101
      def test_array_of_bytes_to_strings(self):
          converted = pa.array(np.array([b'x'], dtype=object), pa.string())
          assert converted.type == pa.string()
  
      # Make sure that if an ndarray of bytes is passed to the array
      # constructor and the type is string, it will fail if those bytes
      # cannot be converted to utf-8
      def test_array_of_bytes_to_strings_bad_data(self):
          with pytest.raises(
                  pa.lib.ArrowInvalid,
                  match="was not a utf8 string"):
              pa.array(np.array([b'\x80\x81'], dtype=object), pa.string())
  
      def test_numpy_string_array_to_fixed_size_binary(self):
          arr = np.array([b'foo', b'bar', b'baz'], dtype='|S3')
  
          converted = pa.array(arr, type=pa.binary(3))
          expected = pa.array(list(arr), type=pa.binary(3))
          assert converted.equals(expected)
  
          mask = np.array([True, False, True])
          converted = pa.array(arr, type=pa.binary(3), mask=mask)
          expected = pa.array([b'foo', None, b'baz'], type=pa.binary(3))
          assert converted.equals(expected)
  
          with pytest.raises(pa.lib.ArrowInvalid,
                             match=r'Got bytestring of length 3 \(expected 4\)'):
              arr = np.array([b'foo', b'bar', b'baz'], dtype='|S3')
              pa.array(arr, type=pa.binary(4))
  
          with pytest.raises(
                  pa.lib.ArrowInvalid,
                  match=r'Got bytestring of length 12 \(expected 3\)'):
              arr = np.array([b'foo', b'bar', b'baz'], dtype='|U3')
              pa.array(arr, type=pa.binary(3))
  
  
  class TestConvertDecimalTypes:
      """
      Conversion test for decimal types.
      """
      decimal32 = [
          decimal.Decimal('-1234.123'),
          decimal.Decimal('1234.439')
      ]
      decimal64 = [
          decimal.Decimal('-129934.123331'),
          decimal.Decimal('129534.123731')
      ]
      decimal128 = [
          decimal.Decimal('394092382910493.12341234678'),
          decimal.Decimal('-314292388910493.12343437128')
      ]
  
      @pytest.mark.parametrize(('values', 'expected_type'), [
          pytest.param(decimal32, pa.decimal128(7, 3), id='decimal32'),
          pytest.param(decimal64, pa.decimal128(12, 6), id='decimal64'),
          pytest.param(decimal128, pa.decimal128(26, 11), id='decimal128')
      ])
      def test_decimal_from_pandas(self, values, expected_type):
          expected = pd.DataFrame({'decimals': values})
          table = pa.Table.from_pandas(expected, preserve_index=False)
          field = pa.field('decimals', expected_type)
  
          # schema's metadata is generated by from_pandas conversion
          expected_schema = pa.schema([field], metadata=table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
      @pytest.mark.parametrize('values', [
          pytest.param(decimal32, id='decimal32'),
          pytest.param(decimal64, id='decimal64'),
          pytest.param(decimal128, id='decimal128')
      ])
      def test_decimal_to_pandas(self, values):
          expected = pd.DataFrame({'decimals': values})
          converted = pa.Table.from_pandas(expected)
          df = converted.to_pandas()
          tm.assert_frame_equal(df, expected)
  
      def test_decimal_fails_with_truncation(self):
          data1 = [decimal.Decimal('1.234')]
          type1 = pa.decimal128(10, 2)
          with pytest.raises(pa.ArrowInvalid):
              pa.array(data1, type=type1)
  
          data2 = [decimal.Decimal('1.2345')]
          type2 = pa.decimal128(10, 3)
          with pytest.raises(pa.ArrowInvalid):
              pa.array(data2, type=type2)
  
      def test_decimal_with_different_precisions(self):
          data = [
              decimal.Decimal('0.01'),
              decimal.Decimal('0.001'),
          ]
          series = pd.Series(data)
          array = pa.array(series)
          assert array.to_pylist() == data
          assert array.type == pa.decimal128(3, 3)
  
          array = pa.array(data, type=pa.decimal128(12, 5))
          expected = [decimal.Decimal('0.01000'), decimal.Decimal('0.00100')]
          assert array.to_pylist() == expected
  
      def test_decimal_with_None_explicit_type(self):
          series = pd.Series([decimal.Decimal('3.14'), None])
          _check_series_roundtrip(series, type_=pa.decimal128(12, 5))
  
          # Test that having all None values still produces decimal array
          series = pd.Series([None] * 2)
          _check_series_roundtrip(series, type_=pa.decimal128(12, 5))
  
      def test_decimal_with_None_infer_type(self):
          series = pd.Series([decimal.Decimal('3.14'), None])
          _check_series_roundtrip(series, expected_pa_type=pa.decimal128(3, 2))
  
      def test_strided_objects(self, tmpdir):
          # see ARROW-3053
          data = {
              'a': {0: 'a'},
              'b': {0: decimal.Decimal('0.0')}
          }
  
          # This yields strided objects
          df = pd.DataFrame.from_dict(data)
          _check_pandas_roundtrip(df)
  
  
  class TestConvertListTypes:
      """
      Conversion tests for list<> types.
      """
  
      def test_column_of_arrays(self):
          df, schema = dataframe_with_arrays()
          _check_pandas_roundtrip(df, schema=schema, expected_schema=schema)
          table = pa.Table.from_pandas(df, schema=schema, preserve_index=False)
  
          # schema's metadata is generated by from_pandas conversion
          expected_schema = schema.with_metadata(table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
          for column in df.columns:
              field = schema.field(column)
              _check_array_roundtrip(df[column], type=field.type)
  
      def test_column_of_arrays_to_py(self):
          # Test regression in ARROW-1199 not caught in above test
          dtype = 'i1'
          arr = np.array([
              np.arange(10, dtype=dtype),
              np.arange(5, dtype=dtype),
              None,
              np.arange(1, dtype=dtype)
          ], dtype=object)
          type_ = pa.list_(pa.int8())
          parr = pa.array(arr, type=type_)
  
          assert parr[0].as_py() == list(range(10))
          assert parr[1].as_py() == list(range(5))
          assert parr[2].as_py() is None
          assert parr[3].as_py() == [0]
  
      def test_column_of_boolean_list(self):
          # ARROW-4370: Table to pandas conversion fails for list of bool
          array = pa.array([[True, False], [True]], type=pa.list_(pa.bool_()))
          table = pa.Table.from_arrays([array], names=['col1'])
          df = table.to_pandas()
  
          expected_df = pd.DataFrame({'col1': [[True, False], [True]]})
          tm.assert_frame_equal(df, expected_df)
  
          s = table[0].to_pandas()
          tm.assert_series_equal(pd.Series(s), df['col1'], check_names=False)
  
      def test_column_of_decimal_list(self):
          array = pa.array([[decimal.Decimal('1'), decimal.Decimal('2')],
                            [decimal.Decimal('3.3')]],
                           type=pa.list_(pa.decimal128(2, 1)))
          table = pa.Table.from_arrays([array], names=['col1'])
          df = table.to_pandas()
  
          expected_df = pd.DataFrame(
              {'col1': [[decimal.Decimal('1'), decimal.Decimal('2')],
                        [decimal.Decimal('3.3')]]})
          tm.assert_frame_equal(df, expected_df)
  
      def test_nested_types_from_ndarray_null_entries(self):
          # Root cause of ARROW-6435
          s = pd.Series(np.array([np.nan, np.nan], dtype=object))
  
          for ty in [pa.list_(pa.int64()),
                     pa.large_list(pa.int64()),
                     pa.struct([pa.field('f0', 'int32')])]:
              result = pa.array(s, type=ty)
              expected = pa.array([None, None], type=ty)
              assert result.equals(expected)
  
              with pytest.raises(TypeError):
                  pa.array(s.values, type=ty)
  
      def test_column_of_lists(self):
          df, schema = dataframe_with_lists()
          _check_pandas_roundtrip(df, schema=schema, expected_schema=schema)
          table = pa.Table.from_pandas(df, schema=schema, preserve_index=False)
  
          # schema's metadata is generated by from_pandas conversion
          expected_schema = schema.with_metadata(table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
          for column in df.columns:
              field = schema.field(column)
              _check_array_roundtrip(df[column], type=field.type)
  
      def test_column_of_lists_first_empty(self):
          # ARROW-2124
          num_lists = [[], [2, 3, 4], [3, 6, 7, 8], [], [2]]
          series = pd.Series([np.array(s, dtype=float) for s in num_lists])
          arr = pa.array(series)
          result = pd.Series(arr.to_pandas())
          tm.assert_series_equal(result, series)
  
      def test_column_of_lists_chunked(self):
          # ARROW-1357
          df = pd.DataFrame({
              'lists': np.array([
                  [1, 2],
                  None,
                  [2, 3],
                  [4, 5],
                  [6, 7],
                  [8, 9]
              ], dtype=object)
          })
  
          schema = pa.schema([
              pa.field('lists', pa.list_(pa.int64()))
          ])
  
          t1 = pa.Table.from_pandas(df[:2], schema=schema)
          t2 = pa.Table.from_pandas(df[2:], schema=schema)
  
          table = pa.concat_tables([t1, t2])
          result = table.to_pandas()
  
          tm.assert_frame_equal(result, df)
  
      def test_empty_column_of_lists_chunked(self):
          df = pd.DataFrame({
              'lists': np.array([], dtype=object)
          })
  
          schema = pa.schema([
              pa.field('lists', pa.list_(pa.int64()))
          ])
  
          table = pa.Table.from_pandas(df, schema=schema)
          result = table.to_pandas()
  
          tm.assert_frame_equal(result, df)
  
      def test_column_of_lists_chunked2(self):
          data1 = [[0, 1], [2, 3], [4, 5], [6, 7], [10, 11],
                   [12, 13], [14, 15], [16, 17]]
          data2 = [[8, 9], [18, 19]]
  
          a1 = pa.array(data1)
          a2 = pa.array(data2)
  
          t1 = pa.Table.from_arrays([a1], names=['a'])
          t2 = pa.Table.from_arrays([a2], names=['a'])
  
          concatenated = pa.concat_tables([t1, t2])
  
          result = concatenated.to_pandas()
          expected = pd.DataFrame({'a': data1 + data2})
  
          tm.assert_frame_equal(result, expected)
  
      def test_column_of_lists_strided(self):
          df, schema = dataframe_with_lists()
          df = pd.concat([df] * 6, ignore_index=True)
  
          arr = df['int64'].values[::3]
          assert arr.strides[0] != 8
  
          _check_array_roundtrip(arr)
  
      def test_nested_lists_all_none(self):
          data = np.array([[None, None], None], dtype=object)
  
          arr = pa.array(data)
          expected = pa.array(list(data))
          assert arr.equals(expected)
          assert arr.type == pa.list_(pa.null())
  
          data2 = np.array([None, None, [None, None],
                            np.array([None, None], dtype=object)],
                           dtype=object)
          arr = pa.array(data2)
          expected = pa.array([None, None, [None, None], [None, None]])
          assert arr.equals(expected)
  
      def test_nested_lists_all_empty(self):
          # ARROW-2128
          data = pd.Series([[], [], []])
          arr = pa.array(data)
          expected = pa.array(list(data))
          assert arr.equals(expected)
          assert arr.type == pa.list_(pa.null())
  
      def test_nested_list_first_empty(self):
          # ARROW-2711
          data = pd.Series([[], ["a"]])
          arr = pa.array(data)
          expected = pa.array(list(data))
          assert arr.equals(expected)
          assert arr.type == pa.list_(pa.string())
  
      def test_nested_smaller_ints(self):
          # ARROW-1345, ARROW-2008, there were some type inference bugs happening
          # before
          data = pd.Series([np.array([1, 2, 3], dtype='i1'), None])
          result = pa.array(data)
          result2 = pa.array(data.values)
          expected = pa.array([[1, 2, 3], None], type=pa.list_(pa.int8()))
          assert result.equals(expected)
          assert result2.equals(expected)
  
          data3 = pd.Series([np.array([1, 2, 3], dtype='f4'), None])
          result3 = pa.array(data3)
          expected3 = pa.array([[1, 2, 3], None], type=pa.list_(pa.float32()))
          assert result3.equals(expected3)
  
      def test_infer_lists(self):
          data = OrderedDict([
              ('nan_ints', [[None, 1], [2, 3]]),
              ('ints', [[0, 1], [2, 3]]),
              ('strs', [[None, 'b'], ['c', 'd']]),
              ('nested_strs', [[[None, 'b'], ['c', 'd']], None])
          ])
          df = pd.DataFrame(data)
  
          expected_schema = pa.schema([
              pa.field('nan_ints', pa.list_(pa.int64())),
              pa.field('ints', pa.list_(pa.int64())),
              pa.field('strs', pa.list_(pa.string())),
              pa.field('nested_strs', pa.list_(pa.list_(pa.string())))
          ])
  
          _check_pandas_roundtrip(df, expected_schema=expected_schema)
  
      def test_fixed_size_list(self):
          # ARROW-7365
          fixed_ty = pa.list_(pa.int64(), list_size=4)
          variable_ty = pa.list_(pa.int64())
  
          data = [[0, 1, 2, 3], None, [4, 5, 6, 7], [8, 9, 10, 11]]
          fixed_arr = pa.array(data, type=fixed_ty)
          variable_arr = pa.array(data, type=variable_ty)
  
          result = fixed_arr.to_pandas()
          expected = variable_arr.to_pandas()
  
          for left, right in zip(result, expected):
              if left is None:
                  assert right is None
              npt.assert_array_equal(left, right)
  
      def test_infer_numpy_array(self):
          data = OrderedDict([
              ('ints', [
                  np.array([0, 1], dtype=np.int64),
                  np.array([2, 3], dtype=np.int64)
              ])
          ])
          df = pd.DataFrame(data)
          expected_schema = pa.schema([
              pa.field('ints', pa.list_(pa.int64()))
          ])
  
          _check_pandas_roundtrip(df, expected_schema=expected_schema)
  
      def test_to_list_of_structs_pandas(self):
          ints = pa.array([1, 2, 3], pa.int32())
          strings = pa.array([['a', 'b'], ['c', 'd'], ['e', 'f']],
                             pa.list_(pa.string()))
          structs = pa.StructArray.from_arrays([ints, strings], ['f1', 'f2'])
          data = pa.ListArray.from_arrays([0, 1, 3], structs)
  
          expected = pd.Series([
              [{'f1': 1, 'f2': ['a', 'b']}],
              [{'f1': 2, 'f2': ['c', 'd']},
               {'f1': 3, 'f2': ['e', 'f']}]
          ])
  
          series = pd.Series(data.to_pandas())
          tm.assert_series_equal(series, expected)
  
      @pytest.mark.parametrize('t,data,expected', [
          (
              pa.int64,
              [[1, 2], [3], None],
              [None, [3], None]
          ),
          (
              pa.string,
              [['aaa', 'bb'], ['c'], None],
              [None, ['c'], None]
          ),
          (
              pa.null,
              [[None, None], [None], None],
              [None, [None], None]
          )
      ])
      def test_array_from_pandas_typed_array_with_mask(self, t, data, expected):
          m = np.array([True, False, True])
  
          s = pd.Series(data)
          result = pa.Array.from_pandas(s, mask=m, type=pa.list_(t()))
  
          assert pa.Array.from_pandas(expected,
                                      type=pa.list_(t())).equals(result)
  
      def test_empty_list_roundtrip(self):
          empty_list_array = np.empty((3,), dtype=object)
          empty_list_array.fill([])
  
          df = pd.DataFrame({'a': np.array(['1', '2', '3']),
                             'b': empty_list_array})
          tbl = pa.Table.from_pandas(df)
  
          result = tbl.to_pandas()
  
          tm.assert_frame_equal(result, df)
  
      def test_array_from_nested_arrays(self):
          df, schema = dataframe_with_arrays()
          for field in schema:
              arr = df[field.name].values
              expected = pa.array(list(arr), type=field.type)
              result = pa.array(arr)
              assert result.type == field.type  # == list<scalar>
              assert result.equals(expected)
  
      def test_nested_large_list(self):
          s = (pa.array([[[1, 2, 3], [4]], None],
                        type=pa.large_list(pa.large_list(pa.int64())))
               .to_pandas())
          tm.assert_series_equal(
              s, pd.Series([[[1, 2, 3], [4]], None], dtype=object),
              check_names=False)
  
      def test_large_binary_list(self):
          for list_type_factory in (pa.list_, pa.large_list):
              s = (pa.array([["aa", "bb"], None, ["cc"], []],
                            type=list_type_factory(pa.large_binary()))
                   .to_pandas())
              tm.assert_series_equal(
                  s, pd.Series([[b"aa", b"bb"], None, [b"cc"], []]),
                  check_names=False)
              s = (pa.array([["aa", "bb"], None, ["cc"], []],
                            type=list_type_factory(pa.large_string()))
                   .to_pandas())
              tm.assert_series_equal(
                  s, pd.Series([["aa", "bb"], None, ["cc"], []]),
                  check_names=False)
  
      def test_list_of_dictionary(self):
          child = pa.array(["foo", "bar", None, "foo"]).dictionary_encode()
          arr = pa.ListArray.from_arrays([0, 1, 3, 3, 4], child)
  
          # Expected a Series of lists
          expected = pd.Series(arr.to_pylist())
          tm.assert_series_equal(arr.to_pandas(), expected)
  
          # Same but with nulls
          arr = arr.take([0, 1, None, 3])
          expected[2] = None
          tm.assert_series_equal(arr.to_pandas(), expected)
  
      @pytest.mark.slow
      @pytest.mark.large_memory
      def test_auto_chunking_on_list_overflow(self):
          # ARROW-9976
          n = 2**24
          df = pd.DataFrame.from_dict({
              "a": list(np.zeros((n, 2**7), dtype='uint8')),
              "b": range(n)
          })
          table = pa.Table.from_pandas(df)
  
          column_a = table[0]
          assert column_a.num_chunks == 2
          assert len(column_a.chunk(0)) == 2**24 - 1
          assert len(column_a.chunk(1)) == 1
  
      def test_map_array_roundtrip(self):
          data = [[(b'a', 1), (b'b', 2)],
                  [(b'c', 3)],
                  [(b'd', 4), (b'e', 5), (b'f', 6)],
                  [(b'g', 7)]]
  
          df = pd.DataFrame({"map": data})
          schema = pa.schema([("map", pa.map_(pa.binary(), pa.int32()))])
  
          _check_pandas_roundtrip(df, schema=schema)
  
      def test_map_array_chunked(self):
          data1 = [[(b'a', 1), (b'b', 2)],
                   [(b'c', 3)],
                   [(b'd', 4), (b'e', 5), (b'f', 6)],
                   [(b'g', 7)]]
          data2 = [[(k, v * 2) for k, v in row] for row in data1]
  
          arr1 = pa.array(data1, type=pa.map_(pa.binary(), pa.int32()))
          arr2 = pa.array(data2, type=pa.map_(pa.binary(), pa.int32()))
          arr = pa.chunked_array([arr1, arr2])
  
          expected = pd.Series(data1 + data2)
          actual = arr.to_pandas()
          tm.assert_series_equal(actual, expected, check_names=False)
  
      def test_map_array_with_nulls(self):
          data = [[(b'a', 1), (b'b', 2)],
                  None,
                  [(b'd', 4), (b'e', 5), (b'f', None)],
                  [(b'g', 7)]]
  
          # None value in item array causes upcast to float
          expected = [[(k, float(v) if v is not None else None) for k, v in row]
                      if row is not None else None for row in data]
          expected = pd.Series(expected)
  
          arr = pa.array(data, type=pa.map_(pa.binary(), pa.int32()))
          actual = arr.to_pandas()
          tm.assert_series_equal(actual, expected, check_names=False)
  
      def test_map_array_dictionary_encoded(self):
          offsets = pa.array([0, 3, 5])
          items = pa.array(['a', 'b', 'c', 'a', 'd']).dictionary_encode()
          keys = pa.array(list(range(len(items))))
          arr = pa.MapArray.from_arrays(offsets, keys, items)
  
          # Dictionary encoded values converted to dense
          expected = pd.Series(
              [[(0, 'a'), (1, 'b'), (2, 'c')], [(3, 'a'), (4, 'd')]])
  
          actual = arr.to_pandas()
          tm.assert_series_equal(actual, expected, check_names=False)
  
  
  class TestConvertStructTypes:
      """
      Conversion tests for struct types.
      """
  
      def test_pandas_roundtrip(self):
          df = pd.DataFrame({'dicts': [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]})
  
          expected_schema = pa.schema([
              ('dicts', pa.struct([('a', pa.int64()), ('b', pa.int64())])),
          ])
  
          _check_pandas_roundtrip(df, expected_schema=expected_schema)
  
          # specifying schema explicitly in from_pandas
          _check_pandas_roundtrip(
              df, schema=expected_schema, expected_schema=expected_schema)
  
      def test_to_pandas(self):
          ints = pa.array([None, 2, 3], type=pa.int64())
          strs = pa.array(['a', None, 'c'], type=pa.string())
          bools = pa.array([True, False, None], type=pa.bool_())
          arr = pa.StructArray.from_arrays(
              [ints, strs, bools],
              ['ints', 'strs', 'bools'])
  
          expected = pd.Series([
              {'ints': None, 'strs': 'a', 'bools': True},
              {'ints': 2, 'strs': None, 'bools': False},
              {'ints': 3, 'strs': 'c', 'bools': None},
          ])
  
          series = pd.Series(arr.to_pandas())
          tm.assert_series_equal(series, expected)
  
      def test_to_pandas_multiple_chunks(self):
          # ARROW-11855
          gc.collect()
          bytes_start = pa.total_allocated_bytes()
          ints1 = pa.array([1], type=pa.int64())
          ints2 = pa.array([2], type=pa.int64())
          arr1 = pa.StructArray.from_arrays([ints1], ['ints'])
          arr2 = pa.StructArray.from_arrays([ints2], ['ints'])
          arr = pa.chunked_array([arr1, arr2])
  
          expected = pd.Series([
              {'ints': 1},
              {'ints': 2}
          ])
  
          series = pd.Series(arr.to_pandas())
          tm.assert_series_equal(series, expected)
  
          del series
          del arr
          del arr1
          del arr2
          del ints1
          del ints2
          bytes_end = pa.total_allocated_bytes()
          assert bytes_end == bytes_start
  
      def test_from_numpy(self):
          dt = np.dtype([('x', np.int32),
                         (('y_title', 'y'), np.bool_)])
          ty = pa.struct([pa.field('x', pa.int32()),
                          pa.field('y', pa.bool_())])
  
          data = np.array([], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == []
  
          data = np.array([(42, True), (43, False)], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == [{'x': 42, 'y': True},
                                     {'x': 43, 'y': False}]
  
          # With mask
          arr = pa.array(data, mask=np.bool_([False, True]), type=ty)
          assert arr.to_pylist() == [{'x': 42, 'y': True}, None]
  
          # Trivial struct type
          dt = np.dtype([])
          ty = pa.struct([])
  
          data = np.array([], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == []
  
          data = np.array([(), ()], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == [{}, {}]
  
      def test_from_numpy_nested(self):
          # Note: an object field inside a struct
          dt = np.dtype([('x', np.dtype([('xx', np.int8),
                                         ('yy', np.bool_)])),
                         ('y', np.int16),
                         ('z', np.object_)])
          # Note: itemsize is not a multiple of sizeof(object)
          assert dt.itemsize == 12
          ty = pa.struct([pa.field('x', pa.struct([pa.field('xx', pa.int8()),
                                                   pa.field('yy', pa.bool_())])),
                          pa.field('y', pa.int16()),
                          pa.field('z', pa.string())])
  
          data = np.array([], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == []
  
          data = np.array([
              ((1, True), 2, 'foo'),
              ((3, False), 4, 'bar')], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == [
              {'x': {'xx': 1, 'yy': True}, 'y': 2, 'z': 'foo'},
              {'x': {'xx': 3, 'yy': False}, 'y': 4, 'z': 'bar'}]
  
      @pytest.mark.large_memory
      def test_from_numpy_large(self):
          # Exercise rechunking + nulls
          target_size = 3 * 1024**3  # 4GB
          dt = np.dtype([('x', np.float64), ('y', 'object')])
          bs = 65536 - dt.itemsize
          block = b'.' * bs
          n = target_size // (bs + dt.itemsize)
          data = np.zeros(n, dtype=dt)
          data['x'] = np.random.random_sample(n)
          data['y'] = block
          # Add implicit nulls
          data['x'][data['x'] < 0.2] = np.nan
  
          ty = pa.struct([pa.field('x', pa.float64()),
                          pa.field('y', pa.binary())])
          arr = pa.array(data, type=ty, from_pandas=True)
          assert arr.num_chunks == 2
  
          def iter_chunked_array(arr):
              for chunk in arr.iterchunks():
                  yield from chunk
  
          def check(arr, data, mask=None):
              assert len(arr) == len(data)
              xs = data['x']
              ys = data['y']
              for i, obj in enumerate(iter_chunked_array(arr)):
                  try:
                      d = obj.as_py()
                      if mask is not None and mask[i]:
                          assert d is None
                      else:
                          x = xs[i]
                          if np.isnan(x):
                              assert d['x'] is None
                          else:
                              assert d['x'] == x
                          assert d['y'] == ys[i]
                  except Exception:
                      print("Failed at index", i)
                      raise
  
          check(arr, data)
          del arr
  
          # Now with explicit mask
          mask = np.random.random_sample(n) < 0.2
          arr = pa.array(data, type=ty, mask=mask, from_pandas=True)
          assert arr.num_chunks == 2
  
          check(arr, data, mask)
          del arr
  
      def test_from_numpy_bad_input(self):
          ty = pa.struct([pa.field('x', pa.int32()),
                          pa.field('y', pa.bool_())])
          dt = np.dtype([('x', np.int32),
                         ('z', np.bool_)])
  
          data = np.array([], dtype=dt)
          with pytest.raises(ValueError,
                             match="Missing field 'y'"):
              pa.array(data, type=ty)
          data = np.int32([])
          with pytest.raises(TypeError,
                             match="Expected struct array"):
              pa.array(data, type=ty)
  
      def test_from_tuples(self):
          df = pd.DataFrame({'tuples': [(1, 2), (3, 4)]})
          expected_df = pd.DataFrame(
              {'tuples': [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]})
  
          # conversion from tuples works when specifying expected struct type
          struct_type = pa.struct([('a', pa.int64()), ('b', pa.int64())])
  
          arr = np.asarray(df['tuples'])
          _check_array_roundtrip(
              arr, expected=expected_df['tuples'], type=struct_type)
  
          expected_schema = pa.schema([('tuples', struct_type)])
          _check_pandas_roundtrip(
              df, expected=expected_df, schema=expected_schema,
              expected_schema=expected_schema)
  
      def test_struct_of_dictionary(self):
          names = ['ints', 'strs']
          children = [pa.array([456, 789, 456]).dictionary_encode(),
                      pa.array(["foo", "foo", None]).dictionary_encode()]
          arr = pa.StructArray.from_arrays(children, names=names)
  
          # Expected a Series of {field name: field value} dicts
          rows_as_tuples = zip(*(child.to_pylist() for child in children))
          rows_as_dicts = [dict(zip(names, row)) for row in rows_as_tuples]
  
          expected = pd.Series(rows_as_dicts)
          tm.assert_series_equal(arr.to_pandas(), expected)
  
          # Same but with nulls
          arr = arr.take([0, None, 2])
          expected[1] = None
          tm.assert_series_equal(arr.to_pandas(), expected)
  
  
  class TestZeroCopyConversion:
      """
      Tests that zero-copy conversion works with some types.
      """
  
      def test_zero_copy_success(self):
          result = pa.array([0, 1, 2]).to_pandas(zero_copy_only=True)
          npt.assert_array_equal(result, [0, 1, 2])
  
      def test_zero_copy_dictionaries(self):
          arr = pa.DictionaryArray.from_arrays(
              np.array([0, 0]),
              np.array([5]))
  
          result = arr.to_pandas(zero_copy_only=True)
          values = pd.Categorical([5, 5])
  
          tm.assert_series_equal(pd.Series(result), pd.Series(values),
                                 check_names=False)
  
      def test_zero_copy_timestamp(self):
          arr = np.array(['2007-07-13'], dtype='datetime64[ns]')
          result = pa.array(arr).to_pandas(zero_copy_only=True)
          npt.assert_array_equal(result, arr)
  
      def test_zero_copy_duration(self):
          arr = np.array([1], dtype='timedelta64[ns]')
          result = pa.array(arr).to_pandas(zero_copy_only=True)
          npt.assert_array_equal(result, arr)
  
      def check_zero_copy_failure(self, arr):
          with pytest.raises(pa.ArrowInvalid):
              arr.to_pandas(zero_copy_only=True)
  
      def test_zero_copy_failure_on_object_types(self):
          self.check_zero_copy_failure(pa.array(['A', 'B', 'C']))
  
      def test_zero_copy_failure_with_int_when_nulls(self):
          self.check_zero_copy_failure(pa.array([0, 1, None]))
  
      def test_zero_copy_failure_with_float_when_nulls(self):
          self.check_zero_copy_failure(pa.array([0.0, 1.0, None]))
  
      def test_zero_copy_failure_on_bool_types(self):
          self.check_zero_copy_failure(pa.array([True, False]))
  
      def test_zero_copy_failure_on_list_types(self):
          arr = pa.array([[1, 2], [8, 9]], type=pa.list_(pa.int64()))
          self.check_zero_copy_failure(arr)
  
      def test_zero_copy_failure_on_timestamp_with_nulls(self):
          arr = np.array([1, None], dtype='datetime64[ns]')
          self.check_zero_copy_failure(pa.array(arr))
  
      def test_zero_copy_failure_on_duration_with_nulls(self):
          arr = np.array([1, None], dtype='timedelta64[ns]')
          self.check_zero_copy_failure(pa.array(arr))
  
  
  def _non_threaded_conversion():
      df = _alltypes_example()
      _check_pandas_roundtrip(df, use_threads=False)
      _check_pandas_roundtrip(df, use_threads=False, as_batch=True)
  
  
  def _threaded_conversion():
      df = _alltypes_example()
      _check_pandas_roundtrip(df, use_threads=True)
      _check_pandas_roundtrip(df, use_threads=True, as_batch=True)
  
  
  class TestConvertMisc:
      """
      Miscellaneous conversion tests.
      """
  
      type_pairs = [
          (np.int8, pa.int8()),
          (np.int16, pa.int16()),
          (np.int32, pa.int32()),
          (np.int64, pa.int64()),
          (np.uint8, pa.uint8()),
          (np.uint16, pa.uint16()),
          (np.uint32, pa.uint32()),
          (np.uint64, pa.uint64()),
          (np.float16, pa.float16()),
          (np.float32, pa.float32()),
          (np.float64, pa.float64()),
          # XXX unsupported
          # (np.dtype([('a', 'i2')]), pa.struct([pa.field('a', pa.int16())])),
          (np.object_, pa.string()),
          (np.object_, pa.binary()),
          (np.object_, pa.binary(10)),
          (np.object_, pa.list_(pa.int64())),
      ]
  
      def test_all_none_objects(self):
          df = pd.DataFrame({'a': [None, None, None]})
          _check_pandas_roundtrip(df)
  
      def test_all_none_category(self):
          df = pd.DataFrame({'a': [None, None, None]})
          df['a'] = df['a'].astype('category')
          _check_pandas_roundtrip(df)
  
      def test_empty_arrays(self):
          for dtype, pa_type in self.type_pairs:
              arr = np.array([], dtype=dtype)
              _check_array_roundtrip(arr, type=pa_type)
  
      def test_non_threaded_conversion(self):
          _non_threaded_conversion()
  
      def test_threaded_conversion_multiprocess(self):
          # Parallel conversion should work from child processes too (ARROW-2963)
          pool = mp.Pool(2)
          try:
              pool.apply(_threaded_conversion)
          finally:
              pool.close()
              pool.join()
  
      def test_category(self):
          repeats = 5
          v1 = ['foo', None, 'bar', 'qux', np.nan]
          v2 = [4, 5, 6, 7, 8]
          v3 = [b'foo', None, b'bar', b'qux', np.nan]
  
          arrays = {
              'cat_strings': pd.Categorical(v1 * repeats),
              'cat_strings_with_na': pd.Categorical(v1 * repeats,
                                                    categories=['foo', 'bar']),
              'cat_ints': pd.Categorical(v2 * repeats),
              'cat_binary': pd.Categorical(v3 * repeats),
              'cat_strings_ordered': pd.Categorical(
                  v1 * repeats, categories=['bar', 'qux', 'foo'],
                  ordered=True),
              'ints': v2 * repeats,
              'ints2': v2 * repeats,
              'strings': v1 * repeats,
              'strings2': v1 * repeats,
              'strings3': v3 * repeats}
          df = pd.DataFrame(arrays)
          _check_pandas_roundtrip(df)
  
          for k in arrays:
              _check_array_roundtrip(arrays[k])
  
      def test_category_implicit_from_pandas(self):
          # ARROW-3374
          def _check(v):
              arr = pa.array(v)
              result = arr.to_pandas()
              tm.assert_series_equal(pd.Series(result), pd.Series(v))
  
          arrays = [
              pd.Categorical(['a', 'b', 'c'], categories=['a', 'b']),
              pd.Categorical(['a', 'b', 'c'], categories=['a', 'b'],
                             ordered=True)
          ]
          for arr in arrays:
              _check(arr)
  
      def test_empty_category(self):
          # ARROW-2443
          df = pd.DataFrame({'cat': pd.Categorical([])})
          _check_pandas_roundtrip(df)
  
      def test_category_zero_chunks(self):
          # ARROW-5952
          for pa_type, dtype in [(pa.string(), 'object'), (pa.int64(), 'int64')]:
              a = pa.chunked_array([], pa.dictionary(pa.int8(), pa_type))
              result = a.to_pandas()
              expected = pd.Categorical([], categories=np.array([], dtype=dtype))
              tm.assert_series_equal(pd.Series(result), pd.Series(expected))
  
              table = pa.table({'a': a})
              result = table.to_pandas()
              expected = pd.DataFrame({'a': expected})
              tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize(
          "data,error_type",
          [
              ({"a": ["a", 1, 2.0]}, pa.ArrowTypeError),
              ({"a": ["a", 1, 2.0]}, pa.ArrowTypeError),
              ({"a": [1, True]}, pa.ArrowTypeError),
              ({"a": [True, "a"]}, pa.ArrowInvalid),
              ({"a": [1, "a"]}, pa.ArrowInvalid),
              ({"a": [1.0, "a"]}, pa.ArrowInvalid),
          ],
      )
      def test_mixed_types_fails(self, data, error_type):
          df = pd.DataFrame(data)
          msg = "Conversion failed for column a with type object"
          with pytest.raises(error_type, match=msg):
              pa.Table.from_pandas(df)
  
      def test_strided_data_import(self):
          cases = []
  
          columns = ['a', 'b', 'c']
          N, K = 100, 3
          random_numbers = np.random.randn(N, K).copy() * 100
  
          numeric_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8',
                            'f4', 'f8']
  
          for type_name in numeric_dtypes:
              cases.append(random_numbers.astype(type_name))
  
          # strings
          cases.append(np.array([random_ascii(10) for i in range(N * K)],
                                dtype=object)
                       .reshape(N, K).copy())
  
          # booleans
          boolean_objects = (np.array([True, False, True] * N, dtype=object)
                             .reshape(N, K).copy())
  
          # add some nulls, so dtype comes back as objects
          boolean_objects[5] = None
          cases.append(boolean_objects)
  
          cases.append(np.arange("2016-01-01T00:00:00.001", N * K,
                                 dtype='datetime64[ms]')
                       .reshape(N, K).copy())
  
          strided_mask = (random_numbers > 0).astype(bool)[:, 0]
  
          for case in cases:
              df = pd.DataFrame(case, columns=columns)
              col = df['a']
  
              _check_pandas_roundtrip(df)
              _check_array_roundtrip(col)
              _check_array_roundtrip(col, mask=strided_mask)
  
      def test_all_nones(self):
          def _check_series(s):
              converted = pa.array(s)
              assert isinstance(converted, pa.NullArray)
              assert len(converted) == 3
              assert converted.null_count == 3
              for item in converted:
                  assert item is pa.NA
  
          _check_series(pd.Series([None] * 3, dtype=object))
          _check_series(pd.Series([np.nan] * 3, dtype=object))
          _check_series(pd.Series([None, np.nan, None], dtype=object))
  
      def test_partial_schema(self):
          data = OrderedDict([
              ('a', [0, 1, 2, 3, 4]),
              ('b', np.array([-10, -5, 0, 5, 10], dtype=np.int32)),
              ('c', [-10, -5, 0, 5, 10])
          ])
          df = pd.DataFrame(data)
  
          partial_schema = pa.schema([
              pa.field('c', pa.int64()),
              pa.field('a', pa.int64())
          ])
  
          _check_pandas_roundtrip(df, schema=partial_schema,
                                  expected=df[['c', 'a']],
                                  expected_schema=partial_schema)
  
      def test_table_batch_empty_dataframe(self):
          df = pd.DataFrame({})
          _check_pandas_roundtrip(df)
          _check_pandas_roundtrip(df, as_batch=True)
  
          df2 = pd.DataFrame({}, index=[0, 1, 2])
          _check_pandas_roundtrip(df2, preserve_index=True)
          _check_pandas_roundtrip(df2, as_batch=True, preserve_index=True)
  
      def test_convert_empty_table(self):
          arr = pa.array([], type=pa.int64())
          empty_objects = pd.Series(np.array([], dtype=object))
          tm.assert_series_equal(arr.to_pandas(),
                                 pd.Series(np.array([], dtype=np.int64)))
          arr = pa.array([], type=pa.string())
          tm.assert_series_equal(arr.to_pandas(), empty_objects)
          arr = pa.array([], type=pa.list_(pa.int64()))
          tm.assert_series_equal(arr.to_pandas(), empty_objects)
          arr = pa.array([], type=pa.struct([pa.field('a', pa.int64())]))
          tm.assert_series_equal(arr.to_pandas(), empty_objects)
  
      def test_non_natural_stride(self):
          """
          ARROW-2172: converting from a Numpy array with a stride that's
          not a multiple of itemsize.
          """
          dtype = np.dtype([('x', np.int32), ('y', np.int16)])
          data = np.array([(42, -1), (-43, 2)], dtype=dtype)
          assert data.strides == (6,)
          arr = pa.array(data['x'], type=pa.int32())
          assert arr.to_pylist() == [42, -43]
          arr = pa.array(data['y'], type=pa.int16())
          assert arr.to_pylist() == [-1, 2]
  
      def test_array_from_strided_numpy_array(self):
          # ARROW-5651
          np_arr = np.arange(0, 10, dtype=np.float32)[1:-1:2]
          pa_arr = pa.array(np_arr, type=pa.float64())
          expected = pa.array([1.0, 3.0, 5.0, 7.0], type=pa.float64())
          pa_arr.equals(expected)
  
      def test_safe_unsafe_casts(self):
          # ARROW-2799
          df = pd.DataFrame({
              'A': list('abc'),
              'B': np.linspace(0, 1, 3)
          })
  
          schema = pa.schema([
              pa.field('A', pa.string()),
              pa.field('B', pa.int32())
          ])
  
          with pytest.raises(ValueError):
              pa.Table.from_pandas(df, schema=schema)
  
          table = pa.Table.from_pandas(df, schema=schema, safe=False)
          assert table.column('B').type == pa.int32()
  
      def test_error_sparse(self):
          # ARROW-2818
          try:
              df = pd.DataFrame({'a': pd.arrays.SparseArray([1, np.nan, 3])})
          except AttributeError:
              # pandas.arrays module introduced in pandas 0.24
              df = pd.DataFrame({'a': pd.SparseArray([1, np.nan, 3])})
          with pytest.raises(TypeError, match="Sparse pandas data"):
              pa.Table.from_pandas(df)
  
  
  def test_safe_cast_from_float_with_nans_to_int():
      # TODO(kszucs): write tests for creating Date32 and Date64 arrays, see
      #               ARROW-4258 and https://github.com/apache/arrow/pull/3395
      values = pd.Series([1, 2, None, 4])
      arr = pa.Array.from_pandas(values, type=pa.int32(), safe=True)
      expected = pa.array([1, 2, None, 4], type=pa.int32())
      assert arr.equals(expected)
  
  
  def _fully_loaded_dataframe_example():
      index = pd.MultiIndex.from_arrays([
          pd.date_range('2000-01-01', periods=5).repeat(2),
          np.tile(np.array(['foo', 'bar'], dtype=object), 5)
      ])
  
      c1 = pd.date_range('2000-01-01', periods=10)
      data = {
          0: c1,
          1: c1.tz_localize('utc'),
          2: c1.tz_localize('US/Eastern'),
          3: c1[::2].tz_localize('utc').repeat(2).astype('category'),
          4: ['foo', 'bar'] * 5,
          5: pd.Series(['foo', 'bar'] * 5).astype('category').values,
          6: [True, False] * 5,
          7: np.random.randn(10),
          8: np.random.randint(0, 100, size=10),
          9: pd.period_range('2013', periods=10, freq='M')
      }
  
      if LooseVersion(pd.__version__) >= '0.21':
          # There is an issue with pickling IntervalIndex in pandas 0.20.x
          data[10] = pd.interval_range(start=1, freq=1, periods=10)
  
      return pd.DataFrame(data, index=index)
  
  
  @pytest.mark.parametrize('columns', ([b'foo'], ['foo']))
  def test_roundtrip_with_bytes_unicode(columns):
      df = pd.DataFrame(columns=columns)
      table1 = pa.Table.from_pandas(df)
      table2 = pa.Table.from_pandas(table1.to_pandas())
      assert table1.equals(table2)
      assert table1.schema.equals(table2.schema)
      assert table1.schema.metadata == table2.schema.metadata
  
  
  def _check_serialize_components_roundtrip(pd_obj):
      with pytest.warns(FutureWarning):
          ctx = pa.default_serialization_context()
  
      with pytest.warns(FutureWarning):
          components = ctx.serialize(pd_obj).to_components()
      with pytest.warns(FutureWarning):
          deserialized = ctx.deserialize_components(components)
  
      if isinstance(pd_obj, pd.DataFrame):
          tm.assert_frame_equal(pd_obj, deserialized)
      else:
          tm.assert_series_equal(pd_obj, deserialized)
  
  
  @pytest.mark.skipif('1.16.0' <= LooseVersion(np.__version__) < '1.16.1',
                      reason='Until numpy/numpy#12745 is resolved')
  def test_serialize_deserialize_pandas():
      # ARROW-1784, serialize and deserialize DataFrame by decomposing
      # BlockManager
      df = _fully_loaded_dataframe_example()
      _check_serialize_components_roundtrip(df)
  
  
  def test_serialize_deserialize_empty_pandas():
      # ARROW-7996, serialize and deserialize empty pandas objects
      df = pd.DataFrame({'col1': [], 'col2': [], 'col3': []})
      _check_serialize_components_roundtrip(df)
  
      series = pd.Series([], dtype=np.float32, name='col')
      _check_serialize_components_roundtrip(series)
  
  
  def _pytime_from_micros(val):
      microseconds = val % 1000000
      val //= 1000000
      seconds = val % 60
      val //= 60
      minutes = val % 60
      hours = val // 60
      return time(hours, minutes, seconds, microseconds)
  
  
  def _pytime_to_micros(pytime):
      return (pytime.hour * 3600000000 +
              pytime.minute * 60000000 +
              pytime.second * 1000000 +
              pytime.microsecond)
  
  
  def test_convert_unsupported_type_error_message():
      # ARROW-1454
  
      # custom python objects
      class A:
          pass
  
      df = pd.DataFrame({'a': [A(), A()]})
  
      msg = 'Conversion failed for column a with type object'
      with pytest.raises(ValueError, match=msg):
          pa.Table.from_pandas(df)
  
      # period unsupported for pandas <= 0.25
      if LooseVersion(pd.__version__) <= '0.25':
          df = pd.DataFrame({
              'a': pd.period_range('2000-01-01', periods=20),
          })
  
          msg = 'Conversion failed for column a with type (period|object)'
          with pytest.raises((TypeError, ValueError), match=msg):
              pa.Table.from_pandas(df)
  
  
  # ----------------------------------------------------------------------
  # Hypothesis tests
  
  
  @h.given(past.arrays(past.pandas_compatible_types))
  def test_array_to_pandas_roundtrip(arr):
      s = arr.to_pandas()
      restored = pa.array(s, type=arr.type, from_pandas=True)
      assert restored.equals(arr)
  
  
  # ----------------------------------------------------------------------
  # Test object deduplication in to_pandas
  
  
  def _generate_dedup_example(nunique, repeats):
      unique_values = [rands(10) for i in range(nunique)]
      return unique_values * repeats
  
  
  def _assert_nunique(obj, expected):
      assert len({id(x) for x in obj}) == expected
  
  
  def test_to_pandas_deduplicate_strings_array_types():
      nunique = 100
      repeats = 10
      values = _generate_dedup_example(nunique, repeats)
  
      for arr in [pa.array(values, type=pa.binary()),
                  pa.array(values, type=pa.utf8()),
                  pa.chunked_array([values, values])]:
          _assert_nunique(arr.to_pandas(), nunique)
          _assert_nunique(arr.to_pandas(deduplicate_objects=False), len(arr))
  
  
  def test_to_pandas_deduplicate_strings_table_types():
      nunique = 100
      repeats = 10
      values = _generate_dedup_example(nunique, repeats)
  
      arr = pa.array(values)
      rb = pa.RecordBatch.from_arrays([arr], ['foo'])
      tbl = pa.Table.from_batches([rb])
  
      for obj in [rb, tbl]:
          _assert_nunique(obj.to_pandas()['foo'], nunique)
          _assert_nunique(obj.to_pandas(deduplicate_objects=False)['foo'],
                          len(obj))
  
  
  def test_to_pandas_deduplicate_integers_as_objects():
      nunique = 100
      repeats = 10
  
      # Python automatically interns smaller integers
      unique_values = list(np.random.randint(10000000, 1000000000, size=nunique))
      unique_values[nunique // 2] = None
  
      arr = pa.array(unique_values * repeats)
  
      _assert_nunique(arr.to_pandas(integer_object_nulls=True), nunique)
      _assert_nunique(arr.to_pandas(integer_object_nulls=True,
                                    deduplicate_objects=False),
                      # Account for None
                      (nunique - 1) * repeats + 1)
  
  
  def test_to_pandas_deduplicate_date_time():
      nunique = 100
      repeats = 10
  
      unique_values = list(range(nunique))
  
      cases = [
          # raw type, array type, to_pandas options
          ('int32', 'date32', {'date_as_object': True}),
          ('int64', 'date64', {'date_as_object': True}),
          ('int32', 'time32[ms]', {}),
          ('int64', 'time64[us]', {})
      ]
  
      for raw_type, array_type, pandas_options in cases:
          raw_arr = pa.array(unique_values * repeats, type=raw_type)
          casted_arr = raw_arr.cast(array_type)
  
          _assert_nunique(casted_arr.to_pandas(**pandas_options),
                          nunique)
          _assert_nunique(casted_arr.to_pandas(deduplicate_objects=False,
                                               **pandas_options),
                          len(casted_arr))
  
  
  # ---------------------------------------------------------------------
  
  def test_table_from_pandas_checks_field_nullability():
      # ARROW-2136
      df = pd.DataFrame({'a': [1.2, 2.1, 3.1],
                         'b': [np.nan, 'string', 'foo']})
      schema = pa.schema([pa.field('a', pa.float64(), nullable=False),
                          pa.field('b', pa.utf8(), nullable=False)])
  
      with pytest.raises(ValueError):
          pa.Table.from_pandas(df, schema=schema)
  
  
  def test_table_from_pandas_keeps_column_order_of_dataframe():
      df1 = pd.DataFrame(OrderedDict([
          ('partition', [0, 0, 1, 1]),
          ('arrays', [[0, 1, 2], [3, 4], None, None]),
          ('floats', [None, None, 1.1, 3.3])
      ]))
      df2 = df1[['floats', 'partition', 'arrays']]
  
      schema1 = pa.schema([
          ('partition', pa.int64()),
          ('arrays', pa.list_(pa.int64())),
          ('floats', pa.float64()),
      ])
      schema2 = pa.schema([
          ('floats', pa.float64()),
          ('partition', pa.int64()),
          ('arrays', pa.list_(pa.int64()))
      ])
  
      table1 = pa.Table.from_pandas(df1, preserve_index=False)
      table2 = pa.Table.from_pandas(df2, preserve_index=False)
  
      assert table1.schema.equals(schema1)
      assert table2.schema.equals(schema2)
  
  
  def test_table_from_pandas_keeps_column_order_of_schema():
      # ARROW-3766
      df = pd.DataFrame(OrderedDict([
          ('partition', [0, 0, 1, 1]),
          ('arrays', [[0, 1, 2], [3, 4], None, None]),
          ('floats', [None, None, 1.1, 3.3])
      ]))
  
      schema = pa.schema([
          ('floats', pa.float64()),
          ('arrays', pa.list_(pa.int32())),
          ('partition', pa.int32())
      ])
  
      df1 = df[df.partition == 0]
      df2 = df[df.partition == 1][['floats', 'partition', 'arrays']]
  
      table1 = pa.Table.from_pandas(df1, schema=schema, preserve_index=False)
      table2 = pa.Table.from_pandas(df2, schema=schema, preserve_index=False)
  
      assert table1.schema.equals(schema)
      assert table1.schema.equals(table2.schema)
  
  
  def test_table_from_pandas_columns_argument_only_does_filtering():
      df = pd.DataFrame(OrderedDict([
          ('partition', [0, 0, 1, 1]),
          ('arrays', [[0, 1, 2], [3, 4], None, None]),
          ('floats', [None, None, 1.1, 3.3])
      ]))
  
      columns1 = ['arrays', 'floats', 'partition']
      schema1 = pa.schema([
          ('arrays', pa.list_(pa.int64())),
          ('floats', pa.float64()),
          ('partition', pa.int64())
      ])
  
      columns2 = ['floats', 'partition']
      schema2 = pa.schema([
          ('floats', pa.float64()),
          ('partition', pa.int64())
      ])
  
      table1 = pa.Table.from_pandas(df, columns=columns1, preserve_index=False)
      table2 = pa.Table.from_pandas(df, columns=columns2, preserve_index=False)
  
      assert table1.schema.equals(schema1)
      assert table2.schema.equals(schema2)
  
  
  def test_table_from_pandas_columns_and_schema_are_mutually_exclusive():
      df = pd.DataFrame(OrderedDict([
          ('partition', [0, 0, 1, 1]),
          ('arrays', [[0, 1, 2], [3, 4], None, None]),
          ('floats', [None, None, 1.1, 3.3])
      ]))
      schema = pa.schema([
          ('partition', pa.int32()),
          ('arrays', pa.list_(pa.int32())),
          ('floats', pa.float64()),
      ])
      columns = ['arrays', 'floats']
  
      with pytest.raises(ValueError):
          pa.Table.from_pandas(df, schema=schema, columns=columns)
  
  
  def test_table_from_pandas_keeps_schema_nullability():
      # ARROW-5169
      df = pd.DataFrame({'a': [1, 2, 3, 4]})
  
      schema = pa.schema([
          pa.field('a', pa.int64(), nullable=False),
      ])
  
      table = pa.Table.from_pandas(df)
      assert table.schema.field('a').nullable is True
      table = pa.Table.from_pandas(df, schema=schema)
      assert table.schema.field('a').nullable is False
  
  
  def test_table_from_pandas_schema_index_columns():
      # ARROW-5220
      df = pd.DataFrame({'a': [1, 2, 3], 'b': [0.1, 0.2, 0.3]})
  
      schema = pa.schema([
          ('a', pa.int64()),
          ('b', pa.float64()),
          ('index', pa.int32()),
      ])
  
      # schema includes index with name not in dataframe
      with pytest.raises(KeyError, match="name 'index' present in the"):
          pa.Table.from_pandas(df, schema=schema)
  
      df.index.name = 'index'
  
      # schema includes correct index name -> roundtrip works
      _check_pandas_roundtrip(df, schema=schema, preserve_index=True,
                              expected_schema=schema)
  
      # schema includes correct index name but preserve_index=False
      with pytest.raises(ValueError, match="'preserve_index=False' was"):
          pa.Table.from_pandas(df, schema=schema, preserve_index=False)
  
      # in case of preserve_index=None -> RangeIndex serialized as metadata
      # clashes with the index in the schema
      with pytest.raises(ValueError, match="name 'index' is present in the "
                                           "schema, but it is a RangeIndex"):
          pa.Table.from_pandas(df, schema=schema, preserve_index=None)
  
      df.index = pd.Index([0, 1, 2], name='index')
  
      # for non-RangeIndex, both preserve_index=None and True work
      _check_pandas_roundtrip(df, schema=schema, preserve_index=None,
                              expected_schema=schema)
      _check_pandas_roundtrip(df, schema=schema, preserve_index=True,
                              expected_schema=schema)
  
      # schema has different order (index column not at the end)
      schema = pa.schema([
          ('index', pa.int32()),
          ('a', pa.int64()),
          ('b', pa.float64()),
      ])
      _check_pandas_roundtrip(df, schema=schema, preserve_index=None,
                              expected_schema=schema)
      _check_pandas_roundtrip(df, schema=schema, preserve_index=True,
                              expected_schema=schema)
  
      # schema does not include the index -> index is not included as column
      # even though preserve_index=True/None
      schema = pa.schema([
          ('a', pa.int64()),
          ('b', pa.float64()),
      ])
      expected = df.copy()
      expected = expected.reset_index(drop=True)
      _check_pandas_roundtrip(df, schema=schema, preserve_index=None,
                              expected_schema=schema, expected=expected)
      _check_pandas_roundtrip(df, schema=schema, preserve_index=True,
                              expected_schema=schema, expected=expected)
  
      # dataframe with a MultiIndex
      df.index = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
                                           names=['level1', 'level2'])
      schema = pa.schema([
          ('level1', pa.string()),
          ('level2', pa.int64()),
          ('a', pa.int64()),
          ('b', pa.float64()),
      ])
      _check_pandas_roundtrip(df, schema=schema, preserve_index=True,
                              expected_schema=schema)
      _check_pandas_roundtrip(df, schema=schema, preserve_index=None,
                              expected_schema=schema)
  
      # only one of the levels of the MultiIndex is included
      schema = pa.schema([
          ('level2', pa.int64()),
          ('a', pa.int64()),
          ('b', pa.float64()),
      ])
      expected = df.copy()
      expected = expected.reset_index('level1', drop=True)
      _check_pandas_roundtrip(df, schema=schema, preserve_index=True,
                              expected_schema=schema, expected=expected)
      _check_pandas_roundtrip(df, schema=schema, preserve_index=None,
                              expected_schema=schema, expected=expected)
  
  
  def test_table_from_pandas_schema_index_columns__unnamed_index():
      # ARROW-6999 - unnamed indices in specified schema
      df = pd.DataFrame({'a': [1, 2, 3], 'b': [0.1, 0.2, 0.3]})
  
      expected_schema = pa.schema([
          ('a', pa.int64()),
          ('b', pa.float64()),
          ('__index_level_0__', pa.int64()),
      ])
  
      schema = pa.Schema.from_pandas(df, preserve_index=True)
      table = pa.Table.from_pandas(df, preserve_index=True, schema=schema)
      assert table.schema.remove_metadata().equals(expected_schema)
  
      # non-RangeIndex (preserved by default)
      df = pd.DataFrame({'a': [1, 2, 3], 'b': [0.1, 0.2, 0.3]}, index=[0, 1, 2])
      schema = pa.Schema.from_pandas(df)
      table = pa.Table.from_pandas(df, schema=schema)
      assert table.schema.remove_metadata().equals(expected_schema)
  
  
  def test_table_from_pandas_schema_with_custom_metadata():
      # ARROW-7087 - metadata disappear from pandas
      df = pd.DataFrame()
      schema = pa.Schema.from_pandas(df).with_metadata({'meta': 'True'})
      table = pa.Table.from_pandas(df, schema=schema)
      assert table.schema.metadata.get(b'meta') == b'True'
  
  
  def test_table_from_pandas_schema_field_order_metadat():
      # ARROW-10532
      # ensure that a different field order in specified schema doesn't
      # mangle metadata
      df = pd.DataFrame({
          "datetime": pd.date_range("2020-01-01T00:00:00Z", freq="H", periods=2),
          "float": np.random.randn(2)
      })
  
      schema = pa.schema([
          pa.field("float", pa.float32(), nullable=True),
          pa.field("datetime", pa.timestamp("s", tz="UTC"), nullable=False)
      ])
  
      table = pa.Table.from_pandas(df, schema=schema)
      assert table.schema.equals(schema)
      metadata_float = table.schema.pandas_metadata["columns"][0]
      assert metadata_float["name"] == "float"
      assert metadata_float["metadata"] is None
      metadata_datetime = table.schema.pandas_metadata["columns"][1]
      assert metadata_datetime["name"] == "datetime"
      assert metadata_datetime["metadata"] == {'timezone': 'UTC'}
  
      result = table.to_pandas()
      expected = df[["float", "datetime"]].astype({"float": "float32"})
      tm.assert_frame_equal(result, expected)
  
  
  # ----------------------------------------------------------------------
  # RecordBatch, Table
  
  
  def test_recordbatch_from_to_pandas():
      data = pd.DataFrame({
          'c1': np.array([1, 2, 3, 4, 5], dtype='int64'),
          'c2': np.array([1, 2, 3, 4, 5], dtype='uint32'),
          'c3': np.random.randn(5),
          'c4': ['foo', 'bar', None, 'baz', 'qux'],
          'c5': [False, True, False, True, False]
      })
  
      batch = pa.RecordBatch.from_pandas(data)
      result = batch.to_pandas()
      tm.assert_frame_equal(data, result)
  
  
  def test_recordbatchlist_to_pandas():
      data1 = pd.DataFrame({
          'c1': np.array([1, 1, 2], dtype='uint32'),
          'c2': np.array([1.0, 2.0, 3.0], dtype='float64'),
          'c3': [True, None, False],
          'c4': ['foo', 'bar', None]
      })
  
      data2 = pd.DataFrame({
          'c1': np.array([3, 5], dtype='uint32'),
          'c2': np.array([4.0, 5.0], dtype='float64'),
          'c3': [True, True],
          'c4': ['baz', 'qux']
      })
  
      batch1 = pa.RecordBatch.from_pandas(data1)
      batch2 = pa.RecordBatch.from_pandas(data2)
  
      table = pa.Table.from_batches([batch1, batch2])
      result = table.to_pandas()
      data = pd.concat([data1, data2]).reset_index(drop=True)
      tm.assert_frame_equal(data, result)
  
  
  def test_recordbatch_table_pass_name_to_pandas():
      rb = pa.record_batch([pa.array([1, 2, 3, 4])], names=['a0'])
      t = pa.table([pa.array([1, 2, 3, 4])], names=['a0'])
      assert rb[0].to_pandas().name == 'a0'
      assert t[0].to_pandas().name == 'a0'
  
  
  # ----------------------------------------------------------------------
  # Metadata serialization
  
  
  @pytest.mark.parametrize(
      ('type', 'expected'),
      [
          (pa.null(), 'empty'),
          (pa.bool_(), 'bool'),
          (pa.int8(), 'int8'),
          (pa.int16(), 'int16'),
          (pa.int32(), 'int32'),
          (pa.int64(), 'int64'),
          (pa.uint8(), 'uint8'),
          (pa.uint16(), 'uint16'),
          (pa.uint32(), 'uint32'),
          (pa.uint64(), 'uint64'),
          (pa.float16(), 'float16'),
          (pa.float32(), 'float32'),
          (pa.float64(), 'float64'),
          (pa.date32(), 'date'),
          (pa.date64(), 'date'),
          (pa.binary(), 'bytes'),
          (pa.binary(length=4), 'bytes'),
          (pa.string(), 'unicode'),
          (pa.list_(pa.list_(pa.int16())), 'list[list[int16]]'),
          (pa.decimal128(18, 3), 'decimal'),
          (pa.timestamp('ms'), 'datetime'),
          (pa.timestamp('us', 'UTC'), 'datetimetz'),
          (pa.time32('s'), 'time'),
          (pa.time64('us'), 'time')
      ]
  )
  def test_logical_type(type, expected):
      assert get_logical_type(type) == expected
  
  
  # ----------------------------------------------------------------------
  # to_pandas uses MemoryPool
  
  def test_array_uses_memory_pool():
      # ARROW-6570
      N = 10000
      arr = pa.array(np.arange(N, dtype=np.int64),
                     mask=np.random.randint(0, 2, size=N).astype(np.bool_))
  
      # In the case the gc is caught loafing
      gc.collect()
  
      prior_allocation = pa.total_allocated_bytes()
  
      x = arr.to_pandas()
      assert pa.total_allocated_bytes() == (prior_allocation + N * 8)
      x = None  # noqa
      gc.collect()
  
      assert pa.total_allocated_bytes() == prior_allocation
  
      # zero copy does not allocate memory
      arr = pa.array(np.arange(N, dtype=np.int64))
  
      prior_allocation = pa.total_allocated_bytes()
      x = arr.to_pandas()  # noqa
      assert pa.total_allocated_bytes() == prior_allocation
  
  
  def test_singleton_blocks_zero_copy():
      # Part of ARROW-3789
      t = pa.table([pa.array(np.arange(1000, dtype=np.int64))], ['f0'])
  
      # Zero copy if split_blocks=True
      _check_to_pandas_memory_unchanged(t, split_blocks=True)
  
      prior_allocation = pa.total_allocated_bytes()
      result = t.to_pandas()
      assert result['f0'].values.flags.writeable
      assert pa.total_allocated_bytes() > prior_allocation
  
  
  def _check_to_pandas_memory_unchanged(obj, **kwargs):
      prior_allocation = pa.total_allocated_bytes()
      x = obj.to_pandas(**kwargs)  # noqa
  
      # Memory allocation unchanged -- either zero copy or self-destructing
      assert pa.total_allocated_bytes() == prior_allocation
  
  
  def test_to_pandas_split_blocks():
      # ARROW-3789
      t = pa.table([
          pa.array([1, 2, 3, 4, 5], type='i1'),
          pa.array([1, 2, 3, 4, 5], type='i4'),
          pa.array([1, 2, 3, 4, 5], type='i8'),
          pa.array([1, 2, 3, 4, 5], type='f4'),
          pa.array([1, 2, 3, 4, 5], type='f8'),
          pa.array([1, 2, 3, 4, 5], type='f8'),
          pa.array([1, 2, 3, 4, 5], type='f8'),
          pa.array([1, 2, 3, 4, 5], type='f8'),
      ], ['f{}'.format(i) for i in range(8)])
  
      _check_blocks_created(t, 8)
      _check_to_pandas_memory_unchanged(t, split_blocks=True)
  
  
  def _check_blocks_created(t, number):
      x = t.to_pandas(split_blocks=True)
      assert len(x._data.blocks) == number
  
  
  def test_to_pandas_self_destruct():
      K = 50
  
      def _make_table():
          return pa.table([
              # Slice to force a copy
              pa.array(np.random.randn(10000)[::2])
              for i in range(K)
          ], ['f{}'.format(i) for i in range(K)])
  
      t = _make_table()
      _check_to_pandas_memory_unchanged(t, split_blocks=True, self_destruct=True)
  
      # Check non-split-block behavior
      t = _make_table()
      _check_to_pandas_memory_unchanged(t, self_destruct=True)
  
  
  def test_table_uses_memory_pool():
      N = 10000
      arr = pa.array(np.arange(N, dtype=np.int64))
      t = pa.table([arr, arr, arr], ['f0', 'f1', 'f2'])
  
      prior_allocation = pa.total_allocated_bytes()
      x = t.to_pandas()
  
      assert pa.total_allocated_bytes() == (prior_allocation + 3 * N * 8)
  
      # Check successful garbage collection
      x = None  # noqa
      gc.collect()
      assert pa.total_allocated_bytes() == prior_allocation
  
  
  def test_object_leak_in_numpy_array():
      # ARROW-6876
      arr = pa.array([{'a': 1}])
      np_arr = arr.to_pandas()
      assert np_arr.dtype == np.dtype('object')
      obj = np_arr[0]
      refcount = sys.getrefcount(obj)
      assert sys.getrefcount(obj) == refcount
      del np_arr
      assert sys.getrefcount(obj) == refcount - 1
  
  
  def test_object_leak_in_dataframe():
      # ARROW-6876
      arr = pa.array([{'a': 1}])
      table = pa.table([arr], ['f0'])
      col = table.to_pandas()['f0']
      assert col.dtype == np.dtype('object')
      obj = col[0]
      refcount = sys.getrefcount(obj)
      assert sys.getrefcount(obj) == refcount
      del col
      assert sys.getrefcount(obj) == refcount - 1
  
  
  # ----------------------------------------------------------------------
  # Some nested array tests array tests
  
  
  def test_array_from_py_float32():
      data = [[1.2, 3.4], [9.0, 42.0]]
  
      t = pa.float32()
  
      arr1 = pa.array(data[0], type=t)
      arr2 = pa.array(data, type=pa.list_(t))
  
      expected1 = np.array(data[0], dtype=np.float32)
      expected2 = pd.Series([np.array(data[0], dtype=np.float32),
                             np.array(data[1], dtype=np.float32)])
  
      assert arr1.type == t
      assert arr1.equals(pa.array(expected1))
      assert arr2.equals(pa.array(expected2))
  
  
  # ----------------------------------------------------------------------
  # Timestamp tests
  
  
  def test_cast_timestamp_unit():
      # ARROW-1680
      val = datetime.now()
      s = pd.Series([val])
      s_nyc = s.dt.tz_localize('tzlocal()').dt.tz_convert('America/New_York')
  
      us_with_tz = pa.timestamp('us', tz='America/New_York')
  
      arr = pa.Array.from_pandas(s_nyc, type=us_with_tz)
  
      # ARROW-1906
      assert arr.type == us_with_tz
  
      arr2 = pa.Array.from_pandas(s, type=pa.timestamp('us'))
  
      assert arr[0].as_py() == s_nyc[0].to_pydatetime()
      assert arr2[0].as_py() == s[0].to_pydatetime()
  
      # Disallow truncation
      arr = pa.array([123123], type='int64').cast(pa.timestamp('ms'))
      expected = pa.array([123], type='int64').cast(pa.timestamp('s'))
  
      # sanity check that the cast worked right
      assert arr.type == pa.timestamp('ms')
  
      target = pa.timestamp('s')
      with pytest.raises(ValueError):
          arr.cast(target)
  
      result = arr.cast(target, safe=False)
      assert result.equals(expected)
  
      # ARROW-1949
      series = pd.Series([pd.Timestamp(1), pd.Timestamp(10), pd.Timestamp(1000)])
      expected = pa.array([0, 0, 1], type=pa.timestamp('us'))
  
      with pytest.raises(ValueError):
          pa.array(series, type=pa.timestamp('us'))
  
      with pytest.raises(ValueError):
          pa.Array.from_pandas(series, type=pa.timestamp('us'))
  
      result = pa.Array.from_pandas(series, type=pa.timestamp('us'), safe=False)
      assert result.equals(expected)
  
      result = pa.array(series, type=pa.timestamp('us'), safe=False)
      assert result.equals(expected)
  
  
  def test_nested_with_timestamp_tz_round_trip():
      ts = pd.Timestamp.now()
      ts_dt = ts.to_pydatetime()
      arr = pa.array([ts_dt], type=pa.timestamp('us', tz='America/New_York'))
      struct = pa.StructArray.from_arrays([arr, arr], ['start', 'stop'])
  
      result = struct.to_pandas()
      restored = pa.array(result)
      assert restored.equals(struct)
  
  
  def test_nested_with_timestamp_tz():
      # ARROW-7723
      ts = pd.Timestamp.now()
      ts_dt = ts.to_pydatetime()
  
      # XXX: Ensure that this data does not get promoted to nanoseconds (and thus
      # integers) to preserve behavior in 0.15.1
      for unit in ['s', 'ms', 'us']:
          if unit in ['s', 'ms']:
              # This is used for verifying timezone conversion to micros are not
              # important
              def truncate(x): return x.replace(microsecond=0)
          else:
              def truncate(x): return x
          arr = pa.array([ts], type=pa.timestamp(unit))
          arr2 = pa.array([ts], type=pa.timestamp(unit, tz='America/New_York'))
  
          arr3 = pa.StructArray.from_arrays([arr, arr], ['start', 'stop'])
          arr4 = pa.StructArray.from_arrays([arr2, arr2], ['start', 'stop'])
  
          result = arr3.to_pandas()
          assert isinstance(result[0]['start'], datetime)
          assert result[0]['start'].tzinfo is None
          assert isinstance(result[0]['stop'], datetime)
          assert result[0]['stop'].tzinfo is None
  
          result = arr4.to_pandas()
          assert isinstance(result[0]['start'], datetime)
          assert result[0]['start'].tzinfo is not None
          utc_dt = result[0]['start'].astimezone(timezone.utc)
          assert truncate(utc_dt).replace(tzinfo=None) == truncate(ts_dt)
          assert isinstance(result[0]['stop'], datetime)
          assert result[0]['stop'].tzinfo is not None
  
          # same conversion for table
          result = pa.table({'a': arr3}).to_pandas()
          assert isinstance(result['a'][0]['start'], datetime)
          assert result['a'][0]['start'].tzinfo is None
          assert isinstance(result['a'][0]['stop'], datetime)
          assert result['a'][0]['stop'].tzinfo is None
  
          result = pa.table({'a': arr4}).to_pandas()
          assert isinstance(result['a'][0]['start'], datetime)
          assert result['a'][0]['start'].tzinfo is not None
          assert isinstance(result['a'][0]['stop'], datetime)
          assert result['a'][0]['stop'].tzinfo is not None
  
  
  # ----------------------------------------------------------------------
  # DictionaryArray tests
  
  
  def test_dictionary_with_pandas():
      src_indices = np.repeat([0, 1, 2], 2)
      dictionary = np.array(['foo', 'bar', 'baz'], dtype=object)
      mask = np.array([False, False, True, False, False, False])
  
      for index_type in ['uint8', 'int8', 'uint16', 'int16', 'uint32', 'int32',
                         'uint64', 'int64']:
          indices = src_indices.astype(index_type)
          d1 = pa.DictionaryArray.from_arrays(indices, dictionary)
          d2 = pa.DictionaryArray.from_arrays(indices, dictionary, mask=mask)
  
          if index_type[0] == 'u':
              # TODO: unsigned dictionary indices to pandas
              with pytest.raises(TypeError):
                  d1.to_pandas()
              continue
  
          pandas1 = d1.to_pandas()
          ex_pandas1 = pd.Categorical.from_codes(indices, categories=dictionary)
  
          tm.assert_series_equal(pd.Series(pandas1), pd.Series(ex_pandas1))
  
          pandas2 = d2.to_pandas()
          assert pandas2.isnull().sum() == 1
  
          # Unsigned integers converted to signed
          signed_indices = indices
          if index_type[0] == 'u':
              signed_indices = indices.astype(index_type[1:])
          ex_pandas2 = pd.Categorical.from_codes(np.where(mask, -1,
                                                          signed_indices),
                                                 categories=dictionary)
  
          tm.assert_series_equal(pd.Series(pandas2), pd.Series(ex_pandas2))
  
  
  def random_strings(n, item_size, pct_null=0, dictionary=None):
      if dictionary is not None:
          result = dictionary[np.random.randint(0, len(dictionary), size=n)]
      else:
          result = np.array([random_ascii(item_size) for i in range(n)],
                            dtype=object)
  
      if pct_null > 0:
          result[np.random.rand(n) < pct_null] = None
  
      return result
  
  
  def test_variable_dictionary_to_pandas():
      np.random.seed(12345)
  
      d1 = pa.array(random_strings(100, 32), type='string')
      d2 = pa.array(random_strings(100, 16), type='string')
      d3 = pa.array(random_strings(10000, 10), type='string')
  
      a1 = pa.DictionaryArray.from_arrays(
          np.random.randint(0, len(d1), size=1000, dtype='i4'),
          d1
      )
      a2 = pa.DictionaryArray.from_arrays(
          np.random.randint(0, len(d2), size=1000, dtype='i4'),
          d2
      )
  
      # With some nulls
      a3 = pa.DictionaryArray.from_arrays(
          np.random.randint(0, len(d3), size=1000, dtype='i4'), d3)
  
      i4 = pa.array(
          np.random.randint(0, len(d3), size=1000, dtype='i4'),
          mask=np.random.rand(1000) < 0.1
      )
      a4 = pa.DictionaryArray.from_arrays(i4, d3)
  
      expected_dict = pa.concat_arrays([d1, d2, d3])
  
      a = pa.chunked_array([a1, a2, a3, a4])
      a_dense = pa.chunked_array([a1.cast('string'),
                                  a2.cast('string'),
                                  a3.cast('string'),
                                  a4.cast('string')])
  
      result = a.to_pandas()
      result_dense = a_dense.to_pandas()
  
      assert (result.cat.categories == expected_dict.to_pandas()).all()
  
      expected_dense = result.astype('str')
      expected_dense[result_dense.isnull()] = None
      tm.assert_series_equal(result_dense, expected_dense)
  
  
  def test_dictionary_encoded_nested_to_pandas():
      # ARROW-6899
      child = pa.array(['a', 'a', 'a', 'b', 'b']).dictionary_encode()
  
      arr = pa.ListArray.from_arrays([0, 3, 5], child)
  
      result = arr.to_pandas()
      expected = pd.Series([np.array(['a', 'a', 'a'], dtype=object),
                            np.array(['b', 'b'], dtype=object)])
  
      tm.assert_series_equal(result, expected)
  
  
  def test_dictionary_from_pandas():
      cat = pd.Categorical(['a', 'b', 'a'])
      expected_type = pa.dictionary(pa.int8(), pa.string())
  
      result = pa.array(cat)
      assert result.to_pylist() == ['a', 'b', 'a']
      assert result.type.equals(expected_type)
  
      # with missing values in categorical
      cat = pd.Categorical(['a', 'b', None, 'a'])
  
      result = pa.array(cat)
      assert result.to_pylist() == ['a', 'b', None, 'a']
      assert result.type.equals(expected_type)
  
      # with additional mask
      result = pa.array(cat, mask=np.array([False, False, False, True]))
      assert result.to_pylist() == ['a', 'b', None, None]
      assert result.type.equals(expected_type)
  
  
  def test_dictionary_from_pandas_specified_type():
      # ARROW-7168 - ensure specified type is always respected
  
      # the same as cat = pd.Categorical(['a', 'b']) but explicit about dtypes
      cat = pd.Categorical.from_codes(
          np.array([0, 1], dtype='int8'), np.array(['a', 'b'], dtype=object))
  
      # different index type -> allow this
      # (the type of the 'codes' in pandas is not part of the data type)
      typ = pa.dictionary(index_type=pa.int16(), value_type=pa.string())
      result = pa.array(cat, type=typ)
      assert result.type.equals(typ)
      assert result.to_pylist() == ['a', 'b']
  
      # mismatching values type -> raise error
      typ = pa.dictionary(index_type=pa.int8(), value_type=pa.int64())
      with pytest.raises(pa.ArrowInvalid):
          result = pa.array(cat, type=typ)
  
      # mismatching order -> raise error (for now a deprecation warning)
      typ = pa.dictionary(
          index_type=pa.int8(), value_type=pa.string(), ordered=True)
      with pytest.warns(FutureWarning, match="The 'ordered' flag of the passed"):
          result = pa.array(cat, type=typ)
      assert result.to_pylist() == ['a', 'b']
  
      # with mask
      typ = pa.dictionary(index_type=pa.int16(), value_type=pa.string())
      result = pa.array(cat, type=typ, mask=np.array([False, True]))
      assert result.type.equals(typ)
      assert result.to_pylist() == ['a', None]
  
      # empty categorical -> be flexible in values type to allow
      cat = pd.Categorical([])
  
      typ = pa.dictionary(index_type=pa.int8(), value_type=pa.string())
      result = pa.array(cat, type=typ)
      assert result.type.equals(typ)
      assert result.to_pylist() == []
      typ = pa.dictionary(index_type=pa.int8(), value_type=pa.int64())
      result = pa.array(cat, type=typ)
      assert result.type.equals(typ)
      assert result.to_pylist() == []
  
      # passing non-dictionary type
      cat = pd.Categorical(['a', 'b'])
      result = pa.array(cat, type=pa.string())
      expected = pa.array(['a', 'b'], type=pa.string())
      assert result.equals(expected)
      assert result.to_pylist() == ['a', 'b']
  
  
  # ----------------------------------------------------------------------
  # Array protocol in pandas conversions tests
  
  
  def test_array_protocol():
      if LooseVersion(pd.__version__) < '0.24.0':
          pytest.skip('IntegerArray only introduced in 0.24')
  
      df = pd.DataFrame({'a': pd.Series([1, 2, None], dtype='Int64')})
  
      if LooseVersion(pd.__version__) < '0.26.0.dev':
          # with pandas<=0.25, trying to convert nullable integer errors
          with pytest.raises(TypeError):
              pa.table(df)
      else:
          # __arrow_array__ added to pandas IntegerArray in 0.26.0.dev
  
          # default conversion
          result = pa.table(df)
          expected = pa.array([1, 2, None], pa.int64())
          assert result[0].chunk(0).equals(expected)
  
          # with specifying schema
          schema = pa.schema([('a', pa.float64())])
          result = pa.table(df, schema=schema)
          expected2 = pa.array([1, 2, None], pa.float64())
          assert result[0].chunk(0).equals(expected2)
  
          # pass Series to pa.array
          result = pa.array(df['a'])
          assert result.equals(expected)
          result = pa.array(df['a'], type=pa.float64())
          assert result.equals(expected2)
  
          # pass actual ExtensionArray to pa.array
          result = pa.array(df['a'].values)
          assert result.equals(expected)
          result = pa.array(df['a'].values, type=pa.float64())
          assert result.equals(expected2)
  
  
  class DummyExtensionType(pa.PyExtensionType):
  
      def __init__(self):
          pa.PyExtensionType.__init__(self, pa.int64())
  
      def __reduce__(self):
          return DummyExtensionType, ()
  
  
  def PandasArray__arrow_array__(self, type=None):
      # hardcode dummy return regardless of self - we only want to check that
      # this method is correctly called
      storage = pa.array([1, 2, 3], type=pa.int64())
      return pa.ExtensionArray.from_storage(DummyExtensionType(), storage)
  
  
  def test_array_protocol_pandas_extension_types(monkeypatch):
      # ARROW-7022 - ensure protocol works for Period / Interval extension dtypes
  
      if LooseVersion(pd.__version__) < '0.24.0':
          pytest.skip('Period/IntervalArray only introduced in 0.24')
  
      storage = pa.array([1, 2, 3], type=pa.int64())
      expected = pa.ExtensionArray.from_storage(DummyExtensionType(), storage)
  
      monkeypatch.setattr(pd.arrays.PeriodArray, "__arrow_array__",
                          PandasArray__arrow_array__, raising=False)
      monkeypatch.setattr(pd.arrays.IntervalArray, "__arrow_array__",
                          PandasArray__arrow_array__, raising=False)
      for arr in [pd.period_range("2012-01-01", periods=3, freq="D").array,
                  pd.interval_range(1, 4).array]:
          result = pa.array(arr)
          assert result.equals(expected)
          result = pa.array(pd.Series(arr))
          assert result.equals(expected)
          result = pa.array(pd.Index(arr))
          assert result.equals(expected)
          result = pa.table(pd.DataFrame({'a': arr})).column('a').chunk(0)
          assert result.equals(expected)
  
  
  # ----------------------------------------------------------------------
  # Pandas ExtensionArray support
  
  
  def _Int64Dtype__from_arrow__(self, array):
      # for test only deal with single chunk for now
      # TODO: do we require handling of chunked arrays in the protocol?
      if isinstance(array, pa.Array):
          arr = array
      else:
          # ChunkedArray - here only deal with a single chunk for the test
          arr = array.chunk(0)
      buflist = arr.buffers()
      data = np.frombuffer(buflist[-1], dtype='int64')[
          arr.offset:arr.offset + len(arr)]
      bitmask = buflist[0]
      if bitmask is not None:
          mask = pa.BooleanArray.from_buffers(
              pa.bool_(), len(arr), [None, bitmask])
          mask = np.asarray(mask)
      else:
          mask = np.ones(len(arr), dtype=bool)
      int_arr = pd.arrays.IntegerArray(data.copy(), ~mask, copy=False)
      return int_arr
  
  
  def test_convert_to_extension_array(monkeypatch):
      if LooseVersion(pd.__version__) < "0.26.0.dev":
          pytest.skip("Conversion from IntegerArray to arrow not yet supported")
  
      import pandas.core.internals as _int
  
      # table converted from dataframe with extension types (so pandas_metadata
      # has this information)
      df = pd.DataFrame(
          {'a': [1, 2, 3], 'b': pd.array([2, 3, 4], dtype='Int64'),
           'c': [4, 5, 6]})
      table = pa.table(df)
  
      # Int64Dtype is recognized -> convert to extension block by default
      # for a proper roundtrip
      result = table.to_pandas()
      assert not isinstance(result._data.blocks[0], _int.ExtensionBlock)
      assert result._data.blocks[0].values.dtype == np.dtype("int64")
      assert isinstance(result._data.blocks[1], _int.ExtensionBlock)
      tm.assert_frame_equal(result, df)
  
      # test with missing values
      df2 = pd.DataFrame({'a': pd.array([1, 2, None], dtype='Int64')})
      table2 = pa.table(df2)
      result = table2.to_pandas()
      assert isinstance(result._data.blocks[0], _int.ExtensionBlock)
      tm.assert_frame_equal(result, df2)
  
      # monkeypatch pandas Int64Dtype to *not* have the protocol method
      if LooseVersion(pd.__version__) < "1.3.0.dev":
          monkeypatch.delattr(
              pd.core.arrays.integer._IntegerDtype, "__from_arrow__")
      else:
          monkeypatch.delattr(
              pd.core.arrays.integer.NumericDtype, "__from_arrow__")
      # Int64Dtype has no __from_arrow__ -> use normal conversion
      result = table.to_pandas()
      assert len(result._data.blocks) == 1
      assert not isinstance(result._data.blocks[0], _int.ExtensionBlock)
  
  
  class MyCustomIntegerType(pa.PyExtensionType):
  
      def __init__(self):
          pa.PyExtensionType.__init__(self, pa.int64())
  
      def __reduce__(self):
          return MyCustomIntegerType, ()
  
      def to_pandas_dtype(self):
          return pd.Int64Dtype()
  
  
  def test_conversion_extensiontype_to_extensionarray(monkeypatch):
      # converting extension type to linked pandas ExtensionDtype/Array
      import pandas.core.internals as _int
  
      if LooseVersion(pd.__version__) < "0.24.0":
          pytest.skip("ExtensionDtype introduced in pandas 0.24")
  
      storage = pa.array([1, 2, 3, 4], pa.int64())
      arr = pa.ExtensionArray.from_storage(MyCustomIntegerType(), storage)
      table = pa.table({'a': arr})
  
      if LooseVersion(pd.__version__) < "0.26.0.dev":
          # ensure pandas Int64Dtype has the protocol method (for older pandas)
          monkeypatch.setattr(
              pd.Int64Dtype, '__from_arrow__', _Int64Dtype__from_arrow__,
              raising=False)
  
      # extension type points to Int64Dtype, which knows how to create a
      # pandas ExtensionArray
      result = arr.to_pandas()
      assert isinstance(result._data.blocks[0], _int.ExtensionBlock)
      expected = pd.Series([1, 2, 3, 4], dtype='Int64')
      tm.assert_series_equal(result, expected)
  
      result = table.to_pandas()
      assert isinstance(result._data.blocks[0], _int.ExtensionBlock)
      expected = pd.DataFrame({'a': pd.array([1, 2, 3, 4], dtype='Int64')})
      tm.assert_frame_equal(result, expected)
  
      # monkeypatch pandas Int64Dtype to *not* have the protocol method
      # (remove the version added above and the actual version for recent pandas)
      if LooseVersion(pd.__version__) < "0.26.0.dev":
          monkeypatch.delattr(pd.Int64Dtype, "__from_arrow__")
      elif LooseVersion(pd.__version__) < "1.3.0.dev":
          monkeypatch.delattr(
              pd.core.arrays.integer._IntegerDtype, "__from_arrow__")
      else:
          monkeypatch.delattr(
              pd.core.arrays.integer.NumericDtype, "__from_arrow__")
  
      result = arr.to_pandas()
      assert not isinstance(result._data.blocks[0], _int.ExtensionBlock)
      expected = pd.Series([1, 2, 3, 4])
      tm.assert_series_equal(result, expected)
  
      with pytest.raises(ValueError):
          table.to_pandas()
  
  
  def test_to_pandas_extension_dtypes_mapping():
      if LooseVersion(pd.__version__) < "0.26.0.dev":
          pytest.skip("Conversion to pandas IntegerArray not yet supported")
  
      table = pa.table({'a': pa.array([1, 2, 3], pa.int64())})
  
      # default use numpy dtype
      result = table.to_pandas()
      assert result['a'].dtype == np.dtype('int64')
  
      # specify to override the default
      result = table.to_pandas(types_mapper={pa.int64(): pd.Int64Dtype()}.get)
      assert isinstance(result['a'].dtype, pd.Int64Dtype)
  
      # types that return None in function get normal conversion
      table = pa.table({'a': pa.array([1, 2, 3], pa.int32())})
      result = table.to_pandas(types_mapper={pa.int64(): pd.Int64Dtype()}.get)
      assert result['a'].dtype == np.dtype('int32')
  
      # `types_mapper` overrules the pandas metadata
      table = pa.table(pd.DataFrame({'a': pd.array([1, 2, 3], dtype="Int64")}))
      result = table.to_pandas()
      assert isinstance(result['a'].dtype, pd.Int64Dtype)
      result = table.to_pandas(
          types_mapper={pa.int64(): pd.PeriodDtype('D')}.get)
      assert isinstance(result['a'].dtype, pd.PeriodDtype)
  
  
  def test_array_to_pandas():
      if LooseVersion(pd.__version__) < "1.1":
          pytest.skip("ExtensionDtype to_pandas method missing")
  
      for arr in [pd.period_range("2012-01-01", periods=3, freq="D").array,
                  pd.interval_range(1, 4).array]:
          result = pa.array(arr).to_pandas()
          expected = pd.Series(arr)
          tm.assert_series_equal(result, expected)
  
          # TODO implement proper conversion for chunked array
          # result = pa.table({"col": arr})["col"].to_pandas()
          # expected = pd.Series(arr, name="col")
          # tm.assert_series_equal(result, expected)
  
  
  # ----------------------------------------------------------------------
  # Legacy metadata compatibility tests
  
  
  def test_metadata_compat_range_index_pre_0_12():
      # Forward compatibility for metadata created from pandas.RangeIndex
      # prior to pyarrow 0.13.0
      a_values = ['foo', 'bar', None, 'baz']
      b_values = ['a', 'a', 'b', 'b']
      a_arrow = pa.array(a_values, type='utf8')
      b_arrow = pa.array(b_values, type='utf8')
  
      rng_index_arrow = pa.array([0, 2, 4, 6], type='int64')
  
      gen_name_0 = '__index_level_0__'
      gen_name_1 = '__index_level_1__'
  
      # Case 1: named RangeIndex
      e1 = pd.DataFrame({
          'a': a_values
      }, index=pd.RangeIndex(0, 8, step=2, name='qux'))
      t1 = pa.Table.from_arrays([a_arrow, rng_index_arrow],
                                names=['a', 'qux'])
      t1 = t1.replace_schema_metadata({
          b'pandas': json.dumps(
              {'index_columns': ['qux'],
               'column_indexes': [{'name': None,
                                   'field_name': None,
                                   'pandas_type': 'unicode',
                                   'numpy_type': 'object',
                                   'metadata': {'encoding': 'UTF-8'}}],
               'columns': [{'name': 'a',
                            'field_name': 'a',
                            'pandas_type': 'unicode',
                            'numpy_type': 'object',
                            'metadata': None},
                           {'name': 'qux',
                            'field_name': 'qux',
                            'pandas_type': 'int64',
                            'numpy_type': 'int64',
                            'metadata': None}],
               'pandas_version': '0.23.4'}
          )})
      r1 = t1.to_pandas()
      tm.assert_frame_equal(r1, e1)
  
      # Case 2: named RangeIndex, but conflicts with an actual column
      e2 = pd.DataFrame({
          'qux': a_values
      }, index=pd.RangeIndex(0, 8, step=2, name='qux'))
      t2 = pa.Table.from_arrays([a_arrow, rng_index_arrow],
                                names=['qux', gen_name_0])
      t2 = t2.replace_schema_metadata({
          b'pandas': json.dumps(
              {'index_columns': [gen_name_0],
               'column_indexes': [{'name': None,
                                   'field_name': None,
                                   'pandas_type': 'unicode',
                                   'numpy_type': 'object',
                                   'metadata': {'encoding': 'UTF-8'}}],
               'columns': [{'name': 'a',
                            'field_name': 'a',
                            'pandas_type': 'unicode',
                            'numpy_type': 'object',
                            'metadata': None},
                           {'name': 'qux',
                            'field_name': gen_name_0,
                            'pandas_type': 'int64',
                            'numpy_type': 'int64',
                            'metadata': None}],
               'pandas_version': '0.23.4'}
          )})
      r2 = t2.to_pandas()
      tm.assert_frame_equal(r2, e2)
  
      # Case 3: unnamed RangeIndex
      e3 = pd.DataFrame({
          'a': a_values
      }, index=pd.RangeIndex(0, 8, step=2, name=None))
      t3 = pa.Table.from_arrays([a_arrow, rng_index_arrow],
                                names=['a', gen_name_0])
      t3 = t3.replace_schema_metadata({
          b'pandas': json.dumps(
              {'index_columns': [gen_name_0],
               'column_indexes': [{'name': None,
                                   'field_name': None,
                                   'pandas_type': 'unicode',
                                   'numpy_type': 'object',
                                   'metadata': {'encoding': 'UTF-8'}}],
               'columns': [{'name': 'a',
                            'field_name': 'a',
                            'pandas_type': 'unicode',
                            'numpy_type': 'object',
                            'metadata': None},
                           {'name': None,
                            'field_name': gen_name_0,
                            'pandas_type': 'int64',
                            'numpy_type': 'int64',
                            'metadata': None}],
               'pandas_version': '0.23.4'}
          )})
      r3 = t3.to_pandas()
      tm.assert_frame_equal(r3, e3)
  
      # Case 4: MultiIndex with named RangeIndex
      e4 = pd.DataFrame({
          'a': a_values
      }, index=[pd.RangeIndex(0, 8, step=2, name='qux'), b_values])
      t4 = pa.Table.from_arrays([a_arrow, rng_index_arrow, b_arrow],
                                names=['a', 'qux', gen_name_1])
      t4 = t4.replace_schema_metadata({
          b'pandas': json.dumps(
              {'index_columns': ['qux', gen_name_1],
               'column_indexes': [{'name': None,
                                   'field_name': None,
                                   'pandas_type': 'unicode',
                                   'numpy_type': 'object',
                                   'metadata': {'encoding': 'UTF-8'}}],
               'columns': [{'name': 'a',
                            'field_name': 'a',
                            'pandas_type': 'unicode',
                            'numpy_type': 'object',
                            'metadata': None},
                           {'name': 'qux',
                            'field_name': 'qux',
                            'pandas_type': 'int64',
                            'numpy_type': 'int64',
                            'metadata': None},
                           {'name': None,
                            'field_name': gen_name_1,
                            'pandas_type': 'unicode',
                            'numpy_type': 'object',
                            'metadata': None}],
               'pandas_version': '0.23.4'}
          )})
      r4 = t4.to_pandas()
      tm.assert_frame_equal(r4, e4)
  
      # Case 4: MultiIndex with unnamed RangeIndex
      e5 = pd.DataFrame({
          'a': a_values
      }, index=[pd.RangeIndex(0, 8, step=2, name=None), b_values])
      t5 = pa.Table.from_arrays([a_arrow, rng_index_arrow, b_arrow],
                                names=['a', gen_name_0, gen_name_1])
      t5 = t5.replace_schema_metadata({
          b'pandas': json.dumps(
              {'index_columns': [gen_name_0, gen_name_1],
               'column_indexes': [{'name': None,
                                   'field_name': None,
                                   'pandas_type': 'unicode',
                                   'numpy_type': 'object',
                                   'metadata': {'encoding': 'UTF-8'}}],
               'columns': [{'name': 'a',
                            'field_name': 'a',
                            'pandas_type': 'unicode',
                            'numpy_type': 'object',
                            'metadata': None},
                           {'name': None,
                            'field_name': gen_name_0,
                            'pandas_type': 'int64',
                            'numpy_type': 'int64',
                            'metadata': None},
                           {'name': None,
                            'field_name': gen_name_1,
                            'pandas_type': 'unicode',
                            'numpy_type': 'object',
                            'metadata': None}],
               'pandas_version': '0.23.4'}
          )})
      r5 = t5.to_pandas()
      tm.assert_frame_equal(r5, e5)
  
  
  def test_metadata_compat_missing_field_name():
      # Combination of missing field name but with index column as metadata.
      # This combo occurs in the latest versions of fastparquet (0.3.2), but not
      # in pyarrow itself (since field_name was added in 0.8, index as metadata
      # only added later)
  
      a_values = [1, 2, 3, 4]
      b_values = ['a', 'b', 'c', 'd']
      a_arrow = pa.array(a_values, type='int64')
      b_arrow = pa.array(b_values, type='utf8')
  
      expected = pd.DataFrame({
          'a': a_values,
          'b': b_values,
      }, index=pd.RangeIndex(0, 8, step=2, name='qux'))
      table = pa.table({'a': a_arrow, 'b': b_arrow})
  
      # metadata generated by fastparquet 0.3.2 with missing field_names
      table = table.replace_schema_metadata({
          b'pandas': json.dumps({
              'column_indexes': [
                  {'field_name': None,
                   'metadata': None,
                   'name': None,
                   'numpy_type': 'object',
                   'pandas_type': 'mixed-integer'}
              ],
              'columns': [
                  {'metadata': None,
                   'name': 'a',
                   'numpy_type': 'int64',
                   'pandas_type': 'int64'},
                  {'metadata': None,
                   'name': 'b',
                   'numpy_type': 'object',
                   'pandas_type': 'unicode'}
              ],
              'index_columns': [
                  {'kind': 'range',
                   'name': 'qux',
                   'start': 0,
                   'step': 2,
                   'stop': 8}
              ],
              'pandas_version': '0.25.0'}
  
          )})
      result = table.to_pandas()
      tm.assert_frame_equal(result, expected)
  
  
  def test_metadata_index_name_not_json_serializable():
      name = np.int64(6)  # not json serializable by default
      table = pa.table(pd.DataFrame(index=pd.RangeIndex(0, 4, name=name)))
      metadata = table.schema.pandas_metadata
      assert metadata['index_columns'][0]['name'] == '6'
  
  
  def test_metadata_index_name_is_json_serializable():
      name = 6  # json serializable by default
      table = pa.table(pd.DataFrame(index=pd.RangeIndex(0, 4, name=name)))
      metadata = table.schema.pandas_metadata
      assert metadata['index_columns'][0]['name'] == 6
  
  
  def make_df_with_timestamps():
      # Some of the milliseconds timestamps deliberately don't fit in the range
      # that is possible with nanosecond timestamps.
      df = pd.DataFrame({
          'dateTimeMs': [
              np.datetime64('0001-01-01 00:00', 'ms'),
              np.datetime64('2012-05-02 12:35', 'ms'),
              np.datetime64('2012-05-03 15:42', 'ms'),
              np.datetime64('3000-05-03 15:42', 'ms'),
          ],
          'dateTimeNs': [
              np.datetime64('1991-01-01 00:00', 'ns'),
              np.datetime64('2012-05-02 12:35', 'ns'),
              np.datetime64('2012-05-03 15:42', 'ns'),
              np.datetime64('2050-05-03 15:42', 'ns'),
          ],
      })
      # Not part of what we're testing, just ensuring that the inputs are what we
      # expect.
      assert (df.dateTimeMs.dtype, df.dateTimeNs.dtype) == (
          # O == object, <M8[ns] == timestamp64[ns]
          np.dtype("O"), np.dtype("<M8[ns]")
      )
      return df
  
  
  @pytest.mark.parquet
  def test_timestamp_as_object_parquet(tempdir):
      # Timestamps can be stored as Parquet and reloaded into Pandas with no loss
      # of information if the timestamp_as_object option is True.
      df = make_df_with_timestamps()
      table = pa.Table.from_pandas(df)
      filename = tempdir / "timestamps_from_pandas.parquet"
      pq.write_table(table, filename, version="2.0")
      result = pq.read_table(filename)
      df2 = result.to_pandas(timestamp_as_object=True)
      tm.assert_frame_equal(df, df2)
  
  
  def test_timestamp_as_object_out_of_range():
      # Out of range timestamps can be converted Arrow and reloaded into Pandas
      # with no loss of information if the timestamp_as_object option is True.
      df = make_df_with_timestamps()
      table = pa.Table.from_pandas(df)
      df2 = table.to_pandas(timestamp_as_object=True)
      tm.assert_frame_equal(df, df2)
  
  
  @pytest.mark.parametrize("resolution", ["s", "ms", "us"])
  @pytest.mark.parametrize("tz", [None, "America/New_York"])
  # One datetime outside nanosecond range, one inside nanosecond range:
  @pytest.mark.parametrize("dt", [datetime(1553, 1, 1), datetime(2020, 1, 1)])
  def test_timestamp_as_object_non_nanosecond(resolution, tz, dt):
      # Timestamps can be converted Arrow and reloaded into Pandas with no loss
      # of information if the timestamp_as_object option is True.
      arr = pa.array([dt], type=pa.timestamp(resolution, tz=tz))
      table = pa.table({'a': arr})
  
      for result in [
          arr.to_pandas(timestamp_as_object=True),
          table.to_pandas(timestamp_as_object=True)['a']
      ]:
          assert result.dtype == object
          assert isinstance(result[0], datetime)
          if tz:
              assert result[0].tzinfo is not None
              expected = result[0].tzinfo.fromutc(dt)
          else:
              assert result[0].tzinfo is None
              expected = dt
          assert result[0] == expected
  
  
  def test_threaded_pandas_import():
      invoke_script("pandas_threaded_import.py")
  