test_pandas.py

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# to you under the Apache License, Version 2.0 (the
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# with the License.  You may obtain a copy of the License at
#
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import gc
import decimal
import json
import multiprocessing as mp
import sys
import warnings

from collections import OrderedDict
from datetime import date, datetime, time, timedelta, timezone

import hypothesis as h
import hypothesis.strategies as st
import numpy as np
import numpy.testing as npt
import pytest

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.tests.util as test_util
from pyarrow.vendored.version import Version

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


try:
    _np_VisibleDeprecationWarning = np.VisibleDeprecationWarning
except AttributeError:
    from numpy.exceptions import (
        VisibleDeprecationWarning as _np_VisibleDeprecationWarning
    )


# 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,
        'datetime[s]': np.arange("2016-01-01T00:00:00.001", size,
                                 dtype='datetime64[s]'),
        'datetime[ms]': np.arange("2016-01-01T00:00:00.001", size,
                                  dtype='datetime64[ms]'),
        'datetime[us]': np.arange("2016-01-01T00:00:00.001", size,
                                  dtype='datetime64[us]'),
        'datetime[ns]': np.arange("2016-01-01T00:00:00.001", size,
                                  dtype='datetime64[ns]'),
        'timedelta64[s]': np.arange(0, size, dtype='timedelta64[s]'),
        'timedelta64[ms]': np.arange(0, size, dtype='timedelta64[ms]'),
        'timedelta64[us]': np.arange(0, size, dtype='timedelta64[us]'),
        'timedelta64[ns]': np.arange(0, size, dtype='timedelta64[ns]'),
        '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

        for col in expected.columns:
            if expected[col].dtype == 'object':
                expected[col] = expected[col].replace({np.nan: None})

    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", "elementwise comparison failed", DeprecationWarning)
        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(values).copy()
            expected[mask.copy()] = None

        if expected.dtype == 'object':
            expected = expected.replace({np.nan: None})

    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_non_string_columns_with_index(self):
        df = pd.DataFrame({0: [1.0, 2.0, 3.0], 1: [4.0, 5.0, 6.0]})
        df = df.set_index(0)

        # assert that the from_pandas raises the warning
        with pytest.warns(UserWarning):
            table = pa.Table.from_pandas(df)
            assert table.field(0).name == '1'

        expected = df.copy()
        # non-str index name will be converted to str
        expected.index.name = str(expected.index.name)
        with pytest.warns(UserWarning):
            _check_pandas_roundtrip(df, expected=expected,
                                    preserve_index=True)

    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_column_index_names_with_tz(self):
        # ARROW-13756
        # Bug if index is timezone aware DataTimeIndex

        df = pd.DataFrame(
            np.random.randn(5, 3),
            columns=pd.date_range("2021-01-01", periods=3, freq="50D", tz="CET")
        )
        _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 warnings.catch_warnings():
            warnings.simplefilter(action="error")
            # make_block deprecation in pandas, still under discussion
            # https://github.com/pandas-dev/pandas/pull/56422
            # https://github.com/pandas-dev/pandas/issues/40226
            warnings.filterwarnings(
                "ignore", "make_block is deprecated", DeprecationWarning
            )
            _check_pandas_roundtrip(df, preserve_index=True)

    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 warnings.catch_warnings():
            warnings.simplefilter(action="error")
            # make_block deprecation in pandas, still under discussion
            # https://github.com/pandas-dev/pandas/pull/56422
            # https://github.com/pandas-dev/pandas/issues/40226
            warnings.filterwarnings(
                "ignore", "make_block is deprecated", DeprecationWarning
            )
            _check_pandas_roundtrip(df, preserve_index=True)

    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):
        if Version("2.0.0") <= Version(pd.__version__) < Version("3.0.0"):
            # TODO: regression in pandas, hopefully fixed in next version
            # https://issues.apache.org/jira/browse/ARROW-18394
            # https://github.com/pandas-dev/pandas/issues/50127
            pytest.skip("Regression in pandas 2.0.0")
        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,
        )

    @pytest.mark.parametrize('unit', ['s', 'ms', 'us', 'ns'])
    def test_timestamps_with_timezone(self, unit):
        if Version(pd.__version__) < Version("2.0.0") and unit != 'ns':
            pytest.skip("pandas < 2.0 only supports nanosecond datetime64")
        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=f'datetime64[{unit}]')
        })
        df['datetime64'] = df['datetime64'].dt.tz_localize('US/Eastern')
        _check_pandas_roundtrip(df)

        _check_series_roundtrip(df['datetime64'])

        # drop-in a null
        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=f'datetime64[{unit}]')
        })
        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()
        # Pandas v2 defaults to [ns], but Arrow defaults to [us] time units
        # so we need to cast the pandas dtype. Pandas v1 will always silently
        # coerce to [ns] due to lack of non-[ns] support.
        expected_df = pd.DataFrame({
            'datetime': pd.Series(date_array, dtype='datetime64[us]')
        })
        tm.assert_frame_equal(expected_df, result)

    def test_python_datetime_with_pytz_tzinfo(self):
        pytz = pytest.importorskip("pytz")

        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() | past.timezones)
    @h.settings(deadline=None)
    def test_python_datetime_with_pytz_timezone(self, tz):
        if str(tz) in ["build/etc/localtime", "Factory"]:
            pytest.skip("Localtime timezone not supported")
        values = [datetime(2018, 1, 1, 12, 23, 45, tzinfo=tz)]
        df = pd.DataFrame({'datetime': values})
        _check_pandas_roundtrip(df, check_dtype=False)

    def test_python_datetime_with_timezone_tzinfo(self):
        pytz = pytest.importorskip("pytz")
        from datetime import timezone

        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()

        # Pandas v2 defaults to [ns], but Arrow defaults to [us] time units
        # so we need to cast the pandas dtype. Pandas v1 will always silently
        # coerce to [ns] due to lack of non-[ns] support.
        expected_df = pd.DataFrame(
            {"datetime": pd.Series(date_array, dtype='datetime64[us]')})

        # 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, 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',
            '2018-05-10T10:24:01',
            '2018-05-11T10:24:01',
            '2018-05-12T10:24:01',
        ])
        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),
            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("coerce_to_ns,expected_dtype",
                             [(False, 'datetime64[ms]'),
                              (True, 'datetime64[ns]')])
    def test_array_types_date_as_object(self, coerce_to_ns, expected_dtype):
        data = [date(2000, 1, 1),
                None,
                date(1970, 1, 1),
                date(2040, 2, 26)]
        expected_days = np.array(['2000-01-01', None, '1970-01-01',
                                  '2040-02-26'], dtype='datetime64[D]')

        if Version(pd.__version__) < Version("2.0.0"):
            # ARROW-3789: Coerce date/timestamp types to datetime64[ns]
            expected_dtype = 'datetime64[ns]'

        expected = np.array(['2000-01-01', None, '1970-01-01',
                             '2040-02-26'], dtype=expected_dtype)

        objects = [pa.array(data),
                   pa.chunked_array([data])]

        for obj in objects:
            result = obj.to_pandas(coerce_temporal_nanoseconds=coerce_to_ns)
            expected_obj = expected_days.astype(object)
            assert result.dtype == expected_obj.dtype
            npt.assert_array_equal(result, expected_obj)

            result = obj.to_pandas(date_as_object=False,
                                   coerce_temporal_nanoseconds=coerce_to_ns)
            assert result.dtype == expected.dtype
            npt.assert_array_equal(result, expected)

    @pytest.mark.parametrize("coerce_to_ns,expected_type",
                             [(False, 'datetime64[ms]'),
                              (True, 'datetime64[ns]')])
    def test_table_convert_date_as_object(self, coerce_to_ns, expected_type):
        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,
                                      coerce_temporal_nanoseconds=coerce_to_ns)
        df_object = table.to_pandas()

        tm.assert_frame_equal(df.astype(expected_type), df_datetime,
                              check_dtype=True)
        tm.assert_frame_equal(df, df_object, check_dtype=True)

    @pytest.mark.parametrize("arrow_type",
                             [pa.date32(), pa.date64(), pa.timestamp('s'),
                              pa.timestamp('ms'), pa.timestamp('us'),
                              pa.timestamp('ns'), pa.timestamp('s', 'UTC'),
                              pa.timestamp('ms', 'UTC'), pa.timestamp('us', 'UTC'),
                              pa.timestamp('ns', 'UTC')])
    def test_array_coerce_temporal_nanoseconds(self, arrow_type):
        data = [date(2000, 1, 1), datetime(2001, 1, 1)]
        expected = pd.Series(data)
        arr = pa.array(data).cast(arrow_type)
        result = arr.to_pandas(
            coerce_temporal_nanoseconds=True, date_as_object=False)
        expected_tz = None
        if hasattr(arrow_type, 'tz') and arrow_type.tz is not None:
            expected_tz = 'UTC'
        expected_type = pa.timestamp('ns', expected_tz).to_pandas_dtype()
        tm.assert_series_equal(result, expected.astype(expected_type))

    @pytest.mark.parametrize("arrow_type",
                             [pa.date32(), pa.date64(), pa.timestamp('s'),
                              pa.timestamp('ms'), pa.timestamp('us'),
                              pa.timestamp('ns'), pa.timestamp('s', 'UTC'),
                              pa.timestamp('ms', 'UTC'), pa.timestamp('us', 'UTC'),
                              pa.timestamp('ns', 'UTC')])
    def test_table_coerce_temporal_nanoseconds(self, arrow_type):
        data = [date(2000, 1, 1), datetime(2001, 1, 1)]
        schema = pa.schema([pa.field('date', arrow_type)])
        expected_df = pd.DataFrame({'date': data})
        table = pa.table([pa.array(data)], schema=schema)
        result_df = table.to_pandas(
            coerce_temporal_nanoseconds=True, date_as_object=False)
        expected_tz = None
        if hasattr(arrow_type, 'tz') and arrow_type.tz is not None:
            expected_tz = 'UTC'
        expected_type = pa.timestamp('ns', expected_tz).to_pandas_dtype()
        tm.assert_frame_equal(result_df, expected_df.astype(expected_type))

    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

        # date32 and date64 convert to [ms] in pandas v2, but
        # in pandas v1 they are silently coerced to [ns]
        ex_datetime64ms = ex_values.astype('datetime64[ms]')
        expected_pandas = pd.DataFrame({'date32': ex_datetime64ms,
                                        'date64': ex_datetime64ms},
                                       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_coerces_to_ns(self):
        # non-ns timestamp gets cast to ns on conversion to pandas
        if Version(pd.__version__) >= Version("2.0.0"):
            pytest.skip("pandas >= 2.0 supports non-nanosecond datetime64")

        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
        # that end up getting coerced into 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(coerce_temporal_nanoseconds=True)

                with pytest.raises(ValueError, match=msg):
                    table.to_pandas(coerce_temporal_nanoseconds=True)

                with pytest.raises(ValueError, match=msg):
                    # chunked array
                    table.column('a').to_pandas(coerce_temporal_nanoseconds=True)

                # just ensure those don't give an error, but do not
                # check actual garbage output
                arr.to_pandas(safe=False, coerce_temporal_nanoseconds=True)
                table.to_pandas(safe=False, coerce_temporal_nanoseconds=True)
                table.column('a').to_pandas(
                    safe=False, coerce_temporal_nanoseconds=True)

    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[us]")})
        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(
        Version('1.16.0') <= Version(np.__version__) < 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_dtype=False' because pandas >= 2 uses datetime.timezone
        # instead of pytz.FixedOffset, and thus the dtype is not exactly
        # identical (pyarrow still defaults to pytz)
        # TODO remove if https://github.com/apache/arrow/issues/15047 is fixed
        _check_pandas_roundtrip(df, check_dtype=False)

    @pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns'])
    def test_timedeltas_no_nulls(self, unit):
        if Version(pd.__version__) < Version("2.0.0"):
            unit = 'ns'
        df = pd.DataFrame({
            'timedelta64': np.array([0, 3600000000000, 7200000000000],
                                    dtype=f'timedelta64[{unit}]')
        })
        field = pa.field('timedelta64', pa.duration(unit))
        schema = pa.schema([field])
        _check_pandas_roundtrip(
            df,
            expected_schema=schema,
        )

    @pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns'])
    def test_timedeltas_nulls(self, unit):
        if Version(pd.__version__) < Version("2.0.0"):
            unit = 'ns'
        df = pd.DataFrame({
            'timedelta64': np.array([0, None, 7200000000000],
                                    dtype=f'timedelta64[{unit}]')
        })
        field = pa.field('timedelta64', pa.duration(unit))
        schema = pa.schema([field])
        _check_pandas_roundtrip(
            df,
            expected_schema=schema,
        )

    def test_month_day_nano_interval(self):
        from pandas.tseries.offsets import DateOffset
        df = pd.DataFrame({
            'date_offset': [None,
                            DateOffset(days=3600, months=3600, microseconds=3,
                                       nanoseconds=600)]
        })
        schema = pa.schema([('date_offset', pa.month_day_nano_interval())])
        _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])
        ex_values = ['foo', None, 'bar', 'mañana', None]
        expected = pd.DataFrame({'strings': ex_values * repeats})

        _check_pandas_roundtrip(df, expected=expected, 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', None]
        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)
        arr.validate(full=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_binary_view(self):
        s = pd.Series([b'123', b'', b'a', None])
        _check_series_roundtrip(s, type_=pa.binary_view())
        df = pd.DataFrame({'a': s})
        _check_pandas_roundtrip(
            df, schema=pa.schema([('a', pa.binary_view())]))

    def test_string_view(self):
        s = pd.Series(['123', '', 'a', None])
        _check_series_roundtrip(s, type_=pa.string_view())
        df = pd.DataFrame({'a': s})
        _check_pandas_roundtrip(
            df, schema=pa.schema([('a', pa.string_view())]))

    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([False, True, False])
        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', [[np.nan, 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())

        # pandas.testing generates a
        # DeprecationWarning: elementwise comparison failed
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "elementwise comparison failed",
                                    DeprecationWarning)
            tm.assert_series_equal(series, expected)

    def test_to_list_of_maps_pandas(self):
        if ((Version(np.__version__) >= Version("1.25.0.dev0")) and
                (Version(pd.__version__) < Version("2.0.0"))):
            # TODO: regression in pandas with numpy 1.25dev
            # https://github.com/pandas-dev/pandas/issues/50360
            pytest.skip("Regression in pandas with numpy 1.25")
        data = [
            [[('foo', ['a', 'b']), ('bar', ['c', 'd'])]],
            [[('baz', []), ('qux', None), ('quux', [None, 'e'])], [('quz', ['f', 'g'])]]
        ]
        arr = pa.array(data, pa.list_(pa.map_(pa.utf8(), pa.list_(pa.utf8()))))
        series = arr.to_pandas()
        expected = pd.Series(data)

        # pandas.testing generates a
        # DeprecationWarning: elementwise comparison failed
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "elementwise comparison failed",
                                    DeprecationWarning)
            tm.assert_series_equal(series, expected)

    def test_to_list_of_maps_pandas_sliced(self):
        """
        A slightly more rigorous test for chunk/slice combinations
        """

        if ((Version(np.__version__) >= Version("1.25.0.dev0")) and
                (Version(pd.__version__) < Version("2.0.0"))):
            # TODO: regression in pandas with numpy 1.25dev
            # https://github.com/pandas-dev/pandas/issues/50360
            pytest.skip("Regression in pandas with numpy 1.25")

        keys = pa.array(['ignore', 'foo', 'bar', 'baz',
                         'qux', 'quux', 'ignore']).slice(1, 5)
        items = pa.array(
            [['ignore'], ['ignore'], ['a', 'b'], ['c', 'd'], [], None, [None, 'e']],
            pa.list_(pa.string()),
        ).slice(2, 5)
        map = pa.MapArray.from_arrays([0, 2, 4], keys, items)
        arr = pa.ListArray.from_arrays([0, 1, 2], map)

        series = arr.to_pandas()
        expected = pd.Series([
            [[('foo', ['a', 'b']), ('bar', ['c', 'd'])]],
            [[('baz', []), ('qux', None)]],
        ])

        series_sliced = arr.slice(1, 2).to_pandas()
        expected_sliced = pd.Series([
            [[('baz', []), ('qux', None)]],
        ])

        # pandas.testing generates a
        # DeprecationWarning: elementwise comparison failed
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "elementwise comparison failed",
                                    DeprecationWarning)
            tm.assert_series_equal(series, expected)
            tm.assert_series_equal(series_sliced, expected_sliced)

    @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())

        with warnings.catch_warnings():
            warnings.filterwarnings("ignore",
                                    "Creating an ndarray from ragged nested",
                                    _np_VisibleDeprecationWarning)
            warnings.filterwarnings("ignore", "elementwise comparison failed",
                                    DeprecationWarning)
            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.large_memory
    def test_auto_chunking_on_list_overflow(self):
        # ARROW-9976
        n = 2**21
        df = pd.DataFrame.from_dict({
            "a": list(np.zeros((n, 2**10), dtype='uint8')),
            "b": range(n)
        })
        table = pa.Table.from_pandas(df)
        table.validate(full=True)

        column_a = table[0]
        assert column_a.num_chunks == 2
        assert len(column_a.chunk(0)) == 2**21 - 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)

    def test_list_no_duplicate_base(self):
        # ARROW-18400
        arr = pa.array([[1, 2], [3, 4, 5], None, [6, None], [7, 8]])
        chunked_arr = pa.chunked_array([arr.slice(0, 3), arr.slice(3, 1)])

        np_arr = chunked_arr.to_numpy()

        expected = np.array([[1., 2.], [3., 4., 5.], None,
                            [6., np.nan]], dtype="object")
        for left, right in zip(np_arr, expected):
            if right is None:
                assert left == right
            else:
                npt.assert_array_equal(left, right)

        expected_base = np.array([[1., 2., 3., 4., 5., 6., np.nan]])
        npt.assert_array_equal(np_arr[0].base, expected_base)

        np_arr_sliced = chunked_arr.slice(1, 3).to_numpy()

        expected = np.array([[3, 4, 5], None, [6, np.nan]], dtype="object")
        for left, right in zip(np_arr_sliced, expected):
            if right is None:
                assert left == right
            else:
                npt.assert_array_equal(left, right)

        expected_base = np.array([[3., 4., 5., 6., np.nan]])
        npt.assert_array_equal(np_arr_sliced[0].base, expected_base)

    def test_list_values_behind_null(self):
        arr = pa.ListArray.from_arrays(
            offsets=pa.array([0, 2, 4, 6]),
            values=pa.array([1, 2, 99, 99, 3, None]),
            mask=pa.array([False, True, False])
        )
        np_arr = arr.to_numpy(zero_copy_only=False)

        expected = np.array([[1., 2.], None, [3., np.nan]], dtype="object")
        for left, right in zip(np_arr, expected):
            if right is None:
                assert left == right
            else:
                npt.assert_array_equal(left, right)


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 necessarily a multiple of sizeof(object)
        # object_ is 8 bytes on 64-bit systems, 4 bytes on 32-bit systems
        assert dt.itemsize == (12 if sys.maxsize > 2**32 else 8)
        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.slow
    @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)
        arr.validate(full=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)
        arr.validate(full=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], dtype="int64"),
        )

        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:
            # Casting np.float64 -> uint32 or uint64 throws a RuntimeWarning
            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                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, preserve_index=None)
        _check_pandas_roundtrip(df, preserve_index=None, as_batch=True)

        expected = pd.DataFrame(columns=pd.Index([]))
        _check_pandas_roundtrip(df, expected, preserve_index=False)
        _check_pandas_roundtrip(df, expected, preserve_index=False, 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'),
        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):
    if Version("2.0.0") <= Version(pd.__version__) < Version("3.0.0"):
        # TODO: regression in pandas, hopefully fixed in next version
        # https://issues.apache.org/jira/browse/ARROW-18394
        # https://github.com/pandas-dev/pandas/issues/50127
        pytest.skip("Regression in pandas 2.0.0")

    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 _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)


# ----------------------------------------------------------------------
# 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.int64()),
    ])

    # 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.int64()),
        ('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_metadata():
    # 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()
    coerce_cols_to_types = {"float": "float32"}
    if Version(pd.__version__) >= Version("2.0.0"):
        # Pandas v2 now support non-nanosecond time units
        coerce_cols_to_types["datetime"] = "datetime64[s, UTC]"
    expected = df[["float", "datetime"]].astype(coerce_cols_to_types)

    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 loading
    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()
    # access private `_values` because the public `values` is made read-only by 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 _get_mgr(df):
    if Version(pd.__version__) < Version("1.1.0"):
        return df._data
    else:
        return df._mgr


def _check_blocks_created(t, number):
    x = t.to_pandas(split_blocks=True)
    assert len(_get_mgr(x).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
    typ = pa.dictionary(
        index_type=pa.int8(), value_type=pa.string(), ordered=True)
    msg = "The 'ordered' flag of the passed categorical values "
    with pytest.raises(ValueError, match=msg):
        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']


def test_convert_categories_to_array_with_string_pyarrow_dtype():
    # gh-33727: categories should be converted to pa.Array
    if Version(pd.__version__) < Version("1.3.0"):
        pytest.skip("PyArrow backed string data type introduced in pandas 1.3.0")

    df = pd.DataFrame({"x": ["foo", "bar", "foo"]}, dtype="string[pyarrow]")
    df = df.astype("category")
    indices = pa.array(df['x'].cat.codes)
    dictionary = pa.array(df["x"].cat.categories.values)
    assert isinstance(dictionary, pa.Array)

    expected = pa.Array.from_pandas(df['x'])
    result = pa.DictionaryArray.from_arrays(indices, dictionary)
    assert result == expected


# ----------------------------------------------------------------------
# Array protocol in pandas conversions tests


def test_array_protocol():
    df = pd.DataFrame({'a': pd.Series([1, 2, None], dtype='Int64')})

    # __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.ExtensionType):

    def __init__(self):
        super().__init__(pa.int64(),
                         'pyarrow.tests.test_pandas.DummyExtensionType')

    def __arrow_ext_serialize__(self):
        return b''

    @classmethod
    def __arrow_ext_deserialize__(cls, storage_type, serialized):
        assert serialized == b''
        assert storage_type == pa.int64()
        return cls()


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

    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):
    # 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 _get_mgr(result).blocks[0].values.dtype == np.dtype("int64")
    assert _get_mgr(result).blocks[1].values.dtype == pd.Int64Dtype()
    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 _get_mgr(result).blocks[0].values.dtype == pd.Int64Dtype()
    tm.assert_frame_equal(result, df2)

    # monkeypatch pandas Int64Dtype to *not* have the protocol method
    if Version(pd.__version__) < 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(_get_mgr(result).blocks) == 1
    assert _get_mgr(result).blocks[0].values.dtype == np.dtype("int64")


class MyCustomIntegerType(pa.ExtensionType):

    def __init__(self):
        super().__init__(pa.int64(),
                         'pyarrow.tests.test_pandas.MyCustomIntegerType')

    def __arrow_ext_serialize__(self):
        return b''

    def to_pandas_dtype(self):
        return pd.Int64Dtype()


def test_conversion_extensiontype_to_extensionarray(monkeypatch):
    # converting extension type to linked pandas ExtensionDtype/Array
    storage = pa.array([1, 2, 3, 4], pa.int64())
    arr = pa.ExtensionArray.from_storage(MyCustomIntegerType(), storage)
    table = pa.table({'a': arr})

    # extension type points to Int64Dtype, which knows how to create a
    # pandas ExtensionArray
    result = arr.to_pandas()
    assert _get_mgr(result).blocks[0].values.dtype == pd.Int64Dtype()
    expected = pd.Series([1, 2, 3, 4], dtype='Int64')
    tm.assert_series_equal(result, expected)

    result = table.to_pandas()
    assert _get_mgr(result).blocks[0].values.dtype == pd.Int64Dtype()
    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 Version(pd.__version__) < 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 _get_mgr(result).blocks[0].values.dtype == np.dtype("int64")
    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():
    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 Version(pd.__version__) < 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)

        result = pa.table({"col": arr})["col"].to_pandas()
        expected = pd.Series(arr, name="col")
        tm.assert_series_equal(result, expected)


def test_roundtrip_empty_table_with_extension_dtype_index():
    df = pd.DataFrame(index=pd.interval_range(start=0, end=3))
    table = pa.table(df)
    if Version(pd.__version__) > Version("1.0"):
        tm.assert_index_equal(table.to_pandas().index, df.index)
    else:
        tm.assert_index_equal(table.to_pandas().index,
                              pd.Index([{'left': 0, 'right': 1},
                                        {'left': 1, 'right': 2},
                                        {'left': 2, 'right': 3}],
                                       dtype='object'))


@pytest.mark.parametrize("index", ["a", ["a", "b"]])
def test_to_pandas_types_mapper_index(index):
    if Version(pd.__version__) < Version("1.5.0"):
        pytest.skip("ArrowDtype missing")
    df = pd.DataFrame(
        {
            "a": [1, 2],
            "b": [3, 4],
            "c": [5, 6],
        },
        dtype=pd.ArrowDtype(pa.int64()),
    ).set_index(index)
    expected = df.copy()
    table = pa.table(df)
    result = table.to_pandas(types_mapper=pd.ArrowDtype)
    tm.assert_frame_equal(result, expected)


def test_array_to_pandas_types_mapper():
    # https://issues.apache.org/jira/browse/ARROW-9664
    if Version(pd.__version__) < Version("1.2.0"):
        pytest.skip("Float64Dtype extension dtype missing")

    data = pa.array([1, 2, 3], pa.int64())

    # Test with mapper function
    types_mapper = {pa.int64(): pd.Int64Dtype()}.get
    result = data.to_pandas(types_mapper=types_mapper)
    assert result.dtype == pd.Int64Dtype()

    # Test mapper function returning None
    types_mapper = {pa.int64(): None}.get
    result = data.to_pandas(types_mapper=types_mapper)
    assert result.dtype == np.dtype("int64")

    # Test mapper function not containing the dtype
    types_mapper = {pa.float64(): pd.Float64Dtype()}.get
    result = data.to_pandas(types_mapper=types_mapper)
    assert result.dtype == np.dtype("int64")


@pytest.mark.pandas
def test_chunked_array_to_pandas_types_mapper():
    # https://issues.apache.org/jira/browse/ARROW-9664
    if Version(pd.__version__) < Version("1.2.0"):
        pytest.skip("Float64Dtype extension dtype missing")

    data = pa.chunked_array([pa.array([1, 2, 3], pa.int64())])
    assert isinstance(data, pa.ChunkedArray)

    # Test with mapper function
    types_mapper = {pa.int64(): pd.Int64Dtype()}.get
    result = data.to_pandas(types_mapper=types_mapper)
    assert result.dtype == pd.Int64Dtype()

    # Test mapper function returning None
    types_mapper = {pa.int64(): None}.get
    result = data.to_pandas(types_mapper=types_mapper)
    assert result.dtype == np.dtype("int64")

    # Test mapper function not containing the dtype
    types_mapper = {pa.float64(): pd.Float64Dtype()}.get
    result = data.to_pandas(types_mapper=types_mapper)
    assert result.dtype == np.dtype("int64")


# ----------------------------------------------------------------------
# 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
@pytest.mark.filterwarnings("ignore:Parquet format '2.0':FutureWarning")
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_timestamp_as_object_fixed_offset():
    # ARROW-16547 to_pandas with timestamp_as_object=True and FixedOffset
    pytz = pytest.importorskip("pytz")
    import datetime
    timezone = pytz.FixedOffset(120)
    dt = timezone.localize(datetime.datetime(2022, 5, 12, 16, 57))

    table = pa.table({"timestamp_col": pa.array([dt])})
    result = table.to_pandas(timestamp_as_object=True)
    assert pa.table(result) == table


def test_threaded_pandas_import():
    invoke_script("pandas_threaded_import.py")


def test_does_not_mutate_timedelta_dtype():
    expected = np.dtype('m8')

    assert np.dtype(np.timedelta64) == expected

    df = pd.DataFrame({"a": [np.timedelta64()]})
    t = pa.Table.from_pandas(df)
    t.to_pandas()

    assert np.dtype(np.timedelta64) == expected


def test_does_not_mutate_timedelta_nested():
    # ARROW-17893: dataframe with timedelta and a list of dictionary
    # also with timedelta produces wrong result with to_pandas

    from datetime import timedelta
    timedelta_1 = [{"timedelta_1": timedelta(seconds=12, microseconds=1)}]
    timedelta_2 = [timedelta(hours=3, minutes=40, seconds=23)]
    table = pa.table({"timedelta_1": timedelta_1, "timedelta_2": timedelta_2})
    df = table.to_pandas()

    assert df["timedelta_2"][0].to_pytimedelta() == timedelta_2[0]


def test_roundtrip_nested_map_table_with_pydicts():
    schema = pa.schema([
        pa.field(
            "a",
            pa.list_(
                pa.map_(pa.int8(), pa.struct([pa.field("b", pa.binary())]))
            )
        )
    ])
    table = pa.table([[
        [[(1, None)]],
        None,
        [
            [(2, {"b": b"abc"})],
            [(3, {"b": None}), (4, {"b": b"def"})],
        ]
    ]],
        schema=schema,
    )

    expected_default_df = pd.DataFrame(
        {"a": [[[(1, None)]], None, [[(2, {"b": b"abc"})],
                                     [(3, {"b": None}), (4, {"b": b"def"})]]]}
    )
    expected_as_pydicts_df = pd.DataFrame(
        {"a": [
            [{1: None}],
            None,
            [{2: {"b": b"abc"}}, {3: {"b": None}, 4: {"b": b"def"}}],
        ]}
    )

    default_df = table.to_pandas()
    as_pydicts_df = table.to_pandas(maps_as_pydicts="strict")

    tm.assert_frame_equal(default_df, expected_default_df)
    tm.assert_frame_equal(as_pydicts_df, expected_as_pydicts_df)

    table_default_roundtrip = pa.Table.from_pandas(default_df, schema=schema)
    assert table.equals(table_default_roundtrip)

    table_as_pydicts_roundtrip = pa.Table.from_pandas(as_pydicts_df, schema=schema)
    assert table.equals(table_as_pydicts_roundtrip)


def test_roundtrip_nested_map_array_with_pydicts_sliced():
    """
    Slightly more robust test with chunking and slicing
    """
    keys_1 = pa.array(['foo', 'bar'])
    keys_2 = pa.array(['baz', 'qux', 'quux', 'quz'])
    keys_3 = pa.array([], pa.string())

    items_1 = pa.array(
        [['a', 'b'], ['c', 'd']],
        pa.list_(pa.string()),
    )
    items_2 = pa.array(
        [[], None, [None, 'e'], ['f', 'g']],
        pa.list_(pa.string()),
    )
    items_3 = pa.array(
        [],
        pa.list_(pa.string()),
    )

    map_chunk_1 = pa.MapArray.from_arrays([0, 2], keys_1, items_1)
    map_chunk_2 = pa.MapArray.from_arrays([0, 3, 4], keys_2, items_2)
    map_chunk_3 = pa.MapArray.from_arrays([0, 0], keys_3, items_3)
    chunked_array = pa.chunked_array([
        pa.ListArray.from_arrays([0, 1], map_chunk_1).slice(0),
        pa.ListArray.from_arrays([0, 1], map_chunk_2.slice(1)).slice(0),
        pa.ListArray.from_arrays([0, 0], map_chunk_3).slice(0),
    ])

    series_default = chunked_array.to_pandas()
    expected_series_default = pd.Series([
        [[('foo', ['a', 'b']), ('bar', ['c', 'd'])]],
        [[('quz', ['f', 'g'])]],
        [],
    ])

    series_pydicts = chunked_array.to_pandas(maps_as_pydicts="strict")
    expected_series_pydicts = pd.Series([
        [{'foo': ['a', 'b'], 'bar': ['c', 'd']}],
        [{'quz': ['f', 'g']}],
        [],
    ])

    sliced = chunked_array.slice(1, 3)
    series_default_sliced = sliced.to_pandas()
    expected_series_default_sliced = pd.Series([
        [[('quz', ['f', 'g'])]],
        [],
    ])

    series_pydicts_sliced = sliced.to_pandas(maps_as_pydicts="strict")
    expected_series_pydicts_sliced = pd.Series([
        [{'quz': ['f', 'g']}],
        [],
    ])

    with warnings.catch_warnings():
        warnings.filterwarnings("ignore", "elementwise comparison failed",
                                DeprecationWarning)
        tm.assert_series_equal(series_default, expected_series_default)
        tm.assert_series_equal(series_pydicts, expected_series_pydicts)
        tm.assert_series_equal(series_default_sliced, expected_series_default_sliced)
        tm.assert_series_equal(series_pydicts_sliced, expected_series_pydicts_sliced)

    ty = pa.list_(pa.map_(pa.string(), pa.list_(pa.string())))

    def assert_roundtrip(series: pd.Series, data) -> None:
        array_roundtrip = pa.chunked_array(pa.Array.from_pandas(series, type=ty))
        array_roundtrip.validate(full=True)
        assert data.equals(array_roundtrip)

    assert_roundtrip(series_default, chunked_array)
    assert_roundtrip(series_pydicts, chunked_array)
    assert_roundtrip(series_default_sliced, sliced)
    assert_roundtrip(series_pydicts_sliced, sliced)


def test_roundtrip_map_array_with_pydicts_duplicate_keys():
    keys = pa.array(['foo', 'bar', 'foo'])
    items = pa.array(
        [['a', 'b'], ['c', 'd'], ['1', '2']],
        pa.list_(pa.string()),
    )
    offsets = [0, 3]
    maps = pa.MapArray.from_arrays(offsets, keys, items)
    ty = pa.map_(pa.string(), pa.list_(pa.string()))

    # ------------------------
    # With maps as pydicts
    with pytest.raises(pa.lib.ArrowException):
        # raises because of duplicate keys
        maps.to_pandas(maps_as_pydicts="strict")
    series_pydicts = maps.to_pandas(maps_as_pydicts="lossy")
    # some data loss occurs for duplicate keys
    expected_series_pydicts = pd.Series([
        {'foo': ['1', '2'], 'bar': ['c', 'd']},
    ])
    # roundtrip is not possible because of data loss
    assert not maps.equals(pa.Array.from_pandas(series_pydicts, type=ty))

    # ------------------------
    # With default assoc list of tuples
    series_default = maps.to_pandas()
    expected_series_default = pd.Series([
        [('foo', ['a', 'b']), ('bar', ['c', 'd']), ('foo', ['1', '2'])],
    ])
    assert maps.equals(pa.Array.from_pandas(series_default, type=ty))

    # custom comparison for compatibility w/ Pandas 1.0.0
    # would otherwise run:
    #   tm.assert_series_equal(series_pydicts, expected_series_pydicts)
    assert len(series_pydicts) == len(expected_series_pydicts)
    for row1, row2 in zip(series_pydicts, expected_series_pydicts):
        assert len(row1) == len(row2)
        for tup1, tup2 in zip(row1.items(), row2.items()):
            assert tup1[0] == tup2[0]
            assert np.array_equal(tup1[1], tup2[1])

    # custom comparison for compatibility w/ Pandas 1.0.0
    # would otherwise run:
    #   tm.assert_series_equal(series_default, expected_series_default)
    assert len(series_default) == len(expected_series_default)
    for row1, row2 in zip(series_default, expected_series_default):
        assert len(row1) == len(row2)
        for tup1, tup2 in zip(row1, row2):
            assert tup1[0] == tup2[0]
            assert np.array_equal(tup1[1], tup2[1])


def test_unhashable_map_keys_with_pydicts():
    keys = pa.array(
        [['a', 'b'], ['c', 'd'], [], ['e'], [None, 'f'], ['g', 'h']],
        pa.list_(pa.string()),
    )
    items = pa.array(['foo', 'bar', 'baz', 'qux', 'quux', 'quz'])
    offsets = [0, 2, 6]
    maps = pa.MapArray.from_arrays(offsets, keys, items)

    # ------------------------
    # With maps as pydicts
    with pytest.raises(TypeError):
        maps.to_pandas(maps_as_pydicts="lossy")

    # ------------------------
    # With default assoc list of tuples
    series = maps.to_pandas()
    expected_series_default = pd.Series([
        [(['a', 'b'], 'foo'), (['c', 'd'], 'bar')],
        [([], 'baz'), (['e'], 'qux'), ([None, 'f'], 'quux'), (['g', 'h'], 'quz')],
    ])

    # custom comparison for compatibility w/ Pandas 1.0.0
    # would otherwise run:
    #   tm.assert_series_equal(series, expected_series_default)
    assert len(series) == len(expected_series_default)
    for row1, row2 in zip(series, expected_series_default):
        assert len(row1) == len(row2)
        for tup1, tup2 in zip(row1, row2):
            assert np.array_equal(tup1[0], tup2[0])
            assert tup1[1] == tup2[1]


def test_table_column_conversion_for_datetime():
    # GH-35235
    # pandas implemented __from_arrow__ for DatetimeTZDtype,
    # but we choose to do the conversion in Arrow instead.
    # https://github.com/pandas-dev/pandas/pull/52201
    series = pd.Series(pd.date_range("2012", periods=2, tz="Europe/Brussels"),
                       name="datetime_column")
    table = pa.table({"datetime_column": pa.array(series)})
    table_col = table.column("datetime_column")

    result = table_col.to_pandas()
    assert result.name == "datetime_column"
    tm.assert_series_equal(result, series)


def test_array_conversion_for_datetime():
    # GH-35235
    # pandas implemented __from_arrow__ for DatetimeTZDtype,
    # but we choose to do the conversion in Arrow instead.
    # https://github.com/pandas-dev/pandas/pull/52201
    series = pd.Series(pd.date_range("2012", periods=2, tz="Europe/Brussels"))
    arr = pa.array(series)

    result = arr.to_pandas()
    tm.assert_series_equal(result, series)


@pytest.mark.large_memory
def test_nested_chunking_valid():
    # GH-32439: Chunking can cause arrays to be in invalid state
    # when nested types are involved.
    # Here we simply ensure we validate correctly.

    def roundtrip(df, schema=None):
        tab = pa.Table.from_pandas(df, schema=schema)
        tab.validate(full=True)
        # we expect to trigger chunking internally
        # an assertion failure here may just mean this threshold has changed
        num_chunks = tab.column(0).num_chunks
        assert num_chunks > 1
        tm.assert_frame_equal(tab.to_pandas(self_destruct=True,
                                            maps_as_pydicts="strict"), df)

    x = b"0" * 720000000
    roundtrip(pd.DataFrame({"strings": [x, x, x]}))

    struct = {"struct_field": x}
    roundtrip(pd.DataFrame({"structs": [struct, struct, struct]}))

    lists = [x]
    roundtrip(pd.DataFrame({"lists": [lists, lists, lists]}))

    los = [struct]
    roundtrip(pd.DataFrame({"los": [los, los, los]}))

    sol = {"struct_field": lists}
    roundtrip(pd.DataFrame({"sol": [sol, sol, sol]}))

    map_of_los = {"a": los}
    map_type = pa.map_(pa.string(),
                       pa.list_(pa.struct([("struct_field", pa.binary())])))
    schema = pa.schema([("maps", map_type)])
    roundtrip(pd.DataFrame({"maps": [map_of_los, map_of_los, map_of_los]}),
              schema=schema)


def test_is_data_frame_race_condition():
    # See https://github.com/apache/arrow/issues/39313
    test_util.invoke_script('arrow_39313.py')