test_table.py

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

from collections import OrderedDict
from collections.abc import Iterable
import sys
import weakref

try:
    import numpy as np
except ImportError:
    np = None
import pytest
import pyarrow as pa
import pyarrow.compute as pc
from pyarrow.interchange import from_dataframe
from pyarrow.vendored.version import Version


def test_chunked_array_basics():
    data = pa.chunked_array([], type=pa.string())
    assert data.type == pa.string()
    assert data.to_pylist() == []
    data.validate()

    data2 = pa.chunked_array([], type='binary')
    assert data2.type == pa.binary()

    with pytest.raises(ValueError):
        pa.chunked_array([])

    data = pa.chunked_array([
        [1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]
    ])
    assert isinstance(data.chunks, list)
    assert all(isinstance(c, pa.lib.Int64Array) for c in data.chunks)
    assert all(isinstance(c, pa.lib.Int64Array) for c in data.iterchunks())
    assert len(data.chunks) == 3
    assert data.get_total_buffer_size() == sum(c.get_total_buffer_size()
                                               for c in data.iterchunks())
    assert sys.getsizeof(data) >= object.__sizeof__(
        data) + data.get_total_buffer_size()
    assert data.nbytes == 3 * 3 * 8  # 3 items per 3 lists with int64 size(8)
    data.validate()

    wr = weakref.ref(data)
    assert wr() is not None
    del data
    assert wr() is None


def test_chunked_array_construction():
    arr = pa.chunked_array([
        [1, 2, 3],
        [4, 5, 6],
        [7, 8, 9],
    ])
    assert arr.type == pa.int64()
    assert len(arr) == 9
    assert len(arr.chunks) == 3

    arr = pa.chunked_array([
        [1, 2, 3],
        [4., 5., 6.],
        [7, 8, 9],
    ])
    assert arr.type == pa.int64()
    assert len(arr) == 9
    assert len(arr.chunks) == 3

    arr = pa.chunked_array([
        [1, 2, 3],
        [4., 5., 6.],
        [7, 8, 9],
    ], type=pa.int8())
    assert arr.type == pa.int8()
    assert len(arr) == 9
    assert len(arr.chunks) == 3

    arr = pa.chunked_array([
        [1, 2, 3],
        []
    ])
    assert arr.type == pa.int64()
    assert len(arr) == 3
    assert len(arr.chunks) == 2

    msg = "cannot construct ChunkedArray from empty vector and omitted type"
    with pytest.raises(ValueError, match=msg):
        assert pa.chunked_array([])

    assert pa.chunked_array([], type=pa.string()).type == pa.string()
    assert pa.chunked_array([[]]).type == pa.null()
    assert pa.chunked_array([[]], type=pa.string()).type == pa.string()


def test_combine_chunks():
    # ARROW-77363
    arr = pa.array([1, 2])
    chunked_arr = pa.chunked_array([arr, arr])
    res = chunked_arr.combine_chunks()
    expected = pa.array([1, 2, 1, 2])
    assert res.equals(expected)


def test_chunked_array_can_combine_chunks_with_no_chunks():
    # https://issues.apache.org/jira/browse/ARROW-17256
    assert pa.chunked_array([], type=pa.bool_()).combine_chunks() == pa.array(
        [], type=pa.bool_()
    )
    assert pa.chunked_array(
        [pa.array([], type=pa.bool_())], type=pa.bool_()
    ).combine_chunks() == pa.array([], type=pa.bool_())


@pytest.mark.numpy
def test_chunked_array_to_numpy():
    data = pa.chunked_array([
        [1, 2, 3],
        [4, 5, 6],
        []
    ])
    arr1 = np.asarray(data)
    arr2 = data.to_numpy()

    assert isinstance(arr2, np.ndarray)
    assert arr2.shape == (6,)
    assert np.array_equal(arr1, arr2)


def test_chunked_array_mismatch_types():
    msg = "chunks must all be same type"
    with pytest.raises(TypeError, match=msg):
        # Given array types are different
        pa.chunked_array([
            pa.array([1, 2, 3]),
            pa.array([1., 2., 3.])
        ])

    with pytest.raises(TypeError, match=msg):
        # Given array type is different from explicit type argument
        pa.chunked_array([pa.array([1, 2, 3])], type=pa.float64())


def test_chunked_array_str():
    data = [
        pa.array([1, 2, 3]),
        pa.array([4, 5, 6])
    ]
    data = pa.chunked_array(data)
    assert str(data) == """[
  [
    1,
    2,
    3
  ],
  [
    4,
    5,
    6
  ]
]"""


@pytest.mark.numpy
def test_chunked_array_getitem():
    data = [
        pa.array([1, 2, 3]),
        pa.array([4, 5, 6])
    ]
    data = pa.chunked_array(data)
    assert data[1].as_py() == 2
    assert data[-1].as_py() == 6
    assert data[-6].as_py() == 1
    with pytest.raises(IndexError):
        data[6]
    with pytest.raises(IndexError):
        data[-7]
    # Ensure this works with numpy scalars
    assert data[np.int32(1)].as_py() == 2

    data_slice = data[2:4]
    assert data_slice.to_pylist() == [3, 4]

    data_slice = data[4:-1]
    assert data_slice.to_pylist() == [5]

    data_slice = data[99:99]
    assert data_slice.type == data.type
    assert data_slice.to_pylist() == []


def test_chunked_array_slice():
    data = [
        pa.array([1, 2, 3]),
        pa.array([4, 5, 6])
    ]
    data = pa.chunked_array(data)

    data_slice = data.slice(len(data))
    assert data_slice.type == data.type
    assert data_slice.to_pylist() == []

    data_slice = data.slice(len(data) + 10)
    assert data_slice.type == data.type
    assert data_slice.to_pylist() == []

    table = pa.Table.from_arrays([data], names=["a"])
    table_slice = table.slice(len(table))
    assert len(table_slice) == 0

    table = pa.Table.from_arrays([data], names=["a"])
    table_slice = table.slice(len(table) + 10)
    assert len(table_slice) == 0


def test_chunked_array_iter():
    data = [
        pa.array([0]),
        pa.array([1, 2, 3]),
        pa.array([4, 5, 6]),
        pa.array([7, 8, 9])
    ]
    arr = pa.chunked_array(data)

    for i, j in zip(range(10), arr):
        assert i == j.as_py()

    assert isinstance(arr, Iterable)


def test_chunked_array_equals():
    def eq(xarrs, yarrs):
        if isinstance(xarrs, pa.ChunkedArray):
            x = xarrs
        else:
            x = pa.chunked_array(xarrs)
        if isinstance(yarrs, pa.ChunkedArray):
            y = yarrs
        else:
            y = pa.chunked_array(yarrs)
        assert x.equals(y)
        assert y.equals(x)
        assert x == y
        assert x != str(y)

    def ne(xarrs, yarrs):
        if isinstance(xarrs, pa.ChunkedArray):
            x = xarrs
        else:
            x = pa.chunked_array(xarrs)
        if isinstance(yarrs, pa.ChunkedArray):
            y = yarrs
        else:
            y = pa.chunked_array(yarrs)
        assert not x.equals(y)
        assert not y.equals(x)
        assert x != y

    eq(pa.chunked_array([], type=pa.int32()),
       pa.chunked_array([], type=pa.int32()))
    ne(pa.chunked_array([], type=pa.int32()),
       pa.chunked_array([], type=pa.int64()))

    a = pa.array([0, 2], type=pa.int32())
    b = pa.array([0, 2], type=pa.int64())
    c = pa.array([0, 3], type=pa.int32())
    d = pa.array([0, 2, 0, 3], type=pa.int32())

    eq([a], [a])
    ne([a], [b])
    eq([a, c], [a, c])
    eq([a, c], [d])
    ne([c, a], [a, c])

    # ARROW-4822
    assert not pa.chunked_array([], type=pa.int32()).equals(None)


@pytest.mark.parametrize(
    ('data', 'typ'),
    [
        ([True, False, True, True], pa.bool_()),
        ([1, 2, 4, 6], pa.int64()),
        ([1.0, 2.5, None], pa.float64()),
        (['a', None, 'b'], pa.string()),
        ([], pa.list_(pa.uint8())),
        ([[1, 2], [3]], pa.list_(pa.int64())),
        ([['a'], None, ['b', 'c']], pa.list_(pa.string())),
        ([(1, 'a'), (2, 'c'), None],
            pa.struct([pa.field('a', pa.int64()), pa.field('b', pa.string())]))
    ]
)
def test_chunked_array_pickle(data, typ, pickle_module):
    arrays = []
    while data:
        arrays.append(pa.array(data[:2], type=typ))
        data = data[2:]
    array = pa.chunked_array(arrays, type=typ)
    array.validate()
    result = pickle_module.loads(pickle_module.dumps(array))
    result.validate()
    assert result.equals(array)


@pytest.mark.pandas
def test_chunked_array_to_pandas():
    import pandas as pd

    data = [
        pa.array([-10, -5, 0, 5, 10])
    ]
    table = pa.table(data, names=['a'])
    col = table.column(0)
    assert isinstance(col, pa.ChunkedArray)
    series = col.to_pandas()
    assert isinstance(series, pd.Series)
    assert series.shape == (5,)
    assert series[0] == -10
    assert series.name == 'a'


@pytest.mark.pandas
def test_chunked_array_to_pandas_preserve_name():
    # https://issues.apache.org/jira/browse/ARROW-7709
    import pandas as pd
    import pandas.testing as tm

    for data in [
            pa.array([1, 2, 3]),
            pa.array(pd.Categorical(["a", "b", "a"])),
            pa.array(pd.date_range("2012", periods=3)),
            pa.array(pd.date_range("2012", periods=3, tz="Europe/Brussels")),
            pa.array([1, 2, 3], pa.timestamp("ms")),
            pa.array([1, 2, 3], pa.timestamp("ms", "Europe/Brussels"))]:
        table = pa.table({"name": data})
        result = table.column("name").to_pandas()
        assert result.name == "name"
        expected = pd.Series(data.to_pandas(), name="name")
        tm.assert_series_equal(result, expected)


@pytest.mark.pandas
def test_table_roundtrip_to_pandas_empty_dataframe():
    # https://issues.apache.org/jira/browse/ARROW-10643
    # The conversion should not results in a table with 0 rows if the original
    # DataFrame has a RangeIndex but is empty.
    import pandas as pd

    data = pd.DataFrame(index=pd.RangeIndex(0, 10, 1))
    table = pa.table(data)
    result = table.to_pandas()

    assert table.num_rows == 10
    assert data.shape == (10, 0)
    assert result.shape == (10, 0)
    assert result.index.equals(data.index)

    data = pd.DataFrame(index=pd.RangeIndex(0, 10, 3))
    table = pa.table(data)
    result = table.to_pandas()

    assert table.num_rows == 4
    assert data.shape == (4, 0)
    assert result.shape == (4, 0)
    assert result.index.equals(data.index)


@pytest.mark.pandas
def test_recordbatch_roundtrip_to_pandas_empty_dataframe():
    # https://issues.apache.org/jira/browse/ARROW-10643
    # The conversion should not results in a RecordBatch with 0 rows if
    #  the original DataFrame has a RangeIndex but is empty.
    import pandas as pd

    data = pd.DataFrame(index=pd.RangeIndex(0, 10, 1))
    batch = pa.RecordBatch.from_pandas(data)
    result = batch.to_pandas()

    assert batch.num_rows == 10
    assert data.shape == (10, 0)
    assert result.shape == (10, 0)
    assert result.index.equals(data.index)

    data = pd.DataFrame(index=pd.RangeIndex(0, 10, 3))
    batch = pa.RecordBatch.from_pandas(data)
    result = batch.to_pandas()

    assert batch.num_rows == 4
    assert data.shape == (4, 0)
    assert result.shape == (4, 0)
    assert result.index.equals(data.index)


@pytest.mark.pandas
def test_to_pandas_empty_table():
    # https://issues.apache.org/jira/browse/ARROW-15370
    import pandas as pd
    import pandas.testing as tm

    df = pd.DataFrame({'a': [1, 2], 'b': [0.1, 0.2]})
    table = pa.table(df)
    result = table.schema.empty_table().to_pandas()
    assert result.shape == (0, 2)
    tm.assert_frame_equal(result, df.iloc[:0])


@pytest.mark.pandas
@pytest.mark.nopandas
def test_chunked_array_asarray():
    # ensure this is tested both when pandas is present or not (ARROW-6564)

    data = [
        pa.array([0]),
        pa.array([1, 2, 3])
    ]
    chunked_arr = pa.chunked_array(data)

    np_arr = np.asarray(chunked_arr)
    assert np_arr.tolist() == [0, 1, 2, 3]
    assert np_arr.dtype == np.dtype('int64')

    # An optional type can be specified when calling np.asarray
    np_arr = np.asarray(chunked_arr, dtype='str')
    assert np_arr.tolist() == ['0', '1', '2', '3']

    # Types are modified when there are nulls
    data = [
        pa.array([1, None]),
        pa.array([1, 2, 3])
    ]
    chunked_arr = pa.chunked_array(data)

    np_arr = np.asarray(chunked_arr)
    elements = np_arr.tolist()
    assert elements[0] == 1.
    assert np.isnan(elements[1])
    assert elements[2:] == [1., 2., 3.]
    assert np_arr.dtype == np.dtype('float64')

    # DictionaryType data will be converted to dense numpy array
    arr = pa.DictionaryArray.from_arrays(
        pa.array([0, 1, 2, 0, 1]), pa.array(['a', 'b', 'c']))
    chunked_arr = pa.chunked_array([arr, arr])
    np_arr = np.asarray(chunked_arr)
    assert np_arr.dtype == np.dtype('object')
    assert np_arr.tolist() == ['a', 'b', 'c', 'a', 'b'] * 2


def test_chunked_array_flatten():
    ty = pa.struct([pa.field('x', pa.int16()),
                    pa.field('y', pa.float32())])
    a = pa.array([(1, 2.5), (3, 4.5), (5, 6.5)], type=ty)
    carr = pa.chunked_array(a)
    x, y = carr.flatten()
    assert x.equals(pa.chunked_array(pa.array([1, 3, 5], type=pa.int16())))
    assert y.equals(pa.chunked_array(pa.array([2.5, 4.5, 6.5],
                                              type=pa.float32())))

    # Empty column
    a = pa.array([], type=ty)
    carr = pa.chunked_array(a)
    x, y = carr.flatten()
    assert x.equals(pa.chunked_array(pa.array([], type=pa.int16())))
    assert y.equals(pa.chunked_array(pa.array([], type=pa.float32())))


def test_chunked_array_unify_dictionaries():
    arr = pa.chunked_array([
        pa.array(["foo", "bar", None, "foo"]).dictionary_encode(),
        pa.array(["quux", None, "foo"]).dictionary_encode(),
    ])
    assert arr.chunk(0).dictionary.equals(pa.array(["foo", "bar"]))
    assert arr.chunk(1).dictionary.equals(pa.array(["quux", "foo"]))
    arr = arr.unify_dictionaries()
    expected_dict = pa.array(["foo", "bar", "quux"])
    assert arr.chunk(0).dictionary.equals(expected_dict)
    assert arr.chunk(1).dictionary.equals(expected_dict)
    assert arr.to_pylist() == ["foo", "bar", None, "foo", "quux", None, "foo"]


def test_recordbatch_dunder_init():
    with pytest.raises(TypeError, match='RecordBatch'):
        pa.RecordBatch()


def test_chunked_array_c_array_interface():
    class ArrayWrapper:
        def __init__(self, array):
            self.array = array

        def __arrow_c_array__(self, requested_schema=None):
            return self.array.__arrow_c_array__(requested_schema)

    data = pa.array([1, 2, 3], pa.int64())
    chunked = pa.chunked_array([data])
    wrapper = ArrayWrapper(data)

    # Can roundtrip through the wrapper.
    result = pa.chunked_array(wrapper)
    assert result == chunked

    # Can also import with a type that implementer can cast to.
    result = pa.chunked_array(wrapper, type=pa.int16())
    assert result == chunked.cast(pa.int16())


def test_chunked_array_c_stream_interface():
    class ChunkedArrayWrapper:
        def __init__(self, chunked):
            self.chunked = chunked

        def __arrow_c_stream__(self, requested_schema=None):
            return self.chunked.__arrow_c_stream__(requested_schema)

    data = pa.chunked_array([[1, 2, 3], [4, None, 6]])
    wrapper = ChunkedArrayWrapper(data)

    # Can roundtrip through the wrapper.
    result = pa.chunked_array(wrapper)
    assert result == data

    # Can also import with a type that implementer can cast to.
    result = pa.chunked_array(wrapper, type=pa.int16())
    assert result == data.cast(pa.int16())


class BatchWrapper:
    def __init__(self, batch):
        self.batch = batch

    def __arrow_c_array__(self, requested_schema=None):
        return self.batch.__arrow_c_array__(requested_schema)


class BatchDeviceWrapper:
    def __init__(self, batch):
        self.batch = batch

    def __arrow_c_device_array__(self, requested_schema=None, **kwargs):
        return self.batch.__arrow_c_device_array__(requested_schema, **kwargs)


@pytest.mark.parametrize("wrapper_class", [BatchWrapper, BatchDeviceWrapper])
def test_recordbatch_c_array_interface(wrapper_class):
    data = pa.record_batch([
        pa.array([1, 2, 3], type=pa.int64())
    ], names=['a'])
    wrapper = wrapper_class(data)

    # Can roundtrip through the wrapper.
    result = pa.record_batch(wrapper)
    assert result == data

    # Can also import with a schema that implementer can cast to.
    castable_schema = pa.schema([
        pa.field('a', pa.int32())
    ])
    result = pa.record_batch(wrapper, schema=castable_schema)
    expected = pa.record_batch([
        pa.array([1, 2, 3], type=pa.int32())
    ], names=['a'])
    assert result == expected


def test_recordbatch_c_array_interface_device_unsupported_keyword():
    # For the device-aware version, we raise a specific error for unsupported keywords
    data = pa.record_batch(
        [pa.array([1, 2, 3], type=pa.int64())], names=['a']
    )

    with pytest.raises(
        NotImplementedError,
        match=r"Received unsupported keyword argument\(s\): \['other'\]"
    ):
        data.__arrow_c_device_array__(other="not-none")

    # but with None value it is ignored
    _ = data.__arrow_c_device_array__(other=None)


@pytest.mark.parametrize("wrapper_class", [BatchWrapper, BatchDeviceWrapper])
def test_table_c_array_interface(wrapper_class):
    data = pa.record_batch([
        pa.array([1, 2, 3], type=pa.int64())
    ], names=['a'])
    wrapper = wrapper_class(data)

    # Can roundtrip through the wrapper.
    result = pa.table(wrapper)
    expected = pa.Table.from_batches([data])
    assert result == expected

    # Can also import with a schema that implementer can cast to.
    castable_schema = pa.schema([
        pa.field('a', pa.int32())
    ])
    result = pa.table(wrapper, schema=castable_schema)
    expected = pa.table({
        'a': pa.array([1, 2, 3], type=pa.int32())
    })
    assert result == expected


def test_table_c_stream_interface():
    class StreamWrapper:
        def __init__(self, batches):
            self.batches = batches

        def __arrow_c_stream__(self, requested_schema=None):
            reader = pa.RecordBatchReader.from_batches(
                self.batches[0].schema, self.batches)
            return reader.__arrow_c_stream__(requested_schema)

    data = [
        pa.record_batch([pa.array([1, 2, 3], type=pa.int64())], names=['a']),
        pa.record_batch([pa.array([4, 5, 6], type=pa.int64())], names=['a'])
    ]
    wrapper = StreamWrapper(data)

    # Can roundtrip through the wrapper.
    result = pa.table(wrapper)
    expected = pa.Table.from_batches(data)
    assert result == expected

    # Passing schema works if already that schema
    result = pa.table(wrapper, schema=data[0].schema)
    assert result == expected

    # Passing a different schema will cast
    good_schema = pa.schema([pa.field('a', pa.int32())])
    result = pa.table(wrapper, schema=good_schema)
    assert result == expected.cast(good_schema)

    # If schema doesn't match, raises NotImplementedError
    with pytest.raises(
        pa.lib.ArrowTypeError, match="Field 0 cannot be cast"
    ):
        pa.table(
            wrapper, schema=pa.schema([pa.field('a', pa.list_(pa.int32()))])
        )


def test_recordbatch_itercolumns():
    data = [
        pa.array(range(5), type='int16'),
        pa.array([-10, -5, 0, None, 10], type='int32')
    ]
    batch = pa.record_batch(data, ['c0', 'c1'])

    columns = []
    for col in batch.itercolumns():
        columns.append(col)

    assert batch.columns == columns
    assert batch == pa.record_batch(columns, names=batch.column_names)
    assert batch != pa.record_batch(columns[1:], names=batch.column_names[1:])
    assert batch != columns


def test_recordbatch_equals():
    data1 = [
        pa.array(range(5), type='int16'),
        pa.array([-10, -5, 0, None, 10], type='int32')
    ]
    data2 = [
        pa.array(['a', 'b', 'c']),
        pa.array([['d'], ['e'], ['f']]),
    ]
    column_names = ['c0', 'c1']

    batch = pa.record_batch(data1, column_names)
    assert batch == pa.record_batch(data1, column_names)
    assert batch.equals(pa.record_batch(data1, column_names))

    assert batch != pa.record_batch(data2, column_names)
    assert not batch.equals(pa.record_batch(data2, column_names))

    batch_meta = pa.record_batch(data1, names=column_names,
                                 metadata={'key': 'value'})
    assert batch_meta.equals(batch)
    assert not batch_meta.equals(batch, check_metadata=True)

    # ARROW-8889
    assert not batch.equals(None)
    assert batch != "foo"


def test_recordbatch_take():
    batch = pa.record_batch(
        [pa.array([1, 2, 3, None, 5]),
         pa.array(['a', 'b', 'c', 'd', 'e'])],
        ['f1', 'f2'])
    assert batch.take(pa.array([2, 3])).equals(batch.slice(2, 2))
    assert batch.take(pa.array([2, None])).equals(
        pa.record_batch([pa.array([3, None]), pa.array(['c', None])],
                        ['f1', 'f2']))


def test_recordbatch_column_sets_private_name():
    # ARROW-6429
    rb = pa.record_batch([pa.array([1, 2, 3, 4])], names=['a0'])
    assert rb[0]._name == 'a0'


def test_recordbatch_from_arrays_validate_schema():
    # ARROW-6263
    arr = pa.array([1, 2])
    schema = pa.schema([pa.field('f0', pa.list_(pa.utf8()))])
    with pytest.raises(NotImplementedError):
        pa.record_batch([arr], schema=schema)


def test_recordbatch_from_arrays_validate_lengths():
    # ARROW-2820
    data = [pa.array([1]), pa.array(["tokyo", "like", "happy"]),
            pa.array(["derek"])]

    with pytest.raises(ValueError):
        pa.record_batch(data, ['id', 'tags', 'name'])


def test_recordbatch_no_fields():
    batch = pa.record_batch([], [])

    assert len(batch) == 0
    assert batch.num_rows == 0
    assert batch.num_columns == 0


def test_recordbatch_from_arrays_invalid_names():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10])
    ]
    with pytest.raises(ValueError):
        pa.record_batch(data, names=['a', 'b', 'c'])

    with pytest.raises(ValueError):
        pa.record_batch(data, names=['a'])


def test_recordbatch_empty_metadata():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10])
    ]

    batch = pa.record_batch(data, ['c0', 'c1'])
    assert batch.schema.metadata is None


def test_recordbatch_pickle(pickle_module):
    data = [
        pa.array(range(5), type='int8'),
        pa.array([-10, -5, 0, 5, 10], type='float32')
    ]
    fields = [
        pa.field('ints', pa.int8()),
        pa.field('floats', pa.float32()),
    ]
    schema = pa.schema(fields, metadata={b'foo': b'bar'})
    batch = pa.record_batch(data, schema=schema)

    result = pickle_module.loads(pickle_module.dumps(batch))
    assert result.equals(batch)
    assert result.schema == schema


def test_recordbatch_get_field():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    batch = pa.RecordBatch.from_arrays(data, names=('a', 'b', 'c'))

    assert batch.field('a').equals(batch.schema.field('a'))
    assert batch.field(0).equals(batch.schema.field('a'))

    with pytest.raises(KeyError):
        batch.field('d')

    with pytest.raises(TypeError):
        batch.field(None)

    with pytest.raises(IndexError):
        batch.field(4)


def test_recordbatch_select_column():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    batch = pa.RecordBatch.from_arrays(data, names=('a', 'b', 'c'))

    assert batch.column('a').equals(batch.column(0))

    with pytest.raises(
            KeyError, match='Field "d" does not exist in schema'):
        batch.column('d')

    with pytest.raises(TypeError):
        batch.column(None)

    with pytest.raises(IndexError):
        batch.column(4)


def test_recordbatch_select():
    a1 = pa.array([1, 2, 3, None, 5])
    a2 = pa.array(['a', 'b', 'c', 'd', 'e'])
    a3 = pa.array([[1, 2], [3, 4], [5, 6], None, [9, 10]])
    batch = pa.record_batch([a1, a2, a3], ['f1', 'f2', 'f3'])

    # selecting with string names
    result = batch.select(['f1'])
    expected = pa.record_batch([a1], ['f1'])
    assert result.equals(expected)

    result = batch.select(['f3', 'f2'])
    expected = pa.record_batch([a3, a2], ['f3', 'f2'])
    assert result.equals(expected)

    # selecting with integer indices
    result = batch.select([0])
    expected = pa.record_batch([a1], ['f1'])
    assert result.equals(expected)

    result = batch.select([2, 1])
    expected = pa.record_batch([a3, a2], ['f3', 'f2'])
    assert result.equals(expected)

    # preserve metadata
    batch2 = batch.replace_schema_metadata({"a": "test"})
    result = batch2.select(["f1", "f2"])
    assert b"a" in result.schema.metadata

    # selecting non-existing column raises
    with pytest.raises(KeyError, match='Field "f5" does not exist'):
        batch.select(['f5'])

    with pytest.raises(IndexError, match="index out of bounds"):
        batch.select([5])

    # duplicate selection gives duplicated names in resulting recordbatch
    result = batch.select(['f2', 'f2'])
    expected = pa.record_batch([a2, a2], ['f2', 'f2'])
    assert result.equals(expected)

    # selection duplicated column raises
    batch = pa.record_batch([a1, a2, a3], ['f1', 'f2', 'f1'])
    with pytest.raises(KeyError, match='Field "f1" exists 2 times'):
        batch.select(['f1'])

    result = batch.select(['f2'])
    expected = pa.record_batch([a2], ['f2'])
    assert result.equals(expected)


def test_recordbatch_from_struct_array_invalid():
    with pytest.raises(TypeError):
        pa.RecordBatch.from_struct_array(pa.array(range(5)))


def test_recordbatch_from_struct_array():
    struct_array = pa.array(
        [{"ints": 1}, {"floats": 1.0}],
        type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
    )
    result = pa.RecordBatch.from_struct_array(struct_array)
    assert result.equals(pa.RecordBatch.from_arrays(
        [
            pa.array([1, None], type=pa.int32()),
            pa.array([None, 1.0], type=pa.float32()),
        ], ["ints", "floats"]
    ))


def test_recordbatch_to_struct_array():
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array([1, None], type=pa.int32()),
            pa.array([None, 1.0], type=pa.float32()),
        ], ["ints", "floats"]
    )
    result = batch.to_struct_array()
    assert result.equals(pa.array(
        [{"ints": 1}, {"floats": 1.0}],
        type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
    ))


def test_table_from_struct_array_invalid():
    with pytest.raises(TypeError, match="Argument 'struct_array' has incorrect type"):
        pa.Table.from_struct_array(pa.array(range(5)))


def test_table_from_struct_array():
    struct_array = pa.array(
        [{"ints": 1}, {"floats": 1.0}],
        type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
    )
    result = pa.Table.from_struct_array(struct_array)
    assert result.equals(pa.Table.from_arrays(
        [
            pa.array([1, None], type=pa.int32()),
            pa.array([None, 1.0], type=pa.float32()),
        ], ["ints", "floats"]
    ))


def test_table_from_struct_array_chunked_array():
    chunked_struct_array = pa.chunked_array(
        [[{"ints": 1}, {"floats": 1.0}]],
        type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
    )
    result = pa.Table.from_struct_array(chunked_struct_array)
    assert result.equals(pa.Table.from_arrays(
        [
            pa.array([1, None], type=pa.int32()),
            pa.array([None, 1.0], type=pa.float32()),
        ], ["ints", "floats"]
    ))


def test_table_to_struct_array():
    table = pa.Table.from_arrays(
        [
            pa.array([1, None], type=pa.int32()),
            pa.array([None, 1.0], type=pa.float32()),
        ], ["ints", "floats"]
    )
    result = table.to_struct_array()
    assert result.equals(pa.chunked_array(
        [[{"ints": 1}, {"floats": 1.0}]],
        type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
    ))


def test_table_to_struct_array_with_max_chunksize():
    table = pa.Table.from_arrays(
        [
            pa.array([1, None], type=pa.int32()),
            pa.array([None, 1.0], type=pa.float32()),
        ], ["ints", "floats"]
    )
    result = table.to_struct_array(max_chunksize=1)
    assert result.equals(pa.chunked_array(
        [[{"ints": 1}], [{"floats": 1.0}]],
        type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
    ))


def test_table_to_struct_array_for_empty_table():
    table = pa.Table.from_arrays(
        [
            pa.array([], type=pa.int32()),
            pa.array([], type=pa.float32()),
        ], ["ints", "floats"]
    )
    result = table.to_struct_array()
    assert result.equals(
        pa.chunked_array(
            [],
            type=pa.struct({"ints": pa.int32(), "floats": pa.float32()}),
        ),
    )


def check_tensors(tensor, expected_tensor, type, size):
    assert tensor.equals(expected_tensor)
    assert tensor.size == size
    assert tensor.type == type
    assert tensor.shape == expected_tensor.shape
    assert tensor.strides == expected_tensor.strides


@pytest.mark.numpy
@pytest.mark.parametrize('typ_str', [
    "uint8", "uint16", "uint32", "uint64",
    "int8", "int16", "int32", "int64",
    "float16", "float32", "float64",
])
def test_recordbatch_to_tensor_uniform_type(typ_str):
    typ = np.dtype(typ_str)
    arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
    arr2 = [10, 20, 30, 40, 50, 60, 70, 80, 90]
    arr3 = [100, 100, 100, 100, 100, 100, 100, 100, 100]
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array(arr1, type=pa.from_numpy_dtype(typ)),
            pa.array(arr2, type=pa.from_numpy_dtype(typ)),
            pa.array(arr3, type=pa.from_numpy_dtype(typ)),
        ], ["a", "b", "c"]
    )

    result = batch.to_tensor(row_major=False)
    x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="F")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ), 27)

    result = batch.to_tensor()
    x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="C")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ), 27)

    # Test offset
    batch1 = batch.slice(1)
    arr1 = [2, 3, 4, 5, 6, 7, 8, 9]
    arr2 = [20, 30, 40, 50, 60, 70, 80, 90]
    arr3 = [100, 100, 100, 100, 100, 100, 100, 100]

    result = batch1.to_tensor(row_major=False)
    x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="F")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ), 24)

    result = batch1.to_tensor()
    x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="C")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ), 24)

    batch2 = batch.slice(1, 5)
    arr1 = [2, 3, 4, 5, 6]
    arr2 = [20, 30, 40, 50, 60]
    arr3 = [100, 100, 100, 100, 100]

    result = batch2.to_tensor(row_major=False)
    x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="F")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ), 15)

    result = batch2.to_tensor()
    x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="C")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ), 15)


@pytest.mark.numpy
@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_to_tensor_mixed_type(cls):
    # uint16 + int16 = int32
    arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
    arr2 = [10, 20, 30, 40, 50, 60, 70, 80, 90]
    arr3 = [100, 200, 300, np.nan, 500, 600, 700, 800, 900]
    tabular = cls.from_arrays(
        [
            pa.array(arr1, type=pa.uint16()),
            pa.array(arr2, type=pa.int16()),
        ], ["a", "b"]
    )

    result = tabular.to_tensor(row_major=False)
    x = np.column_stack([arr1, arr2]).astype(np.int32, order="F")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.int32(), 18)

    result = tabular.to_tensor()
    x = np.column_stack([arr1, arr2]).astype(np.int32, order="C")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.int32(), 18)

    # uint16 + int16 + float32 = float64
    tabular = cls.from_arrays(
        [
            pa.array(arr1, type=pa.uint16()),
            pa.array(arr2, type=pa.int16()),
            pa.array(arr3, type=pa.float32()),
        ], ["a", "b", "c"]
    )
    result = tabular.to_tensor(row_major=False)
    x = np.column_stack([arr1, arr2, arr3]).astype(np.float64, order="F")
    expected = pa.Tensor.from_numpy(x)

    np.testing.assert_equal(result.to_numpy(), x)
    assert result.size == 27
    assert result.type == pa.float64()
    assert result.shape == expected.shape
    assert result.strides == expected.strides

    result = tabular.to_tensor()
    x = np.column_stack([arr1, arr2, arr3]).astype(np.float64, order="C")
    expected = pa.Tensor.from_numpy(x)

    np.testing.assert_equal(result.to_numpy(), x)
    assert result.size == 27
    assert result.type == pa.float64()
    assert result.shape == expected.shape
    assert result.strides == expected.strides


@pytest.mark.numpy
def test_recordbatch_to_tensor_unsupported_mixed_type_with_float16():
    arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
    arr2 = [10, 20, 30, 40, 50, 60, 70, 80, 90]
    arr3 = [100, 200, 300, 400, 500, 600, 700, 800, 900]
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array(arr1, type=pa.uint16()),
            pa.array(np.array(arr2, dtype=np.float16), type=pa.float16()),
            pa.array(arr3, type=pa.float32()),
        ], ["a", "b", "c"]
    )

    with pytest.raises(
        NotImplementedError,
        match="Casting from or to halffloat is not supported."
    ):
        batch.to_tensor()


@pytest.mark.numpy
def test_recordbatch_to_tensor_nan():
    arr1 = [1, 2, 3, 4, np.nan, 6, 7, 8, 9]
    arr2 = [10, 20, 30, 40, 50, 60, 70, np.nan, 90]
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array(arr1, type=pa.float32()),
            pa.array(arr2, type=pa.float32()),
        ], ["a", "b"]
    )
    result = batch.to_tensor(row_major=False)
    x = np.column_stack([arr1, arr2]).astype(np.float32, order="F")
    expected = pa.Tensor.from_numpy(x)

    np.testing.assert_equal(result.to_numpy(), x)
    assert result.size == 18
    assert result.type == pa.float32()
    assert result.shape == expected.shape
    assert result.strides == expected.strides


@pytest.mark.numpy
def test_recordbatch_to_tensor_null():
    arr1 = [1, 2, 3, 4, None, 6, 7, 8, 9]
    arr2 = [10, 20, 30, 40, 50, 60, 70, None, 90]
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array(arr1, type=pa.int32()),
            pa.array(arr2, type=pa.float32()),
        ], ["a", "b"]
    )
    with pytest.raises(
        pa.ArrowTypeError,
        match="Can only convert a Table or RecordBatch with no nulls."
    ):
        batch.to_tensor()

    result = batch.to_tensor(null_to_nan=True, row_major=False)
    x = np.column_stack([arr1, arr2]).astype(np.float64, order="F")
    expected = pa.Tensor.from_numpy(x)

    np.testing.assert_equal(result.to_numpy(), x)
    assert result.size == 18
    assert result.type == pa.float64()
    assert result.shape == expected.shape
    assert result.strides == expected.strides

    # int32 -> float64
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array(arr1, type=pa.int32()),
            pa.array(arr2, type=pa.int32()),
        ], ["a", "b"]
    )

    result = batch.to_tensor(null_to_nan=True, row_major=False)

    np.testing.assert_equal(result.to_numpy(), x)
    assert result.size == 18
    assert result.type == pa.float64()
    assert result.shape == expected.shape
    assert result.strides == expected.strides

    # int8 -> float32
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array(arr1, type=pa.int8()),
            pa.array(arr2, type=pa.int8()),
        ], ["a", "b"]
    )

    result = batch.to_tensor(null_to_nan=True, row_major=False)
    x = np.column_stack([arr1, arr2]).astype(np.float32, order="F")
    expected = pa.Tensor.from_numpy(x)

    np.testing.assert_equal(result.to_numpy(), x)
    assert result.size == 18
    assert result.type == pa.float32()
    assert result.shape == expected.shape
    assert result.strides == expected.strides


@pytest.mark.numpy
def test_recordbatch_to_tensor_empty():
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array([], type=pa.float32()),
            pa.array([], type=pa.float32()),
        ], ["a", "b"]
    )
    result = batch.to_tensor()

    x = np.column_stack([[], []]).astype(np.float32, order="F")
    expected = pa.Tensor.from_numpy(x)

    assert result.size == expected.size
    assert result.type == pa.float32()
    assert result.shape == expected.shape
    assert result.strides == (4, 4)


def test_recordbatch_to_tensor_unsupported():
    arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
    # Unsupported data type
    arr2 = ["a", "b", "c", "a", "b", "c", "a", "b", "c"]
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array(arr1, type=pa.int32()),
            pa.array(arr2, type=pa.utf8()),
        ], ["a", "b"]
    )
    with pytest.raises(
        pa.ArrowTypeError,
        match="DataType is not supported"
    ):
        batch.to_tensor()


@pytest.mark.numpy
@pytest.mark.parametrize('typ_str', [
    "uint8", "uint16", "uint32", "uint64",
    "int8", "int16", "int32", "int64",
    "float16", "float32", "float64",
])
def test_table_to_tensor_uniform_type(typ_str):
    arr1 = [[1, 2, 3], [4, 5, 6, 7, 8, 9]]
    arr2 = [[10, 20], [30, 40, 50, 60, 70, 80, 90]]
    arr3 = [[100, 100, 100, 100, 100, 100], [100, 100, 100]]
    table = pa.Table.from_arrays(
        [
            pa.chunked_array(arr1, type=pa.from_numpy_dtype(typ_str)),
            pa.chunked_array(arr2, type=pa.from_numpy_dtype(typ_str)),
            pa.chunked_array(arr3, type=pa.from_numpy_dtype(typ_str)),
        ], ["a", "b", "c"]
    )

    arr1_f = [1, 2, 3, 4, 5, 6, 7, 8, 9]
    arr2_f = [10, 20, 30, 40, 50, 60, 70, 80, 90]
    arr3_f = [100, 100, 100, 100, 100, 100, 100, 100, 100]

    result = table.to_tensor(row_major=False)
    x = np.column_stack([arr1_f, arr2_f, arr3_f]).astype(typ_str, order="F")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ_str), 27)

    result = table.to_tensor()
    x = np.column_stack([arr1_f, arr2_f, arr3_f]).astype(typ_str, order="C")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ_str), 27)

    # Test offset
    table1 = table.slice(1)
    arr1_f = [2, 3, 4, 5, 6, 7, 8, 9]
    arr2_f = [20, 30, 40, 50, 60, 70, 80, 90]
    arr3_f = [100, 100, 100, 100, 100, 100, 100, 100]

    result = table1.to_tensor(row_major=False)
    x = np.column_stack([arr1_f, arr2_f, arr3_f]).astype(typ_str, order="F")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ_str), 24)

    result = table1.to_tensor()
    x = np.column_stack([arr1_f, arr2_f, arr3_f]).astype(typ_str, order="C")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ_str), 24)

    table2 = table.slice(1, 5)
    arr1_f = [2, 3, 4, 5, 6]
    arr2_f = [20, 30, 40, 50, 60]
    arr3_f = [100, 100, 100, 100, 100]

    result = table2.to_tensor(row_major=False)
    x = np.column_stack([arr1_f, arr2_f, arr3_f]).astype(typ_str, order="F")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ_str), 15)

    result = table2.to_tensor()
    x = np.column_stack([arr1_f, arr2_f, arr3_f]).astype(typ_str, order="C")
    expected = pa.Tensor.from_numpy(x)
    check_tensors(result, expected, pa.from_numpy_dtype(typ_str), 15)


def _table_like_slice_tests(factory):
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10])
    ]
    names = ['c0', 'c1']

    obj = factory(data, names=names)

    sliced = obj.slice(2)
    assert sliced.num_rows == 3

    expected = factory([x.slice(2) for x in data], names=names)
    assert sliced.equals(expected)

    sliced2 = obj.slice(2, 2)
    expected2 = factory([x.slice(2, 2) for x in data], names=names)
    assert sliced2.equals(expected2)

    # 0 offset
    assert obj.slice(0).equals(obj)

    # Slice past end of array
    assert len(obj.slice(len(obj))) == 0

    with pytest.raises(IndexError):
        obj.slice(-1)

    # Check __getitem__-based slicing
    assert obj.slice(0, 0).equals(obj[:0])
    assert obj.slice(0, 2).equals(obj[:2])
    assert obj.slice(2, 2).equals(obj[2:4])
    assert obj.slice(2, len(obj) - 2).equals(obj[2:])
    assert obj.slice(len(obj) - 2, 2).equals(obj[-2:])
    assert obj.slice(len(obj) - 4, 2).equals(obj[-4:-2])


def test_recordbatch_slice_getitem():
    return _table_like_slice_tests(pa.RecordBatch.from_arrays)


def test_table_slice_getitem():
    return _table_like_slice_tests(pa.table)


def test_table_slice_no_columns():
    table = pa.table({'col': range(3)})
    empty = table.select([])
    assert empty.num_rows == 3

    # slice(offset) clamps to the remaining rows
    assert empty.slice(1).num_rows == 2
    assert empty.slice(0).num_rows == 3
    # slice(offset, length) must not exceed the remaining rows
    assert empty.slice(1, 4).num_rows == 2
    # consistent with __getitem__ slicing, which already worked
    assert empty[1:].num_rows == 2
    # offset past the end yields an empty table
    assert empty.slice(5).num_rows == 0


@pytest.mark.pandas
def test_slice_zero_length_table():
    # ARROW-7907: a segfault on this code was fixed after 0.16.0
    table = pa.table({'a': pa.array([], type=pa.timestamp('us'))})
    table_slice = table.slice(0, 0)
    table_slice.to_pandas()

    table = pa.table({'a': pa.chunked_array([], type=pa.string())})
    table.to_pandas()


@pytest.mark.numpy
def test_recordbatchlist_schema_equals():
    a1 = np.array([1], dtype='uint32')
    a2 = np.array([4.0, 5.0], dtype='float64')
    batch1 = pa.record_batch([pa.array(a1)], ['c1'])
    batch2 = pa.record_batch([pa.array(a2)], ['c1'])

    with pytest.raises(pa.ArrowInvalid):
        pa.Table.from_batches([batch1, batch2])


def test_table_column_sets_private_name():
    # ARROW-6429
    t = pa.table([pa.array([1, 2, 3, 4])], names=['a0'])
    assert t[0]._name == 'a0'


def test_table_equals():
    table = pa.Table.from_arrays([], names=[])
    assert table.equals(table)

    # ARROW-4822
    assert not table.equals(None)

    other = pa.Table.from_arrays([], names=[], metadata={'key': 'value'})
    assert not table.equals(other, check_metadata=True)
    assert table.equals(other)


def test_table_from_batches_and_schema():
    schema = pa.schema([
        pa.field('a', pa.int64()),
        pa.field('b', pa.float64()),
    ])
    batch = pa.record_batch([pa.array([1]), pa.array([3.14])],
                            names=['a', 'b'])
    table = pa.Table.from_batches([batch], schema)
    assert table.schema.equals(schema)
    assert table.column(0) == pa.chunked_array([[1]])
    assert table.column(1) == pa.chunked_array([[3.14]])

    incompatible_schema = pa.schema([pa.field('a', pa.int64())])
    with pytest.raises(pa.ArrowInvalid):
        pa.Table.from_batches([batch], incompatible_schema)

    incompatible_batch = pa.record_batch([pa.array([1])], ['a'])
    with pytest.raises(pa.ArrowInvalid):
        pa.Table.from_batches([incompatible_batch], schema)


@pytest.mark.pandas
def test_table_to_batches():
    from pandas.testing import assert_frame_equal
    import pandas as pd

    df1 = pd.DataFrame({'a': list(range(10))})
    df2 = pd.DataFrame({'a': list(range(10, 30))})

    batch1 = pa.RecordBatch.from_pandas(df1, preserve_index=False)
    batch2 = pa.RecordBatch.from_pandas(df2, preserve_index=False)

    table = pa.Table.from_batches([batch1, batch2, batch1])

    expected_df = pd.concat([df1, df2, df1], ignore_index=True)

    batches = table.to_batches()
    assert len(batches) == 3

    assert_frame_equal(pa.Table.from_batches(batches).to_pandas(),
                       expected_df)

    batches = table.to_batches(max_chunksize=15)
    assert list(map(len, batches)) == [10, 15, 5, 10]

    assert_frame_equal(table.to_pandas(), expected_df)
    assert_frame_equal(pa.Table.from_batches(batches).to_pandas(),
                       expected_df)

    table_from_iter = pa.Table.from_batches(iter([batch1, batch2, batch1]))
    assert table.equals(table_from_iter)

    with pytest.raises(ValueError):
        table.to_batches(max_chunksize=0)


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_basics(cls):
    data = [
        pa.array(range(5), type='int16'),
        pa.array([-10, -5, 0, None, 10], type='int32')
    ]
    table = cls.from_arrays(data, names=('a', 'b'))
    table.validate()

    assert not table.schema.metadata
    assert len(table) == 5
    assert table.num_rows == 5
    assert table.num_columns == len(data)
    assert table.shape == (5, 2)
    # (only the second array has a null bitmap)
    assert table.get_total_buffer_size() == (5 * 2) + (5 * 4 + 1)
    assert table.nbytes == (5 * 2) + (5 * 4 + 1)
    assert sys.getsizeof(table) >= object.__sizeof__(
        table) + table.get_total_buffer_size()

    pydict = table.to_pydict()
    assert pydict == OrderedDict([
        ('a', [0, 1, 2, 3, 4]),
        ('b', [-10, -5, 0, None, 10])
    ])
    assert isinstance(pydict, dict)
    assert table == cls.from_pydict(pydict, schema=table.schema)

    with pytest.raises(IndexError):
        # bounds checking
        table[2]

    columns = []
    for col in table.itercolumns():

        if cls is pa.Table:
            assert type(col) is pa.ChunkedArray

            for chunk in col.iterchunks():
                assert chunk is not None

            with pytest.raises(IndexError):
                col.chunk(-1)

            with pytest.raises(IndexError):
                col.chunk(col.num_chunks)

        else:
            assert issubclass(type(col), pa.Array)

        columns.append(col)

    assert table.columns == columns
    assert table == cls.from_arrays(columns, names=table.column_names)
    assert table != cls.from_arrays(columns[1:], names=table.column_names[1:])
    assert table != columns

    # Schema passed explicitly
    schema = pa.schema([pa.field('c0', pa.int16(),
                                 metadata={'key': 'value'}),
                        pa.field('c1', pa.int32())],
                       metadata={b'foo': b'bar'})
    table = cls.from_arrays(data, schema=schema)
    assert table.schema == schema

    wr = weakref.ref(table)
    assert wr() is not None
    del table
    assert wr() is None


def test_table_dunder_init():
    with pytest.raises(TypeError, match='Table'):
        pa.Table()


def test_table_from_arrays_preserves_column_metadata():
    # Added to test https://issues.apache.org/jira/browse/ARROW-3866
    arr0 = pa.array([1, 2])
    arr1 = pa.array([3, 4])
    field0 = pa.field('field1', pa.int64(), metadata=dict(a="A", b="B"))
    field1 = pa.field('field2', pa.int64(), nullable=False)
    table = pa.Table.from_arrays([arr0, arr1],
                                 schema=pa.schema([field0, field1]))
    assert b"a" in table.field(0).metadata
    assert table.field(1).nullable is False


def test_table_from_arrays_invalid_names():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10])
    ]
    with pytest.raises(ValueError):
        pa.Table.from_arrays(data, names=['a', 'b', 'c'])

    with pytest.raises(ValueError):
        pa.Table.from_arrays(data, names=['a'])


def test_table_from_lists():
    data = [
        list(range(5)),
        [-10, -5, 0, 5, 10]
    ]

    result = pa.table(data, names=['a', 'b'])
    expected = pa.Table.from_arrays(data, names=['a', 'b'])
    assert result.equals(expected)

    schema = pa.schema([
        pa.field('a', pa.uint16()),
        pa.field('b', pa.int64())
    ])
    result = pa.table(data, schema=schema)
    expected = pa.Table.from_arrays(data, schema=schema)
    assert result.equals(expected)


def test_table_pickle(pickle_module):
    data = [
        pa.chunked_array([[1, 2], [3, 4]], type=pa.uint32()),
        pa.chunked_array([["some", "strings", None, ""]], type=pa.string()),
    ]
    schema = pa.schema([pa.field('ints', pa.uint32()),
                        pa.field('strs', pa.string())],
                       metadata={b'foo': b'bar'})
    table = pa.Table.from_arrays(data, schema=schema)

    result = pickle_module.loads(pickle_module.dumps(table))
    result.validate()
    assert result.equals(table)


def test_table_get_field():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    table = pa.Table.from_arrays(data, names=('a', 'b', 'c'))

    assert table.field('a').equals(table.schema.field('a'))
    assert table.field(0).equals(table.schema.field('a'))

    with pytest.raises(KeyError):
        table.field('d')

    with pytest.raises(TypeError):
        table.field(None)

    with pytest.raises(IndexError):
        table.field(4)


def test_table_select_column():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    table = pa.Table.from_arrays(data, names=('a', 'b', 'c'))

    assert table.column('a').equals(table.column(0))

    with pytest.raises(KeyError,
                       match='Field "d" does not exist in schema'):
        table.column('d')

    with pytest.raises(TypeError):
        table.column(None)

    with pytest.raises(IndexError):
        table.column(4)


def test_table_column_with_duplicates():
    # ARROW-8209
    table = pa.table([pa.array([1, 2, 3]),
                      pa.array([4, 5, 6]),
                      pa.array([7, 8, 9])], names=['a', 'b', 'a'])

    with pytest.raises(KeyError,
                       match='Field "a" exists 2 times in schema'):
        table.column('a')


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_add_column(cls):
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    table = cls.from_arrays(data, names=('a', 'b', 'c'))

    new_field = pa.field('d', data[1].type)
    t2 = table.add_column(3, new_field, data[1])
    t3 = table.append_column(new_field, data[1])

    expected = cls.from_arrays(data + [data[1]],
                               names=('a', 'b', 'c', 'd'))
    assert t2.equals(expected)
    assert t3.equals(expected)

    t4 = table.add_column(0, new_field, data[1])
    expected = cls.from_arrays([data[1]] + data,
                               names=('d', 'a', 'b', 'c'))
    assert t4.equals(expected)


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_set_column(cls):
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    table = cls.from_arrays(data, names=('a', 'b', 'c'))

    new_field = pa.field('d', data[1].type)
    t2 = table.set_column(0, new_field, data[1])

    expected_data = list(data)
    expected_data[0] = data[1]
    expected = cls.from_arrays(expected_data,
                               names=('d', 'b', 'c'))
    assert t2.equals(expected)


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_drop_columns(cls):
    """ drop one or more columns given labels"""
    a = pa.array(range(5))
    b = pa.array([-10, -5, 0, 5, 10])
    c = pa.array(range(5, 10))

    table = cls.from_arrays([a, b, c], names=('a', 'b', 'c'))
    t2 = table.drop_columns(['a', 'b'])
    t3 = table.drop_columns('a')

    exp_t2 = cls.from_arrays([c], names=('c',))
    assert exp_t2.equals(t2)
    exp_t3 = cls.from_arrays([b, c], names=('b', 'c',))
    assert exp_t3.equals(t3)

    # -- raise KeyError if column not in Table
    with pytest.raises(KeyError, match="Column 'd' not found"):
        table.drop_columns(['d'])


def test_table_drop():
    """ verify the alias of drop_columns is working"""
    a = pa.array(range(5))
    b = pa.array([-10, -5, 0, 5, 10])
    c = pa.array(range(5, 10))

    table = pa.Table.from_arrays([a, b, c], names=('a', 'b', 'c'))
    t2 = table.drop(['a', 'b'])
    t3 = table.drop('a')

    exp_t2 = pa.Table.from_arrays([c], names=('c',))
    assert exp_t2.equals(t2)
    exp_t3 = pa.Table.from_arrays([b, c], names=('b', 'c',))
    assert exp_t3.equals(t3)

    # -- raise KeyError if column not in Table
    with pytest.raises(KeyError, match="Column 'd' not found"):
        table.drop(['d'])


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_remove_column(cls):
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    table = cls.from_arrays(data, names=('a', 'b', 'c'))

    t2 = table.remove_column(0)
    t2.validate()
    expected = cls.from_arrays(data[1:], names=('b', 'c'))
    assert t2.equals(expected)


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_remove_column_empty(cls):
    # ARROW-1865
    data = [
        pa.array(range(5)),
    ]
    table = cls.from_arrays(data, names=['a'])

    t2 = table.remove_column(0)
    t2.validate()
    assert len(t2) == len(table)

    t3 = t2.add_column(0, table.field(0), table[0])
    t3.validate()
    assert t3.equals(table)


def test_empty_table_with_names():
    # ARROW-13784
    data = []
    names = ["a", "b"]
    message = (
        'Length of names [(]2[)] does not match length of arrays [(]0[)]')
    with pytest.raises(ValueError, match=message):
        pa.Table.from_arrays(data, names=names)


def test_empty_table():
    table = pa.table([])

    assert table.column_names == []
    assert table.equals(pa.Table.from_arrays([], []))


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_rename_columns(cls):
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    table = cls.from_arrays(data, names=['a', 'b', 'c'])
    assert table.column_names == ['a', 'b', 'c']

    expected = cls.from_arrays(data, names=['eh', 'bee', 'sea'])

    # Testing with list
    t2 = table.rename_columns(['eh', 'bee', 'sea'])
    t2.validate()
    assert t2.column_names == ['eh', 'bee', 'sea']
    assert t2.equals(expected)

    # Testing with tuple
    t3 = table.rename_columns(('eh', 'bee', 'sea'))
    t3.validate()
    assert t3.column_names == ['eh', 'bee', 'sea']
    assert t3.equals(expected)

    message = "names must be a list or dict not <class 'str'>"
    with pytest.raises(TypeError, match=message):
        table.rename_columns('not a list')


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_rename_columns_mapping(cls):
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array(range(5, 10))
    ]
    table = cls.from_arrays(data, names=['a', 'b', 'c'])
    assert table.column_names == ['a', 'b', 'c']

    expected = cls.from_arrays(data, names=['eh', 'b', 'sea'])
    t1 = table.rename_columns({'a': 'eh', 'c': 'sea'})
    t1.validate()
    assert t1 == expected

    # Test renaming duplicate column names
    table = cls.from_arrays(data, names=['a', 'a', 'c'])
    expected = cls.from_arrays(data, names=['eh', 'eh', 'sea'])
    t2 = table.rename_columns({'a': 'eh', 'c': 'sea'})
    t2.validate()
    assert t2 == expected

    # Test column not found
    with pytest.raises(KeyError, match=r"Column 'd' not found"):
        table.rename_columns({'a': 'eh', 'd': 'sea'})


def test_table_flatten():
    ty1 = pa.struct([pa.field('x', pa.int16()),
                     pa.field('y', pa.float32())])
    ty2 = pa.struct([pa.field('nest', ty1)])
    a = pa.array([(1, 2.5), (3, 4.5)], type=ty1)
    b = pa.array([((11, 12.5),), ((13, 14.5),)], type=ty2)
    c = pa.array([False, True], type=pa.bool_())

    table = pa.Table.from_arrays([a, b, c], names=['a', 'b', 'c'])
    t2 = table.flatten()
    t2.validate()
    expected = pa.Table.from_arrays([
        pa.array([1, 3], type=pa.int16()),
        pa.array([2.5, 4.5], type=pa.float32()),
        pa.array([(11, 12.5), (13, 14.5)], type=ty1),
        c],
        names=['a.x', 'a.y', 'b.nest', 'c'])
    assert t2.equals(expected)


def test_table_combine_chunks():
    batch1 = pa.record_batch([pa.array([1]), pa.array(["a"])],
                             names=['f1', 'f2'])
    batch2 = pa.record_batch([pa.array([2]), pa.array(["b"])],
                             names=['f1', 'f2'])
    table = pa.Table.from_batches([batch1, batch2])
    combined = table.combine_chunks()
    combined.validate()
    assert combined.equals(table)
    for c in combined.columns:
        assert c.num_chunks == 1


def test_table_unify_dictionaries():
    batch1 = pa.record_batch([
        pa.array(["foo", "bar", None, "foo"]).dictionary_encode(),
        pa.array([123, 456, 456, 789]).dictionary_encode(),
        pa.array([True, False, None, None])], names=['a', 'b', 'c'])
    batch2 = pa.record_batch([
        pa.array(["quux", "foo", None, "quux"]).dictionary_encode(),
        pa.array([456, 789, 789, None]).dictionary_encode(),
        pa.array([False, None, None, True])], names=['a', 'b', 'c'])

    table = pa.Table.from_batches([batch1, batch2])
    table = table.replace_schema_metadata({b"key1": b"value1"})
    assert table.column(0).chunk(0).dictionary.equals(
        pa.array(["foo", "bar"]))
    assert table.column(0).chunk(1).dictionary.equals(
        pa.array(["quux", "foo"]))
    assert table.column(1).chunk(0).dictionary.equals(
        pa.array([123, 456, 789]))
    assert table.column(1).chunk(1).dictionary.equals(
        pa.array([456, 789]))

    table = table.unify_dictionaries(pa.default_memory_pool())
    expected_dict_0 = pa.array(["foo", "bar", "quux"])
    expected_dict_1 = pa.array([123, 456, 789])
    assert table.column(0).chunk(0).dictionary.equals(expected_dict_0)
    assert table.column(0).chunk(1).dictionary.equals(expected_dict_0)
    assert table.column(1).chunk(0).dictionary.equals(expected_dict_1)
    assert table.column(1).chunk(1).dictionary.equals(expected_dict_1)

    assert table.to_pydict() == {
        'a': ["foo", "bar", None, "foo", "quux", "foo", None, "quux"],
        'b': [123, 456, 456, 789, 456, 789, 789, None],
        'c': [True, False, None, None, False, None, None, True],
    }
    assert table.schema.metadata == {b"key1": b"value1"}


def test_table_maps_as_pydicts():
    arrays = [
        pa.array(
            [{'x': 1, 'y': 2}, {'z': 3}],
            type=pa.map_(pa.string(), pa.int32())
        )
    ]
    table = pa.Table.from_arrays(arrays, names=['a'])

    table_dict = table.to_pydict(maps_as_pydicts="strict")
    assert 'a' in table_dict
    column_list = table_dict['a']
    assert len(column_list) == 2
    assert column_list == [{'x': 1, 'y': 2}, {'z': 3}]

    table_list = table.to_pylist(maps_as_pydicts="strict")
    assert len(table_list) == 2
    assert table_list == [{'a': {'x': 1, 'y': 2}}, {'a': {'z': 3}}]


def test_concat_tables():
    data = [
        list(range(5)),
        [-10., -5., 0., 5., 10.]
    ]
    data2 = [
        list(range(5, 10)),
        [1., 2., 3., 4., 5.]
    ]

    t1 = pa.Table.from_arrays([pa.array(x) for x in data],
                              names=('a', 'b'))
    t2 = pa.Table.from_arrays([pa.array(x) for x in data2],
                              names=('a', 'b'))

    result = pa.concat_tables([t1, t2])
    result.validate()
    assert len(result) == 10

    expected = pa.Table.from_arrays([pa.array(x + y)
                                     for x, y in zip(data, data2)],
                                    names=('a', 'b'))

    assert result.equals(expected)


def test_concat_tables_permissive():
    t1 = pa.Table.from_arrays([list(range(10))], names=('a',))
    t2 = pa.Table.from_arrays([list(('a', 'b', 'c'))], names=('a',))

    with pytest.raises(
            pa.ArrowTypeError,
            match="Unable to merge: Field a has incompatible types: int64 vs string"):
        _ = pa.concat_tables([t1, t2], promote_options="permissive")


def test_concat_tables_invalid_option():
    t = pa.Table.from_arrays([list(range(10))], names=('a',))

    with pytest.raises(ValueError, match="Invalid promote_options: invalid"):
        pa.concat_tables([t, t], promote_options="invalid")


def test_concat_tables_none_table():
    # ARROW-11997
    with pytest.raises(AttributeError):
        pa.concat_tables([None])


@pytest.mark.pandas
def test_concat_tables_with_different_schema_metadata():
    import pandas as pd

    schema = pa.schema([
        pa.field('a', pa.string()),
        pa.field('b', pa.string()),
    ])

    values = list('abcdefgh')
    df1 = pd.DataFrame({'a': values, 'b': values})
    df2 = pd.DataFrame({'a': [np.nan] * 8, 'b': values})

    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(table2.schema)
    assert not table1.schema.equals(table2.schema, check_metadata=True)

    table3 = pa.concat_tables([table1, table2])
    assert table1.schema.equals(table3.schema, check_metadata=True)
    assert table2.schema.equals(table3.schema)


def test_concat_tables_with_promote_option():
    t1 = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.int64())], ["int64_field"])
    t2 = pa.Table.from_arrays(
        [pa.array([1.0, 2.0], type=pa.float32())], ["float_field"])

    with pytest.warns(FutureWarning):
        result = pa.concat_tables([t1, t2], promote=True)

    assert result.equals(pa.Table.from_arrays([
        pa.array([1, 2, None, None], type=pa.int64()),
        pa.array([None, None, 1.0, 2.0], type=pa.float32()),
    ], ["int64_field", "float_field"]))

    t1 = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.int64())], ["f"])
    t2 = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.float32())], ["f"])

    with pytest.raises(pa.ArrowInvalid, match="Schema at index 1 was different:"):
        with pytest.warns(FutureWarning):
            pa.concat_tables([t1, t2], promote=False)


def test_concat_tables_with_promotion():
    t1 = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.int64())], ["int64_field"])
    t2 = pa.Table.from_arrays(
        [pa.array([1.0, 2.0], type=pa.float32())], ["float_field"])

    result = pa.concat_tables([t1, t2], promote_options="default")

    assert result.equals(pa.Table.from_arrays([
        pa.array([1, 2, None, None], type=pa.int64()),
        pa.array([None, None, 1.0, 2.0], type=pa.float32()),
    ], ["int64_field", "float_field"]))

    t3 = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.int32())], ["int64_field"])
    result = pa.concat_tables(
        [t1, t3], promote_options="permissive")
    assert result.equals(pa.Table.from_arrays([
        pa.array([1, 2, 1, 2], type=pa.int64()),
    ], ["int64_field"]))


def test_concat_tables_with_promotion_error():
    t1 = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.int64())], ["f"])
    t2 = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.float32())], ["f"])

    with pytest.raises(pa.ArrowTypeError, match="Unable to merge:"):
        pa.concat_tables([t1, t2], promote_options="default")


def test_table_negative_indexing():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
        pa.array([1.0, 2.0, 3.0, 4.0, 5.0]),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef']),
    ]
    table = pa.Table.from_arrays(data, names=tuple('abcd'))

    assert table[-1].equals(table[3])
    assert table[-2].equals(table[2])
    assert table[-3].equals(table[1])
    assert table[-4].equals(table[0])

    with pytest.raises(IndexError):
        table[-5]

    with pytest.raises(IndexError):
        table[4]


def test_concat_batches():
    data = [
        list(range(5)),
        [-10., -5., 0., 5., 10.]
    ]
    data2 = [
        list(range(5, 10)),
        [1., 2., 3., 4., 5.]
    ]

    t1 = pa.RecordBatch.from_arrays([pa.array(x) for x in data],
                                    names=('a', 'b'))
    t2 = pa.RecordBatch.from_arrays([pa.array(x) for x in data2],
                                    names=('a', 'b'))

    result = pa.concat_batches([t1, t2])
    result.validate()
    assert len(result) == 10

    expected = pa.RecordBatch.from_arrays([pa.array(x + y)
                                           for x, y in zip(data, data2)],
                                          names=('a', 'b'))

    assert result.equals(expected)


def test_concat_batches_different_schema():
    t1 = pa.RecordBatch.from_arrays(
        [pa.array([1, 2], type=pa.int64())], ["f"])
    t2 = pa.RecordBatch.from_arrays(
        [pa.array([1, 2], type=pa.float32())], ["f"])

    with pytest.raises(pa.ArrowInvalid,
                       match="not match index 0 recordbatch schema"):
        pa.concat_batches([t1, t2])


def test_concat_batches_none_batches():
    # ARROW-11997
    with pytest.raises(AttributeError):
        pa.concat_batches([None])


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_cast_to_incompatible_schema(cls):
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10]),
    ]
    table = cls.from_arrays(data, names=tuple('ab'))

    target_schema1 = pa.schema([
        pa.field('A', pa.int32()),
        pa.field('b', pa.int16()),
    ])
    target_schema2 = pa.schema([
        pa.field('a', pa.int32()),
    ])

    if cls is pa.Table:
        cls_name = 'table'
    else:
        cls_name = 'record batch'
    message = ("Target schema's field names are not matching the "
               f"{cls_name}'s field names:.*")

    with pytest.raises(ValueError, match=message):
        table.cast(target_schema1)
    with pytest.raises(ValueError, match=message):
        table.cast(target_schema2)


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_safe_casting(cls):
    data = [
        pa.array(range(5), type=pa.int64()),
        pa.array([-10, -5, 0, 5, 10], type=pa.int32()),
        pa.array([1.0, 2.0, 3.0, 4.0, 5.0], type=pa.float64()),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
    ]
    table = cls.from_arrays(data, names=tuple('abcd'))

    expected_data = [
        pa.array(range(5), type=pa.int32()),
        pa.array([-10, -5, 0, 5, 10], type=pa.int16()),
        pa.array([1, 2, 3, 4, 5], type=pa.int64()),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
    ]
    expected_table = cls.from_arrays(expected_data, names=tuple('abcd'))

    target_schema = pa.schema([
        pa.field('a', pa.int32()),
        pa.field('b', pa.int16()),
        pa.field('c', pa.int64()),
        pa.field('d', pa.string())
    ])
    casted_table = table.cast(target_schema)

    assert casted_table.equals(expected_table)


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_unsafe_casting(cls):
    data = [
        pa.array(range(5), type=pa.int64()),
        pa.array([-10, -5, 0, 5, 10], type=pa.int32()),
        pa.array([1.1, 2.2, 3.3, 4.4, 5.5], type=pa.float64()),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
    ]
    table = cls.from_arrays(data, names=tuple('abcd'))

    expected_data = [
        pa.array(range(5), type=pa.int32()),
        pa.array([-10, -5, 0, 5, 10], type=pa.int16()),
        pa.array([1, 2, 3, 4, 5], type=pa.int64()),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
    ]
    expected_table = cls.from_arrays(expected_data, names=tuple('abcd'))

    target_schema = pa.schema([
        pa.field('a', pa.int32()),
        pa.field('b', pa.int16()),
        pa.field('c', pa.int64()),
        pa.field('d', pa.string())
    ])

    with pytest.raises(pa.ArrowInvalid, match='truncated'):
        table.cast(target_schema)

    casted_table = table.cast(target_schema, safe=False)
    assert casted_table.equals(expected_table)


@pytest.mark.numpy
def test_invalid_table_construct():
    array = np.array([0, 1], dtype=np.uint8)
    u8 = pa.uint8()
    arrays = [pa.array(array, type=u8), pa.array(array[1:], type=u8)]

    with pytest.raises(pa.lib.ArrowInvalid):
        pa.Table.from_arrays(arrays, names=["a1", "a2"])


@pytest.mark.parametrize('data, klass', [
    ((['', 'foo', 'bar'], [4.5, 5, None]), list),
    ((['', 'foo', 'bar'], [4.5, 5, None]), pa.array),
    (([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array),
])
def test_from_arrays_schema(data, klass):
    data = [klass(data[0]), klass(data[1])]
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())])

    table = pa.Table.from_arrays(data, schema=schema)
    assert table.num_columns == 2
    assert table.num_rows == 3
    assert table.schema == schema

    # length of data and schema not matching
    schema = pa.schema([('strs', pa.utf8())])
    with pytest.raises(ValueError):
        pa.Table.from_arrays(data, schema=schema)

    # with different but compatible schema
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())])
    table = pa.Table.from_arrays(data, schema=schema)
    assert pa.types.is_float32(table.column('floats').type)
    assert table.num_columns == 2
    assert table.num_rows == 3
    assert table.schema == schema

    # with different and incompatible schema
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))])
    with pytest.raises((NotImplementedError, TypeError)):
        pa.Table.from_pydict(data, schema=schema)

    # Cannot pass both schema and metadata / names
    with pytest.raises(ValueError):
        pa.Table.from_arrays(data, schema=schema, names=['strs', 'floats'])

    with pytest.raises(ValueError):
        pa.Table.from_arrays(data, schema=schema, metadata={b'foo': b'bar'})


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_from_pydict(cls):
    table = cls.from_pydict({})
    assert table.num_columns == 0
    assert table.num_rows == 0
    assert table.schema == pa.schema([])
    assert table.to_pydict() == {}

    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())])

    # With lists as values
    data = OrderedDict([('strs', ['', 'foo', 'bar']),
                        ('floats', [4.5, 5, None])])
    table = cls.from_pydict(data)
    assert table.num_columns == 2
    assert table.num_rows == 3
    assert table.schema == schema
    assert table.to_pydict() == data

    # With metadata and inferred schema
    metadata = {b'foo': b'bar'}
    schema = schema.with_metadata(metadata)
    table = cls.from_pydict(data, metadata=metadata)
    assert table.schema == schema
    assert table.schema.metadata == metadata
    assert table.to_pydict() == data

    # With explicit schema
    table = cls.from_pydict(data, schema=schema)
    assert table.schema == schema
    assert table.schema.metadata == metadata
    assert table.to_pydict() == data

    # Cannot pass both schema and metadata
    with pytest.raises(ValueError):
        cls.from_pydict(data, schema=schema, metadata=metadata)

    # Non-convertible values given schema
    with pytest.raises(TypeError):
        cls.from_pydict({'c0': [0, 1, 2]},
                        schema=pa.schema([("c0", pa.string())]))

    # Missing schema fields from the passed mapping
    with pytest.raises(KeyError, match="doesn\'t contain.* c, d"):
        cls.from_pydict(
            {'a': [1, 2, 3], 'b': [3, 4, 5]},
            schema=pa.schema([
                ('a', pa.int64()),
                ('c', pa.int32()),
                ('d', pa.int16())
            ])
        )

    # Passed wrong schema type
    with pytest.raises(TypeError):
        cls.from_pydict({'a': [1, 2, 3]}, schema={})


@pytest.mark.parametrize('data, klass', [
    ((['', 'foo', 'bar'], [4.5, 5, None]), pa.array),
    (([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array),
])
def test_table_from_pydict_arrow_arrays(data, klass):
    data = OrderedDict([('strs', klass(data[0])), ('floats', klass(data[1]))])
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())])

    # With arrays as values
    table = pa.Table.from_pydict(data)
    assert table.num_columns == 2
    assert table.num_rows == 3
    assert table.schema == schema

    # With explicit (matching) schema
    table = pa.Table.from_pydict(data, schema=schema)
    assert table.num_columns == 2
    assert table.num_rows == 3
    assert table.schema == schema

    # with different but compatible schema
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())])
    table = pa.Table.from_pydict(data, schema=schema)
    assert pa.types.is_float32(table.column('floats').type)
    assert table.num_columns == 2
    assert table.num_rows == 3
    assert table.schema == schema

    # with different and incompatible schema
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))])
    with pytest.raises((NotImplementedError, TypeError)):
        pa.Table.from_pydict(data, schema=schema)


@pytest.mark.parametrize('data, klass', [
    ((['', 'foo', 'bar'], [4.5, 5, None]), list),
    ((['', 'foo', 'bar'], [4.5, 5, None]), pa.array),
    (([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array),
])
def test_table_from_pydict_schema(data, klass):
    # passed schema is source of truth for the columns

    data = OrderedDict([('strs', klass(data[0])), ('floats', klass(data[1]))])

    # schema has columns not present in data -> error
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64()),
                        ('ints', pa.int64())])
    with pytest.raises(KeyError, match='ints'):
        pa.Table.from_pydict(data, schema=schema)

    # data has columns not present in schema -> ignored
    schema = pa.schema([('strs', pa.utf8())])
    table = pa.Table.from_pydict(data, schema=schema)
    assert table.num_columns == 1
    assert table.schema == schema
    assert table.column_names == ['strs']


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_from_pylist(cls):
    table = cls.from_pylist([])
    assert table.num_columns == 0
    assert table.num_rows == 0
    assert table.schema == pa.schema([])
    assert table.to_pylist() == []

    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())])

    # With lists as values
    data = [{'strs': '', 'floats': 4.5},
            {'strs': 'foo', 'floats': 5},
            {'strs': 'bar', 'floats': None}]
    table = cls.from_pylist(data)
    assert table.num_columns == 2
    assert table.num_rows == 3
    assert table.schema == schema
    assert table.to_pylist() == data

    # With metadata and inferred schema
    metadata = {b'foo': b'bar'}
    schema = schema.with_metadata(metadata)
    table = cls.from_pylist(data, metadata=metadata)
    assert table.schema == schema
    assert table.schema.metadata == metadata
    assert table.to_pylist() == data

    # With explicit schema
    table = cls.from_pylist(data, schema=schema)
    assert table.schema == schema
    assert table.schema.metadata == metadata
    assert table.to_pylist() == data

    # Cannot pass both schema and metadata
    with pytest.raises(ValueError):
        cls.from_pylist(data, schema=schema, metadata=metadata)

    # Non-convertible values given schema
    with pytest.raises(TypeError):
        cls.from_pylist([{'c0': 0}, {'c0': 1}, {'c0': 2}],
                        schema=pa.schema([("c0", pa.string())]))

    # Missing schema fields in the passed mapping translate to None
    schema = pa.schema([('a', pa.int64()),
                        ('c', pa.int32()),
                        ('d', pa.int16())
                        ])
    table = cls.from_pylist(
        [{'a': 1, 'b': 3}, {'a': 2, 'b': 4}, {'a': 3, 'b': 5}],
        schema=schema
    )
    data = [{'a': 1, 'c': None, 'd': None},
            {'a': 2, 'c': None, 'd': None},
            {'a': 3, 'c': None, 'd': None}]
    assert table.schema == schema
    assert table.to_pylist() == data

    # Passed wrong schema type
    with pytest.raises(TypeError):
        cls.from_pylist([{'a': 1}, {'a': 2}, {'a': 3}], schema={})

    # If the dictionaries of rows are not same length
    data = [{'strs': '', 'floats': 4.5},
            {'floats': 5},
            {'strs': 'bar'}]
    data2 = [{'strs': '', 'floats': 4.5},
             {'strs': None, 'floats': 5},
             {'strs': 'bar', 'floats': None}]
    table = cls.from_pylist(data)
    assert table.num_columns == 2
    assert table.num_rows == 3
    assert table.to_pylist() == data2

    data = [{'strs': ''},
            {'strs': 'foo', 'floats': 5},
            {'floats': None}]
    data2 = [{'strs': ''},
             {'strs': 'foo'},
             {'strs': None}]
    table = cls.from_pylist(data)
    assert table.num_columns == 1
    assert table.num_rows == 3
    assert table.to_pylist() == data2


@pytest.mark.pandas
def test_table_from_pandas_schema():
    # passed schema is source of truth for the columns
    import pandas as pd

    df = pd.DataFrame(OrderedDict([('strs', ['', 'foo', 'bar']),
                                   ('floats', [4.5, 5, None])]))

    # with different but compatible schema
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())])
    table = pa.Table.from_pandas(df, schema=schema)
    assert pa.types.is_float32(table.column('floats').type)
    assert table.schema.remove_metadata() == schema

    # with different and incompatible schema
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))])
    with pytest.raises((NotImplementedError, TypeError)):
        pa.Table.from_pandas(df, schema=schema)

    # schema has columns not present in data -> error
    schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64()),
                        ('ints', pa.int64())])
    with pytest.raises(KeyError, match='ints'):
        pa.Table.from_pandas(df, schema=schema)

    # data has columns not present in schema -> ignored
    schema = pa.schema([('strs', pa.utf8())])
    table = pa.Table.from_pandas(df, schema=schema)
    assert table.num_columns == 1
    assert table.schema.remove_metadata() == schema
    assert table.column_names == ['strs']


@pytest.mark.pandas
def test_table_factory_function():
    import pandas as pd

    # Put in wrong order to make sure that lines up with schema
    d = OrderedDict([('b', ['a', 'b', 'c']), ('a', [1, 2, 3])])

    d_explicit = {'b': pa.array(['a', 'b', 'c'], type='string'),
                  'a': pa.array([1, 2, 3], type='int32')}

    schema = pa.schema([('a', pa.int32()), ('b', pa.string())])

    df = pd.DataFrame(d)
    table1 = pa.table(df)
    table2 = pa.Table.from_pandas(df)
    assert table1.equals(table2)
    table1 = pa.table(df, schema=schema)
    table2 = pa.Table.from_pandas(df, schema=schema)
    assert table1.equals(table2)

    table1 = pa.table(d_explicit)
    table2 = pa.Table.from_pydict(d_explicit)
    assert table1.equals(table2)

    # schema coerces type
    table1 = pa.table(d, schema=schema)
    table2 = pa.Table.from_pydict(d, schema=schema)
    assert table1.equals(table2)


def test_table_factory_function_args():
    # from_pydict not accepting names:
    with pytest.raises(ValueError):
        pa.table({'a': [1, 2, 3]}, names=['a'])

    # backwards compatibility for schema as first positional argument
    schema = pa.schema([('a', pa.int32())])
    table = pa.table({'a': pa.array([1, 2, 3], type=pa.int64())}, schema)
    assert table.column('a').type == pa.int32()

    # from_arrays: accept both names and schema as positional first argument
    data = [pa.array([1, 2, 3], type='int64')]
    names = ['a']
    table = pa.table(data, names)
    assert table.column_names == names
    schema = pa.schema([('a', pa.int64())])
    table = pa.table(data, schema)
    assert table.column_names == names


@pytest.mark.pandas
def test_table_factory_function_args_pandas():
    import pandas as pd

    # from_pandas not accepting names or metadata:
    with pytest.raises(ValueError):
        pa.table(pd.DataFrame({'a': [1, 2, 3]}), names=['a'])

    with pytest.raises(ValueError):
        pa.table(pd.DataFrame({'a': [1, 2, 3]}), metadata={b'foo': b'bar'})

    # backwards compatibility for schema as first positional argument
    schema = pa.schema([('a', pa.int32())])
    table = pa.table(pd.DataFrame({'a': [1, 2, 3]}), schema)
    assert table.column('a').type == pa.int32()


def test_factory_functions_invalid_input():
    with pytest.raises(TypeError, match="Expected pandas DataFrame, python"):
        pa.table("invalid input")

    with pytest.raises(TypeError, match="Expected pandas DataFrame"):
        pa.record_batch("invalid input")


def test_table_repr_to_string():
    # Schema passed explicitly
    schema = pa.schema([pa.field('c0', pa.int16(),
                                 metadata={'key': 'value'}),
                        pa.field('c1', pa.int32())],
                       metadata={b'foo': b'bar'})

    tab = pa.table([pa.array([1, 2, 3, 4], type='int16'),
                    pa.array([10, 20, 30, 40], type='int32')], schema=schema)
    assert str(tab) == """pyarrow.Table
c0: int16
c1: int32
----
c0: [[1,2,3,4]]
c1: [[10,20,30,40]]"""

    assert tab.to_string(show_metadata=True) == """\
pyarrow.Table
c0: int16
  -- field metadata --
  key: 'value'
c1: int32
-- schema metadata --
foo: 'bar'"""

    assert tab.to_string(preview_cols=5) == """\
pyarrow.Table
c0: int16
c1: int32
----
c0: [[1,2,3,4]]
c1: [[10,20,30,40]]"""

    assert tab.to_string(preview_cols=1) == """\
pyarrow.Table
c0: int16
c1: int32
----
c0: [[1,2,3,4]]
..."""


def test_table_repr_to_string_ellipsis():
    # Schema passed explicitly
    schema = pa.schema([pa.field('c0', pa.int16(),
                                 metadata={'key': 'value'}),
                        pa.field('c1', pa.int32())],
                       metadata={b'foo': b'bar'})

    tab = pa.table([pa.array([1, 2, 3, 4]*10, type='int16'),
                    pa.array([10, 20, 30, 40]*10, type='int32')],
                   schema=schema)
    assert str(tab) == """pyarrow.Table
c0: int16
c1: int32
----
c0: [[1,2,3,4,1,...,4,1,2,3,4]]
c1: [[10,20,30,40,10,...,40,10,20,30,40]]"""


def test_record_batch_repr_to_string():
    # Schema passed explicitly
    schema = pa.schema([pa.field('c0', pa.int16(),
                                 metadata={'key': 'value'}),
                        pa.field('c1', pa.int32())],
                       metadata={b'foo': b'bar'})

    batch = pa.record_batch([pa.array([1, 2, 3, 4], type='int16'),
                             pa.array([10, 20, 30, 40], type='int32')],
                            schema=schema)
    assert str(batch) == """pyarrow.RecordBatch
c0: int16
c1: int32
----
c0: [1,2,3,4]
c1: [10,20,30,40]"""

    assert batch.to_string(show_metadata=True) == """\
pyarrow.RecordBatch
c0: int16
  -- field metadata --
  key: 'value'
c1: int32
-- schema metadata --
foo: 'bar'"""

    assert batch.to_string(preview_cols=5) == """\
pyarrow.RecordBatch
c0: int16
c1: int32
----
c0: [1,2,3,4]
c1: [10,20,30,40]"""

    assert batch.to_string(preview_cols=1) == """\
pyarrow.RecordBatch
c0: int16
c1: int32
----
c0: [1,2,3,4]
..."""


def test_record_batch_repr_to_string_ellipsis():
    # Schema passed explicitly
    schema = pa.schema([pa.field('c0', pa.int16(),
                                 metadata={'key': 'value'}),
                        pa.field('c1', pa.int32())],
                       metadata={b'foo': b'bar'})

    batch = pa.record_batch([pa.array([1, 2, 3, 4]*10, type='int16'),
                             pa.array([10, 20, 30, 40]*10, type='int32')],
                            schema=schema)
    assert str(batch) == """pyarrow.RecordBatch
c0: int16
c1: int32
----
c0: [1,2,3,4,1,2,3,4,1,2,...,3,4,1,2,3,4,1,2,3,4]
c1: [10,20,30,40,10,20,30,40,10,20,...,30,40,10,20,30,40,10,20,30,40]"""


def test_table_function_unicode_schema():
    col_a = "äääh"
    col_b = "öööf"

    # Put in wrong order to make sure that lines up with schema
    d = OrderedDict([(col_b, ['a', 'b', 'c']), (col_a, [1, 2, 3])])

    schema = pa.schema([(col_a, pa.int32()), (col_b, pa.string())])

    result = pa.table(d, schema=schema)
    assert result[0].chunk(0).equals(pa.array([1, 2, 3], type='int32'))
    assert result[1].chunk(0).equals(pa.array(['a', 'b', 'c'], type='string'))


def test_table_take_vanilla_functionality():
    table = pa.table(
        [pa.array([1, 2, 3, None, 5]),
         pa.array(['a', 'b', 'c', 'd', 'e'])],
        ['f1', 'f2'])

    assert table.take(pa.array([2, 3])).equals(table.slice(2, 2))


def test_table_take_null_index():
    table = pa.table(
        [pa.array([1, 2, 3, None, 5]),
         pa.array(['a', 'b', 'c', 'd', 'e'])],
        ['f1', 'f2'])

    result_with_null_index = pa.table(
        [pa.array([1, None]),
         pa.array(['a', None])],
        ['f1', 'f2'])

    assert table.take(pa.array([0, None])).equals(result_with_null_index)


def test_table_take_non_consecutive():
    table = pa.table(
        [pa.array([1, 2, 3, None, 5]),
         pa.array(['a', 'b', 'c', 'd', 'e'])],
        ['f1', 'f2'])

    result_non_consecutive = pa.table(
        [pa.array([2, None]),
         pa.array(['b', 'd'])],
        ['f1', 'f2'])

    assert table.take(pa.array([1, 3])).equals(result_non_consecutive)


def test_table_select():
    a1 = pa.array([1, 2, 3, None, 5])
    a2 = pa.array(['a', 'b', 'c', 'd', 'e'])
    a3 = pa.array([[1, 2], [3, 4], [5, 6], None, [9, 10]])
    table = pa.table([a1, a2, a3], ['f1', 'f2', 'f3'])

    # selecting with string names
    result = table.select(['f1'])
    expected = pa.table([a1], ['f1'])
    assert result.equals(expected)

    result = table.select(['f3', 'f2'])
    expected = pa.table([a3, a2], ['f3', 'f2'])
    assert result.equals(expected)

    # selecting with integer indices
    result = table.select([0])
    expected = pa.table([a1], ['f1'])
    assert result.equals(expected)

    result = table.select([2, 1])
    expected = pa.table([a3, a2], ['f3', 'f2'])
    assert result.equals(expected)

    # preserve metadata
    table2 = table.replace_schema_metadata({"a": "test"})
    result = table2.select(["f1", "f2"])
    assert b"a" in result.schema.metadata

    # selecting non-existing column raises
    with pytest.raises(KeyError, match='Field "f5" does not exist'):
        table.select(['f5'])

    with pytest.raises(IndexError, match="index out of bounds"):
        table.select([5])

    # duplicate selection gives duplicated names in resulting table
    result = table.select(['f2', 'f2'])
    expected = pa.table([a2, a2], ['f2', 'f2'])
    assert result.equals(expected)

    # selection duplicated column raises
    table = pa.table([a1, a2, a3], ['f1', 'f2', 'f1'])
    with pytest.raises(KeyError, match='Field "f1" exists 2 times'):
        table.select(['f1'])

    result = table.select(['f2'])
    expected = pa.table([a2], ['f2'])
    assert result.equals(expected)


@pytest.mark.acero
def test_table_group_by():
    def sorted_by_keys(d):
        # Ensure a guaranteed order of keys for aggregation results.
        if "keys2" in d:
            keys = tuple(zip(d["keys"], d["keys2"]))
        else:
            keys = d["keys"]
        sorted_keys = sorted(keys)
        sorted_d = {"keys": sorted(d["keys"])}
        for entry in d:
            if entry == "keys":
                continue
            values = dict(zip(keys, d[entry]))
            for k in sorted_keys:
                sorted_d.setdefault(entry, []).append(values[k])
        return sorted_d

    table = pa.table([
        pa.array(["a", "a", "b", "b", "c"]),
        pa.array(["X", "X", "Y", "Z", "Z"]),
        pa.array([1, 2, 3, 4, 5]),
        pa.array([10, 20, 30, 40, 50])
    ], names=["keys", "keys2", "values", "bigvalues"])

    r = table.group_by("keys").aggregate([
        ("values", "hash_sum")
    ])
    assert sorted_by_keys(r.to_pydict()) == {
        "keys": ["a", "b", "c"],
        "values_sum": [3, 7, 5]
    }

    r = table.group_by("keys").aggregate([
        ("values", "hash_sum"),
        ("values", "hash_count")
    ])
    assert sorted_by_keys(r.to_pydict()) == {
        "keys": ["a", "b", "c"],
        "values_sum": [3, 7, 5],
        "values_count": [2, 2, 1]
    }

    # Test without hash_ prefix
    r = table.group_by("keys").aggregate([
        ("values", "sum")
    ])
    assert sorted_by_keys(r.to_pydict()) == {
        "keys": ["a", "b", "c"],
        "values_sum": [3, 7, 5]
    }

    r = table.group_by("keys").aggregate([
        ("values", "max"),
        ("bigvalues", "sum")
    ])
    assert sorted_by_keys(r.to_pydict()) == {
        "keys": ["a", "b", "c"],
        "values_max": [2, 4, 5],
        "bigvalues_sum": [30, 70, 50]
    }

    r = table.group_by("keys").aggregate([
        ("bigvalues", "max"),
        ("values", "sum")
    ])
    assert sorted_by_keys(r.to_pydict()) == {
        "keys": ["a", "b", "c"],
        "values_sum": [3, 7, 5],
        "bigvalues_max": [20, 40, 50]
    }

    r = table.group_by(["keys", "keys2"]).aggregate([
        ("values", "sum")
    ])
    assert sorted_by_keys(r.to_pydict()) == {
        "keys": ["a", "b", "b", "c"],
        "keys2": ["X", "Y", "Z", "Z"],
        "values_sum": [3, 3, 4, 5]
    }

    # Test many arguments
    r = table.group_by("keys").aggregate([
        ("values", "max"),
        ("bigvalues", "sum"),
        ("bigvalues", "max"),
        ([], "count_all"),
        ("values", "sum")
    ])
    assert sorted_by_keys(r.to_pydict()) == {
        "keys": ["a", "b", "c"],
        "values_max": [2, 4, 5],
        "bigvalues_sum": [30, 70, 50],
        "bigvalues_max": [20, 40, 50],
        "count_all": [2, 2, 1],
        "values_sum": [3, 7, 5]
    }

    table_with_nulls = pa.table([
        pa.array(["a", "a", "a"]),
        pa.array([1, None, None])
    ], names=["keys", "values"])

    r = table_with_nulls.group_by(["keys"]).aggregate([
        ("values", "count", pc.CountOptions(mode="all"))
    ])
    assert r.to_pydict() == {
        "keys": ["a"],
        "values_count": [3]
    }

    r = table_with_nulls.group_by(["keys"]).aggregate([
        ("values", "count", pc.CountOptions(mode="only_null"))
    ])
    assert r.to_pydict() == {
        "keys": ["a"],
        "values_count": [2]
    }

    r = table_with_nulls.group_by(["keys"]).aggregate([
        ("values", "count", pc.CountOptions(mode="only_valid"))
    ])
    assert r.to_pydict() == {
        "keys": ["a"],
        "values_count": [1]
    }

    r = table_with_nulls.group_by(["keys"]).aggregate([
        ([], "count_all"),  # nullary count that takes no parameters
        ("values", "count", pc.CountOptions(mode="only_valid"))
    ])
    assert r.to_pydict() == {
        "keys": ["a"],
        "count_all": [3],
        "values_count": [1]
    }

    r = table_with_nulls.group_by(["keys"]).aggregate([
        ([], "count_all")
    ])
    assert r.to_pydict() == {
        "keys": ["a"],
        "count_all": [3]
    }

    table = pa.table({
        'keys': ['a', 'b', 'a', 'b', 'a', 'b'],
        'values': range(6)})
    table_with_chunks = pa.Table.from_batches(
        table.to_batches(max_chunksize=3))
    r = table_with_chunks.group_by('keys').aggregate([('values', 'sum')])
    assert sorted_by_keys(r.to_pydict()) == {
        "keys": ["a", "b"],
        "values_sum": [6, 9]
    }


@pytest.mark.acero
def test_group_by_sliced_any_all():
    # GH-50043: hash_any/hash_all produce incorrect results on sliced boolean arrays
    # Row 0 will be discarded by slice, should not affect aggregation
    table = pa.table(
        {
            "g": [99, 10, 10],
            "any_arg": [True, False, None],
            "all_arg": [False, True, None],
        }
    )
    sliced = table.slice(1)

    expected = pa.table(
        {
            "g": [10],
            "any_arg_any": [False],
            "all_arg_all": [True],
        }
    )

    # any(False, None) = False, all(True, None) = True
    for use_threads in [False, True]:
        result = sliced.group_by("g", use_threads=use_threads).aggregate(
            [
                ("any_arg", "any"),
                ("all_arg", "all"),
            ]
        )
        assert result.equals(expected)


@pytest.mark.acero
def test_table_group_by_first():
    # "first" is an ordered aggregation -> requires to specify use_threads=False
    table1 = pa.table({'a': [1, 2, 3, 4], 'b': ['a', 'b'] * 2})
    table2 = pa.table({'a': [1, 2, 3, 4], 'b': ['b', 'a'] * 2})
    table = pa.concat_tables([table1, table2])

    with pytest.raises(NotImplementedError):
        table.group_by("b").aggregate([("a", "first")])

    result = table.group_by("b", use_threads=False).aggregate([("a", "first")])
    expected = pa.table({"b": ["a", "b"], "a_first": [1, 2]})
    assert result.equals(expected)


@pytest.mark.acero
def test_table_group_by_pivot_wider():
    table = pa.table({'group': [1, 2, 3, 1, 2, 3],
                      'key': ['h', 'h', 'h', 'w', 'w', 'w'],
                      'value': [10, 20, 30, 40, 50, 60]})

    with pytest.raises(ValueError, match='accepts 3 arguments but 2 passed'):
        table.group_by("group").aggregate([("key", "pivot_wider")])

    # GH-45739: calling hash_pivot_wider without options shouldn't crash
    # (even though it's not very useful as key_names=[])
    result = table.group_by("group").aggregate([(("key", "value"), "pivot_wider")])
    expected = pa.table({'group': [1, 2, 3],
                         'key_value_pivot_wider': [{}, {}, {}]})
    assert result.equals(expected)

    options = pc.PivotWiderOptions(key_names=('h', 'w'))
    result = table.group_by("group").aggregate(
        [(("key", "value"), "pivot_wider", options)])
    expected = pa.table(
        {'group': [1, 2, 3],
         'key_value_pivot_wider': [
             {'h': 10, 'w': 40}, {'h': 20, 'w': 50}, {'h': 30, 'w': 60}]})
    assert result.equals(expected)


def test_table_to_recordbatchreader():
    table = pa.Table.from_pydict({'x': [1, 2, 3]})
    reader = table.to_reader()
    assert table.schema == reader.schema
    assert table == reader.read_all()

    reader = table.to_reader(max_chunksize=2)
    assert reader.read_next_batch().num_rows == 2
    assert reader.read_next_batch().num_rows == 1


@pytest.mark.acero
def test_table_join():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "col2": ["a", "b", "f"]
    })

    t2 = pa.table({
        "colB": [99, 2, 1],
        "col3": ["Z", "B", "A"]
    })

    result = t1.join(t2, "colA", "colB")
    assert result.combine_chunks() == pa.table({
        "colA": [1, 2, 6],
        "col2": ["a", "b", "f"],
        "col3": ["A", "B", None]
    })

    result = t1.join(t2, "colA", "colB", join_type="full outer")
    assert result.combine_chunks().sort_by("colA") == pa.table({
        "colA": [1, 2, 6, 99],
        "col2": ["a", "b", "f", None],
        "col3": ["A", "B", None, "Z"]
    })


@pytest.mark.acero
def test_table_join_unique_key():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "col2": ["a", "b", "f"]
    })

    t2 = pa.table({
        "colA": [99, 2, 1],
        "col3": ["Z", "B", "A"]
    })

    result = t1.join(t2, "colA")
    assert result.combine_chunks() == pa.table({
        "colA": [1, 2, 6],
        "col2": ["a", "b", "f"],
        "col3": ["A", "B", None]
    })

    result = t1.join(t2, "colA", join_type="full outer", right_suffix="_r")
    assert result.combine_chunks().sort_by("colA") == pa.table({
        "colA": [1, 2, 6, 99],
        "col2": ["a", "b", "f", None],
        "col3": ["A", "B", None, "Z"]
    })


@pytest.mark.acero
def test_table_join_collisions():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "colB": [10, 20, 60],
        "colVals": ["a", "b", "f"]
    })

    t2 = pa.table({
        "colA": [99, 2, 1],
        "colB": [99, 20, 10],
        "colVals": ["Z", "B", "A"]
    })

    result = t1.join(t2, "colA", join_type="full outer")
    assert result.combine_chunks().sort_by("colA") == pa.table([
        [1, 2, 6, 99],
        [10, 20, 60, None],
        ["a", "b", "f", None],
        [10, 20, None, 99],
        ["A", "B", None, "Z"],
    ], names=["colA", "colB", "colVals", "colB", "colVals"])


@pytest.mark.acero
@pytest.mark.parametrize('cls', [(pa.Table), (pa.RecordBatch)])
def test_table_filter_expression(cls):
    t1 = cls.from_pydict({
        "colA": [1, 2, 3, 6],
        "colB": [10, 20, None, 60],
        "colVals": ["a", "b", "c", "f"]
    })

    result = t1.filter(pc.field("colB") < 50)
    assert result == cls.from_pydict({
        "colA": [1, 2],
        "colB": [10, 20],
        "colVals": ["a", "b"]
    })


@pytest.mark.acero
def test_table_filter_expression_chunks():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "colB": [10, 20, 60],
        "colVals": ["a", "b", "f"]
    })
    t2 = pa.table({
        "colA": [99, 2, 1],
        "colB": [99, 20, 10],
        "colVals": ["Z", "B", "A"]
    })

    t3 = pa.concat_tables([t1, t2])

    result = t3.filter(pc.field("colA") < 10)
    assert result.combine_chunks() == pa.table({
        "colA": [1, 2, 6, 2, 1],
        "colB": [10, 20, 60, 20, 10],
        "colVals": ["a", "b", "f", "B", "A"]
    })


@pytest.mark.acero
def test_table_join_many_columns():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "col2": ["a", "b", "f"]
    })

    t2 = pa.table({
        "colB": [99, 2, 1],
        "col3": ["Z", "B", "A"],
        "col4": ["Z", "B", "A"],
        "col5": ["Z", "B", "A"],
        "col6": ["Z", "B", "A"],
        "col7": ["Z", "B", "A"]
    })

    result = t1.join(t2, "colA", "colB")
    assert result.combine_chunks() == pa.table({
        "colA": [1, 2, 6],
        "col2": ["a", "b", "f"],
        "col3": ["A", "B", None],
        "col4": ["A", "B", None],
        "col5": ["A", "B", None],
        "col6": ["A", "B", None],
        "col7": ["A", "B", None]
    })

    result = t1.join(t2, "colA", "colB", join_type="full outer")
    assert result.combine_chunks().sort_by("colA") == pa.table({
        "colA": [1, 2, 6, 99],
        "col2": ["a", "b", "f", None],
        "col3": ["A", "B", None, "Z"],
        "col4": ["A", "B", None, "Z"],
        "col5": ["A", "B", None, "Z"],
        "col6": ["A", "B", None, "Z"],
        "col7": ["A", "B", None, "Z"],
    })


@pytest.mark.dataset
def test_table_join_asof():
    t1 = pa.Table.from_pydict({
        "colA": [1, 1, 5, 6, 7],
        "col2": ["a", "b", "a", "b", "f"]
    })

    t2 = pa.Table.from_pydict({
        "colB": [2, 9, 15],
        "col3": ["a", "b", "g"],
        "colC": [1., 3., 5.]
    })

    r = t1.join_asof(
        t2, on="colA", by="col2", tolerance=1,
        right_on="colB", right_by="col3",
    )
    assert r.combine_chunks() == pa.table({
        "colA": [1, 1, 5, 6, 7],
        "col2": ["a", "b", "a", "b", "f"],
        "colC": [1., None, None, None, None],
    })


@pytest.mark.dataset
def test_table_join_asof_multiple_by():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "colB": [10, 20, 60],
        "on": [1, 2, 3],
    })

    t2 = pa.table({
        "colB": [99, 20, 10],
        "colVals": ["Z", "B", "A"],
        "colA": [99, 2, 1],
        "on": [2, 3, 4],
    })

    result = t1.join_asof(
        t2, on="on", by=["colA", "colB"], tolerance=1
    )
    assert result.sort_by("colA") == pa.table({
        "colA": [1, 2, 6],
        "colB": [10, 20, 60],
        "on": [1, 2, 3],
        "colVals": [None, "B", None],
    })


@pytest.mark.dataset
def test_table_join_asof_empty_by():
    t1 = pa.table({
        "on": [1, 2, 3],
    })

    t2 = pa.table({
        "colVals": ["Z", "B", "A"],
        "on": [2, 3, 4],
    })

    result = t1.join_asof(
        t2, on="on", by=[], tolerance=1
    )
    assert result == pa.table({
        "on": [1, 2, 3],
        "colVals": ["Z", "Z", "B"],
    })


@pytest.mark.dataset
def test_table_join_asof_collisions():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "colB": [10, 20, 60],
        "on": [1, 2, 3],
        "colVals": ["a", "b", "f"]
    })

    t2 = pa.table({
        "colB": [99, 20, 10],
        "colVals": ["Z", "B", "A"],
        "colUniq": [100, 200, 300],
        "colA": [99, 2, 1],
        "on": [2, 3, 4],
    })

    msg = (
        "Columns {'colVals'} present in both tables. "
        "AsofJoin does not support column collisions."
    )
    with pytest.raises(ValueError, match=msg):
        t1.join_asof(
            t2, on="on", by=["colA", "colB"], tolerance=1,
            right_on="on", right_by=["colA", "colB"],
        )


@pytest.mark.dataset
def test_table_join_asof_by_length_mismatch():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "colB": [10, 20, 60],
        "on": [1, 2, 3],
    })

    t2 = pa.table({
        "colVals": ["Z", "B", "A"],
        "colUniq": [100, 200, 300],
        "colA": [99, 2, 1],
        "on": [2, 3, 4],
    })

    msg = "inconsistent size of by-key across inputs"
    with pytest.raises(pa.lib.ArrowInvalid, match=msg):
        t1.join_asof(
            t2, on="on", by=["colA", "colB"], tolerance=1,
            right_on="on", right_by=["colA"],
        )


@pytest.mark.dataset
def test_table_join_asof_by_type_mismatch():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "on": [1, 2, 3],
    })

    t2 = pa.table({
        "colVals": ["Z", "B", "A"],
        "colUniq": [100, 200, 300],
        "colA": [99., 2., 1.],
        "on": [2, 3, 4],
    })

    msg = "Expected by-key type int64 but got double for field colA in input 1"
    with pytest.raises(pa.lib.ArrowInvalid, match=msg):
        t1.join_asof(
            t2, on="on", by=["colA"], tolerance=1,
            right_on="on", right_by=["colA"],
        )


@pytest.mark.dataset
def test_table_join_asof_on_type_mismatch():
    t1 = pa.table({
        "colA": [1, 2, 6],
        "on": [1, 2, 3],
    })

    t2 = pa.table({
        "colVals": ["Z", "B", "A"],
        "colUniq": [100, 200, 300],
        "colA": [99, 2, 1],
        "on": [2., 3., 4.],
    })

    msg = "Expected on-key type int64 but got double for field on in input 1"
    with pytest.raises(pa.lib.ArrowInvalid, match=msg):
        t1.join_asof(
            t2, on="on", by=["colA"], tolerance=1,
            right_on="on", right_by=["colA"],
        )


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_cast_invalid(cls):
    # Casting a nullable field to non-nullable should be invalid!
    table = cls.from_pydict({'a': [None, 1], 'b': [None, True]})
    new_schema = pa.schema([pa.field("a", "int64", nullable=True),
                            pa.field("b", "bool", nullable=False)])
    with pytest.raises(ValueError):
        table.cast(new_schema)

    table = cls.from_pydict({'a': [None, 1], 'b': [False, True]})
    assert table.cast(new_schema).schema == new_schema


@pytest.mark.parametrize(
    ('cls'),
    [
        (pa.Table),
        (pa.RecordBatch)
    ]
)
def test_table_sort_by(cls):
    table = cls.from_arrays([
        pa.array([3, 1, 4, 2, 5]),
        pa.array(["b", "a", "b", "a", "c"]),
    ], names=["values", "keys"])

    assert table.sort_by("values").to_pydict() == {
        "keys": ["a", "a", "b", "b", "c"],
        "values": [1, 2, 3, 4, 5]
    }

    assert table.sort_by([("values", "descending", "at_end")]).to_pydict() == {
        "keys": ["c", "b", "b", "a", "a"],
        "values": [5, 4, 3, 2, 1]
    }

    tab = cls.from_arrays([
        pa.array([5, 7, 7, 35], type=pa.int64()),
        pa.array(["foo", "car", "bar", "foobar"])
    ], names=["a", "b"])

    sorted_tab = tab.sort_by([("a", "descending", "at_end")])
    sorted_tab_dict = sorted_tab.to_pydict()
    assert sorted_tab_dict["a"] == [35, 7, 7, 5]
    assert sorted_tab_dict["b"] == ["foobar", "car", "bar", "foo"]

    sorted_tab = tab.sort_by([("a", "ascending", "at_end")])
    sorted_tab_dict = sorted_tab.to_pydict()
    assert sorted_tab_dict["a"] == [5, 7, 7, 35]
    assert sorted_tab_dict["b"] == ["foo", "car", "bar", "foobar"]


def test_record_batch_sort():
    rb = pa.RecordBatch.from_arrays([
        pa.array([7, 35, 7, 5, None, None, 6, 5], type=pa.int64()),
        pa.array([4, 1, 3, 2, None, 1, None, 8], type=pa.int64()),
        pa.array(["foo", "car", "bar", "foobar", "dar", "ear", "far", "gar"])
    ], names=["a", "b", "c"])

    sorted_rb = rb.sort_by([("a", "descending", "at_end"),
                           ("b", "descending", "at_start")])
    sorted_rb_dict = sorted_rb.to_pydict()
    assert sorted_rb_dict["a"] == [35, 7, 7, 6, 5, 5, None, None]
    assert sorted_rb_dict["b"] == [1, 4, 3, None, 8, 2, None, 1]
    assert sorted_rb_dict["c"] == ["car", "foo",
                                   "bar", "far", "gar", "foobar", "dar", "ear"]

    sorted_rb = rb.sort_by([("a", "ascending", "at_start"),
                           ("b", "ascending", "at_end")])
    sorted_rb_dict = sorted_rb.to_pydict()
    assert sorted_rb_dict["a"] == [None, None, 5, 5, 6, 7, 7, 35]
    assert sorted_rb_dict["b"] == [1, None, 2, 8, None, 3, 4, 1]
    assert sorted_rb_dict["c"] == ["ear", "dar",
                                   "foobar", "gar", "far", "bar", "foo", "car"]

    sorted_rb = rb.sort_by([("a", "ascending", "at_start"),
                           ("b", "descending", "at_end")])
    sorted_rb_dict = sorted_rb.to_pydict()
    assert sorted_rb_dict["a"] == [None, None, 5, 5, 6, 7, 7, 35]
    assert sorted_rb_dict["b"] == [1, None, 8, 2, None, 4, 3, 1]
    assert sorted_rb_dict["c"] == ["ear", "dar",
                                   "gar", "foobar", "far", "foo", "bar", "car"]


@pytest.mark.numpy
@pytest.mark.parametrize("constructor", [pa.table, pa.record_batch])
def test_numpy_asarray(constructor):
    table = constructor([[1, 2, 3], [4.0, 5.0, 6.0]], names=["a", "b"])
    result = np.asarray(table)
    expected = np.array([[1, 4], [2, 5], [3, 6]], dtype="float64")
    np.testing.assert_allclose(result, expected)

    result = np.asarray(table, dtype="int32")
    np.testing.assert_allclose(result, expected)
    assert result.dtype == "int32"

    # no columns
    table2 = table.select([])
    result = np.asarray(table2)
    expected = np.empty((3, 0))
    np.testing.assert_allclose(result, expected)
    assert result.dtype == "float64"
    result = np.asarray(table2, dtype="int32")
    np.testing.assert_allclose(result, expected)
    assert result.dtype == "int32"

    # no rows
    table3 = table.slice(0, 0)
    result = np.asarray(table3)
    expected = np.empty((0, 2))
    np.testing.assert_allclose(result, expected)
    assert result.dtype == "float64"
    result = np.asarray(table3, dtype="int32")
    np.testing.assert_allclose(result, expected)
    assert result.dtype == "int32"


@pytest.mark.numpy
@pytest.mark.parametrize("constructor", [pa.table, pa.record_batch])
def test_numpy_array_protocol(constructor):
    table = constructor([[1, 2, 3], [4.0, 5.0, 6.0]], names=["a", "b"])
    expected = np.array([[1, 4], [2, 5], [3, 6]], dtype="float64")

    if Version(np.__version__) < Version("2.0.0.dev0"):
        # copy keyword is not strict and not passed down to __array__
        result = np.array(table, copy=False)
        np.testing.assert_array_equal(result, expected)
    else:
        # starting with numpy 2.0, the copy=False keyword is assumed to be strict
        with pytest.raises(ValueError, match="Unable to avoid a copy"):
            np.array(table, copy=False)


@pytest.mark.acero
def test_invalid_non_join_column():
    NUM_ITEMS = 30
    t1 = pa.Table.from_pydict({
        'id': range(NUM_ITEMS),
        'array_column': [[z for z in range(3)] for x in range(NUM_ITEMS)],
    })
    t2 = pa.Table.from_pydict({
        'id': range(NUM_ITEMS),
        'value': [x for x in range(NUM_ITEMS)]
    })

    # check as left table
    with pytest.raises(pa.lib.ArrowInvalid) as excinfo:
        t1.join(t2, 'id', join_type='inner')
    exp_error_msg = "Data type list<item: int64> is not supported " \
        + "in join non-key field array_column"
    assert exp_error_msg in str(excinfo.value)

    # check as right table
    with pytest.raises(pa.lib.ArrowInvalid) as excinfo:
        t2.join(t1, 'id', join_type='inner')
    assert exp_error_msg in str(excinfo.value)


@pytest.fixture
def cuda_context():
    cuda = pytest.importorskip("pyarrow.cuda", exc_type=ImportError)
    return cuda.Context(0)


@pytest.fixture
def schema():
    return pa.schema([pa.field('c0', pa.int32()), pa.field('c1', pa.int32())])


@pytest.fixture
def cpu_arrays(schema):
    return [pa.array([1, 2, 3, 4, 5], schema.field(0).type),
            pa.array([-10, -5, 0, None, 10], schema.field(1).type)]


@pytest.fixture
def cuda_arrays(cuda_context, cpu_arrays):
    return [arr.copy_to(cuda_context.memory_manager) for arr in cpu_arrays]


@pytest.fixture
def cpu_chunked_array(cpu_arrays):
    chunked_array = pa.chunked_array(cpu_arrays)
    assert chunked_array.is_cpu is True
    return chunked_array


@pytest.fixture
def cuda_chunked_array(cuda_arrays):
    chunked_array = pa.chunked_array(cuda_arrays)
    assert chunked_array.is_cpu is False
    return chunked_array


@pytest.fixture
def cpu_and_cuda_chunked_array(cpu_arrays, cuda_arrays):
    chunked_array = pa.chunked_array(cpu_arrays + cuda_arrays)
    assert chunked_array.is_cpu is False
    return chunked_array


@pytest.fixture
def cpu_recordbatch(cpu_arrays, schema):
    return pa.record_batch(cpu_arrays, schema=schema)


@pytest.fixture
def cuda_recordbatch(cuda_context, cpu_recordbatch):
    return cpu_recordbatch.copy_to(cuda_context.memory_manager)


@pytest.fixture
def cpu_table(schema, cpu_chunked_array):
    return pa.table([cpu_chunked_array, cpu_chunked_array], schema=schema)


@pytest.fixture
def cuda_table(schema, cuda_chunked_array):
    return pa.table([cuda_chunked_array, cuda_chunked_array], schema=schema)


@pytest.fixture
def cpu_and_cuda_table(schema, cpu_chunked_array, cuda_chunked_array):
    return pa.table([cpu_chunked_array, cuda_chunked_array], schema=schema)


def test_chunked_array_non_cpu(cuda_context, cpu_chunked_array, cuda_chunked_array,
                               cpu_and_cuda_chunked_array):
    # type test
    assert cuda_chunked_array.type == cpu_chunked_array.type

    # length() test
    assert cuda_chunked_array.length() == cpu_chunked_array.length()

    # str() test
    assert str(cuda_chunked_array) == str(cpu_chunked_array)

    # repr() test
    assert str(cuda_chunked_array) in repr(cuda_chunked_array)

    # validate() test
    cuda_chunked_array.validate()
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.validate(full=True)

    # null_count test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.null_count

    # nbytes() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.nbytes

    # get_total_buffer_size() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.get_total_buffer_size()

    # getitem() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array[0]

    # is_null() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.is_null()

    # is_nan() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.is_nan()

    # is_valid() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.is_valid()

    # fill_null() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.fill_null(0)

    # equals() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array == cuda_chunked_array

    # to_pandas() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.to_pandas()

    # to_numpy() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.to_numpy()

    # __array__() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.__array__()

    # cast() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.cast()

    # dictionary_encode() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.dictionary_encode()

    # flatten() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.flatten()

    # combine_chunks() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.combine_chunks()

    # unique() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.unique()

    # value_counts() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.value_counts()

    # filter() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.filter([True, False, True, False, True])

    # index() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.index(5)

    # slice() test
    cuda_chunked_array.slice(2, 2)

    # take() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.take([1])

    # drop_null() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.drop_null()

    # sort() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.sort()

    # unify_dictionaries() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.unify_dictionaries()

    # num_chunks test
    assert cuda_chunked_array.num_chunks == cpu_chunked_array.num_chunks

    # chunks test
    assert len(cuda_chunked_array.chunks) == len(cpu_chunked_array.chunks)

    # chunk() test
    chunk = cuda_chunked_array.chunk(0)
    assert chunk.device_type == pa.DeviceAllocationType.CUDA

    # to_pylist() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.to_pylist()

    # __arrow_c_stream__() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.__arrow_c_stream__()

    # __reduce__() test
    with pytest.raises(NotImplementedError):
        cuda_chunked_array.__reduce__()


def verify_cuda_recordbatch(batch, expected_schema):
    batch.validate()
    assert batch.device_type == pa.DeviceAllocationType.CUDA
    assert batch.is_cpu is False
    assert batch.num_columns == len(expected_schema.names)
    assert batch.column_names == expected_schema.names
    assert str(batch) in repr(batch)
    for c in batch.columns:
        assert c.device_type == pa.DeviceAllocationType.CUDA
    assert batch.schema == expected_schema


def test_recordbatch_non_cpu(cuda_context, cpu_recordbatch, cuda_recordbatch,
                             cuda_arrays, schema):
    verify_cuda_recordbatch(cuda_recordbatch, expected_schema=schema)
    N = cuda_recordbatch.num_rows

    # shape test
    assert cuda_recordbatch.shape == (5, 2)

    # columns() test
    assert len(cuda_recordbatch.columns) == 2

    # add_column(), set_column() test
    for fn in [cuda_recordbatch.add_column, cuda_recordbatch.set_column]:
        col = pa.array([-2, -1, 0, 1, 2], pa.int8()
                       ).copy_to(cuda_context.memory_manager)
        new_batch = fn(2, 'c2', col)
        verify_cuda_recordbatch(
            new_batch, expected_schema=schema.append(pa.field('c2', pa.int8())))
        err_msg = ("Got column on device <DeviceAllocationType.CPU: 1>, "
                   "but expected <DeviceAllocationType.CUDA: 2>.")
        with pytest.raises(TypeError, match=err_msg):
            fn(2, 'c2', [1] * N)

    # remove_column() test
    new_batch = cuda_recordbatch.remove_column(1)
    verify_cuda_recordbatch(new_batch, expected_schema=schema.remove(1))

    # drop_columns() test
    new_batch = cuda_recordbatch.drop_columns(['c1'])
    verify_cuda_recordbatch(new_batch, expected_schema=schema.remove(1))
    empty_batch = cuda_recordbatch.drop_columns(['c0', 'c1'])
    assert len(empty_batch.columns) == 0
    assert empty_batch.device_type == pa.DeviceAllocationType.CUDA

    # select() test
    new_batch = cuda_recordbatch.select(['c0'])
    verify_cuda_recordbatch(new_batch, expected_schema=schema.remove(1))

    # cast() test
    new_schema = pa.schema([pa.field('c0', pa.int64()), pa.field('c1', pa.int64())])
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.cast(new_schema)

    # drop_null() test
    null_col = pa.array([1] * N, mask=[True, False, True, False, True]).copy_to(
        cuda_context.memory_manager)
    cuda_recordbatch_with_nulls = cuda_recordbatch.add_column(2, 'c2', null_col)
    with pytest.raises(NotImplementedError):
        cuda_recordbatch_with_nulls.drop_null()

    # filter() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.filter([True] * N)

    # take() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.take([0])

    # sort_by() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.sort_by('c0')

    # field() test
    assert cuda_recordbatch.field(0) == schema.field(0)
    assert cuda_recordbatch.field(1) == schema.field(1)

    # equals() test
    new_batch = cpu_recordbatch.copy_to(cuda_context.memory_manager)
    with pytest.raises(NotImplementedError):
        assert cuda_recordbatch.equals(new_batch) is True

    # from_arrays() test
    new_batch = pa.RecordBatch.from_arrays(cuda_arrays, ['c0', 'c1'])
    verify_cuda_recordbatch(new_batch, expected_schema=schema)
    assert new_batch.copy_to(pa.default_cpu_memory_manager()).equals(cpu_recordbatch)

    # from_pydict() test
    new_batch = pa.RecordBatch.from_pydict({'c0': cuda_arrays[0], 'c1': cuda_arrays[1]})
    verify_cuda_recordbatch(new_batch, expected_schema=schema)
    assert new_batch.copy_to(pa.default_cpu_memory_manager()).equals(cpu_recordbatch)

    # from_struct_array() test
    fields = [schema.field(i) for i in range(len(schema.names))]
    struct_array = pa.StructArray.from_arrays(cuda_arrays, fields=fields)
    with pytest.raises(NotImplementedError):
        pa.RecordBatch.from_struct_array(struct_array)

    # nbytes test
    with pytest.raises(NotImplementedError):
        assert cuda_recordbatch.nbytes

    # get_total_buffer_size() test
    with pytest.raises(NotImplementedError):
        assert cuda_recordbatch.get_total_buffer_size()

    # to_pydict() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.to_pydict()

    # to_pylist() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.to_pylist()

    # to_pandas() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.to_pandas()

    # to_tensor() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.to_tensor()

    # to_struct_array() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.to_struct_array()

    # serialize() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.serialize()

    # slice() test
    new_batch = cuda_recordbatch.slice(1, 3)
    verify_cuda_recordbatch(new_batch, expected_schema=schema)
    assert new_batch.num_rows == 3
    cpu_batch = new_batch.copy_to(pa.default_cpu_memory_manager())
    assert cpu_batch == cpu_recordbatch.slice(1, 3)

    # replace_schema_metadata() test
    new_batch = cuda_recordbatch.replace_schema_metadata({b'key': b'value'})
    verify_cuda_recordbatch(new_batch, expected_schema=schema)
    assert new_batch.schema.metadata == {b'key': b'value'}

    # rename_columns() test
    new_batch = cuda_recordbatch.rename_columns(['col0', 'col1'])
    expected_schema = pa.schema(
        [pa.field('col0', schema.field(0).type),
         pa.field('col1', schema.field(1).type)])
    verify_cuda_recordbatch(new_batch, expected_schema=expected_schema)

    # validate() test
    cuda_recordbatch.validate()
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.validate(full=True)

    # __array__() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.__array__()

    # __arrow_c_array__() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.__arrow_c_array__()

    # __arrow_c_stream__() test
    with pytest.raises(NotImplementedError):
        cuda_recordbatch.__arrow_c_stream__()

    # __dataframe__() test
    with pytest.raises(NotImplementedError):
        from_dataframe(cuda_recordbatch.__dataframe__())


def verify_cuda_table(table, expected_schema):
    table.validate()
    assert table.is_cpu is False
    assert table.num_columns == len(expected_schema.names)
    assert table.column_names == expected_schema.names
    assert str(table) in repr(table)
    for c in table.columns:
        assert c.is_cpu is False
        for chunk in c.iterchunks():
            assert chunk.is_cpu is False
            assert chunk.device_type == pa.DeviceAllocationType.CUDA
    assert table.schema == expected_schema


def test_table_non_cpu(cuda_context, cpu_table, cuda_table,
                       cuda_arrays, cuda_recordbatch, schema):
    verify_cuda_table(cuda_table, expected_schema=schema)
    N = cuda_table.num_rows

    # shape test
    assert cuda_table.shape == (10, 2)

    # columns() test
    assert len(cuda_table.columns) == 2

    # add_column(), set_column() test
    for fn in [cuda_table.add_column, cuda_table.set_column]:
        cpu_col = pa.array([1] * N, pa.int8())
        cuda_col = cpu_col.copy_to(cuda_context.memory_manager)
        new_table = fn(2, 'c2', cuda_col)
        verify_cuda_table(new_table, expected_schema=schema.append(
            pa.field('c2', pa.int8())))
        new_table = fn(2, 'c2', cpu_col)
        assert new_table.is_cpu is False
        assert new_table.column(0).is_cpu is False
        assert new_table.column(1).is_cpu is False
        assert new_table.column(2).is_cpu is True

    # remove_column() test
    new_table = cuda_table.remove_column(1)
    verify_cuda_table(new_table, expected_schema=schema.remove(1))

    # drop_columns() test
    new_table = cuda_table.drop_columns(['c1'])
    verify_cuda_table(new_table, expected_schema=schema.remove(1))
    new_table = cuda_table.drop_columns(['c0', 'c1'])
    assert len(new_table.columns) == 0
    assert new_table.is_cpu

    # select() test
    new_table = cuda_table.select(['c0'])
    verify_cuda_table(new_table, expected_schema=schema.remove(1))

    # cast() test
    new_schema = pa.schema([pa.field('c0', pa.int64()), pa.field('c1', pa.int64())])
    with pytest.raises(NotImplementedError):
        cuda_table.cast(new_schema)

    # drop_null() test
    null_col = pa.array([1] * N, mask=[True] * N).copy_to(cuda_context.memory_manager)
    cuda_table_with_nulls = cuda_table.add_column(2, 'c2', null_col)
    with pytest.raises(NotImplementedError):
        cuda_table_with_nulls.drop_null()

    # filter() test
    with pytest.raises(NotImplementedError):
        cuda_table.filter([True] * N)

    # take() test
    with pytest.raises(NotImplementedError):
        cuda_table.take([0])

    # sort_by() test
    with pytest.raises(NotImplementedError):
        cuda_table.sort_by('c0')

    # field() test
    assert cuda_table.field(0) == schema.field(0)
    assert cuda_table.field(1) == schema.field(1)

    # equals() test
    with pytest.raises(NotImplementedError):
        assert cuda_table.equals(cpu_table)

    # from_arrays() test
    new_table = pa.Table.from_arrays(cuda_arrays, ['c0', 'c1'])
    verify_cuda_table(new_table, expected_schema=schema)

    # from_pydict() test
    new_table = pa.Table.from_pydict({'c0': cuda_arrays[0], 'c1': cuda_arrays[1]})
    verify_cuda_table(new_table, expected_schema=schema)

    # from_struct_array() test
    fields = [schema.field(i) for i in range(len(schema.names))]
    struct_array = pa.StructArray.from_arrays(cuda_arrays, fields=fields)
    with pytest.raises(NotImplementedError):
        pa.Table.from_struct_array(struct_array)

    # from_batches() test
    new_table = pa.Table.from_batches([cuda_recordbatch, cuda_recordbatch], schema)
    verify_cuda_table(new_table, expected_schema=schema)

    # nbytes test
    with pytest.raises(NotImplementedError):
        assert cuda_table.nbytes

    # get_total_buffer_size() test
    with pytest.raises(NotImplementedError):
        assert cuda_table.get_total_buffer_size()

    # to_pydict() test
    with pytest.raises(NotImplementedError):
        cuda_table.to_pydict()

    # to_pylist() test
    with pytest.raises(NotImplementedError):
        cuda_table.to_pylist()

    # to_pandas() test
    with pytest.raises(NotImplementedError):
        cuda_table.to_pandas()

    # to_struct_array() test
    with pytest.raises(NotImplementedError):
        cuda_table.to_struct_array()

    # to_batches() test
    batches = cuda_table.to_batches(max_chunksize=5)
    for batch in batches:
        # GH-44049
        with pytest.raises(AssertionError):
            verify_cuda_recordbatch(batch, expected_schema=schema)

    # to_reader() test
    reader = cuda_table.to_reader(max_chunksize=5)
    for batch in reader:
        # GH-44049
        with pytest.raises(AssertionError):
            verify_cuda_recordbatch(batch, expected_schema=schema)

    # slice() test
    new_table = cuda_table.slice(1, 3)
    verify_cuda_table(new_table, expected_schema=schema)
    assert new_table.num_rows == 3

    # replace_schema_metadata() test
    new_table = cuda_table.replace_schema_metadata({b'key': b'value'})
    verify_cuda_table(new_table, expected_schema=schema)
    assert new_table.schema.metadata == {b'key': b'value'}

    # rename_columns() test
    new_table = cuda_table.rename_columns(['col0', 'col1'])
    expected_schema = pa.schema(
        [pa.field('col0', schema.field(0).type),
         pa.field('col1', schema.field(1).type)])
    verify_cuda_table(new_table, expected_schema=expected_schema)

    # validate() test
    cuda_table.validate()
    with pytest.raises(NotImplementedError):
        cuda_table.validate(full=True)

    # flatten() test
    with pytest.raises(NotImplementedError):
        cuda_table.flatten()

    # combine_chunks() test
    with pytest.raises(NotImplementedError):
        cuda_table.flatten()

    # unify_dictionaries() test
    with pytest.raises(NotImplementedError):
        cuda_table.unify_dictionaries()

    # group_by() test
    with pytest.raises(NotImplementedError):
        cuda_table.group_by('c0')

    # join() test
    with pytest.raises(NotImplementedError):
        cuda_table.join(cuda_table, 'c0')

    # join_asof() test
    with pytest.raises(NotImplementedError):
        cuda_table.join_asof(cuda_table, 'c0', 'c0', 0)

    # __array__() test
    with pytest.raises(NotImplementedError):
        cuda_table.__array__()

    # __arrow_c_stream__() test
    with pytest.raises(NotImplementedError):
        cuda_table.__arrow_c_stream__()

    # __dataframe__() test
    with pytest.raises(NotImplementedError):
        from_dataframe(cuda_table.__dataframe__())

    # __reduce__() test
    with pytest.raises(NotImplementedError):
        cuda_table.__reduce__()