FM / tests

/usr/local/lib64/python3.6/site-packages/pyarrow/tests/

/usr/local/lib64/python3.6/site-packages/pyarrow/tests

Name	Size	Mode	Actions
data/	-	0755	rm
parquet/	-	0755	rm
__pycache__/	-	0755	rm
arrow_7980.py	1094	0644	edit dl rm
bound_function_visit_strings.pyx	2047	0644	edit dl rm
conftest.py	7982	0644	edit dl rm
deserialize_buffer.py	961	0644	edit dl rm
pandas_examples.py	5115	0644	edit dl rm
pandas_threaded_import.py	1429	0644	edit dl rm
pyarrow_cython_example.pyx	1951	0644	edit dl rm
strategies.py	12525	0644	edit dl rm
test_adhoc_memory_leak.py	1410	0644	edit dl rm
test_array.py	100157	0644	edit dl rm
test_builder.py	2184	0644	edit dl rm
test_cffi.py	13090	0644	edit dl rm
test_compute.py	83551	0644	edit dl rm
test_convert_builtin.py	71768	0644	edit dl rm
test_csv.py	68943	0644	edit dl rm
test_cuda.py	27863	0644	edit dl rm
test_cuda_numba_interop.py	8730	0644	edit dl rm
test_cython.py	5948	0644	edit dl rm
test_dataset.py	143023	0644	edit dl rm
test_deprecations.py	891	0644	edit dl rm
test_extension_type.py	24437	0644	edit dl rm
test_feather.py	23209	0644	edit dl rm
test_filesystem.py	2106	0644	edit dl rm
test_flight.py	73715	0644	edit dl rm
test_fs.py	54133	0644	edit dl rm
test_gandiva.py	14385	0644	edit dl rm
test_hdfs.py	13452	0644	edit dl rm
test_io.py	53066	0644	edit dl rm
test_ipc.py	31267	0644	edit dl rm
test_json.py	11325	0644	edit dl rm
test_jvm.py	15471	0644	edit dl rm
test_memory.py	4928	0644	edit dl rm
test_misc.py	4927	0644	edit dl rm
test_orc.py	9221	0644	edit dl rm
test_pandas.py	157909	0644	edit dl rm
test_plasma.py	44011	0644	edit dl rm
test_plasma_tf_op.py	3523	0644	edit dl rm
test_scalars.py	21074	0644	edit dl rm
test_schema.py	21282	0644	edit dl rm
test_serialization.py	42026	0644	edit dl rm
test_serialization_deprecated.py	1775	0644	edit dl rm
test_sparse_tensor.py	17436	0644	edit dl rm
test_strategies.py	1739	0644	edit dl rm
test_table.py	53168	0644	edit dl rm
test_tensor.py	6270	0644	edit dl rm
test_types.py	31487	0644	edit dl rm
test_util.py	1791	0644	edit dl rm
util.py	9011	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pyarrow/tests/test_schema.py (21282B)

# 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
import pickle
import sys
import weakref

import pytest
import numpy as np
import pyarrow as pa

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

def test_schema_constructor_errors():
    msg = ("Do not call Schema's constructor directly, use `pyarrow.schema` "
           "instead")
    with pytest.raises(TypeError, match=msg):
        pa.Schema()

def test_type_integers():
    dtypes = ['int8', 'int16', 'int32', 'int64',
              'uint8', 'uint16', 'uint32', 'uint64']

for name in dtypes:
        factory = getattr(pa, name)
        t = factory()
        assert str(t) == name

def test_type_to_pandas_dtype():
    M8_ns = np.dtype('datetime64[ns]')
    cases = [
        (pa.null(), np.object_),
        (pa.bool_(), np.bool_),
        (pa.int8(), np.int8),
        (pa.int16(), np.int16),
        (pa.int32(), np.int32),
        (pa.int64(), np.int64),
        (pa.uint8(), np.uint8),
        (pa.uint16(), np.uint16),
        (pa.uint32(), np.uint32),
        (pa.uint64(), np.uint64),
        (pa.float16(), np.float16),
        (pa.float32(), np.float32),
        (pa.float64(), np.float64),
        (pa.date32(), M8_ns),
        (pa.date64(), M8_ns),
        (pa.timestamp('ms'), M8_ns),
        (pa.binary(), np.object_),
        (pa.binary(12), np.object_),
        (pa.string(), np.object_),
        (pa.list_(pa.int8()), np.object_),
        # (pa.list_(pa.int8(), 2), np.object_),  # TODO needs pandas conversion
        (pa.map_(pa.int64(), pa.float64()), np.object_),
    ]
    for arrow_type, numpy_type in cases:
        assert arrow_type.to_pandas_dtype() == numpy_type

@pytest.mark.pandas
def test_type_to_pandas_dtype_check_import():
    # ARROW-7980
    test_util.invoke_script('arrow_7980.py')

def test_type_list():
    value_type = pa.int32()
    list_type = pa.list_(value_type)
    assert str(list_type) == 'list<item: int32>'

field = pa.field('my_item', pa.string())
    l2 = pa.list_(field)
    assert str(l2) == 'list<my_item: string>'

def test_type_comparisons():
    val = pa.int32()
    assert val == pa.int32()
    assert val == 'int32'
    assert val != 5

def test_type_for_alias():
    cases = [
        ('i1', pa.int8()),
        ('int8', pa.int8()),
        ('i2', pa.int16()),
        ('int16', pa.int16()),
        ('i4', pa.int32()),
        ('int32', pa.int32()),
        ('i8', pa.int64()),
        ('int64', pa.int64()),
        ('u1', pa.uint8()),
        ('uint8', pa.uint8()),
        ('u2', pa.uint16()),
        ('uint16', pa.uint16()),
        ('u4', pa.uint32()),
        ('uint32', pa.uint32()),
        ('u8', pa.uint64()),
        ('uint64', pa.uint64()),
        ('f4', pa.float32()),
        ('float32', pa.float32()),
        ('f8', pa.float64()),
        ('float64', pa.float64()),
        ('date32', pa.date32()),
        ('date64', pa.date64()),
        ('string', pa.string()),
        ('str', pa.string()),
        ('binary', pa.binary()),
        ('time32[s]', pa.time32('s')),
        ('time32[ms]', pa.time32('ms')),
        ('time64[us]', pa.time64('us')),
        ('time64[ns]', pa.time64('ns')),
        ('timestamp[s]', pa.timestamp('s')),
        ('timestamp[ms]', pa.timestamp('ms')),
        ('timestamp[us]', pa.timestamp('us')),
        ('timestamp[ns]', pa.timestamp('ns')),
        ('duration[s]', pa.duration('s')),
        ('duration[ms]', pa.duration('ms')),
        ('duration[us]', pa.duration('us')),
        ('duration[ns]', pa.duration('ns')),
        ('month_day_nano_interval', pa.month_day_nano_interval()),
    ]

for val, expected in cases:
        assert pa.type_for_alias(val) == expected

def test_type_string():
    t = pa.string()
    assert str(t) == 'string'

def test_type_timestamp_with_tz():
    tz = 'America/Los_Angeles'
    t = pa.timestamp('ns', tz=tz)
    assert t.unit == 'ns'
    assert t.tz == tz

def test_time_types():
    t1 = pa.time32('s')
    t2 = pa.time32('ms')
    t3 = pa.time64('us')
    t4 = pa.time64('ns')

assert t1.unit == 's'
    assert t2.unit == 'ms'
    assert t3.unit == 'us'
    assert t4.unit == 'ns'

assert str(t1) == 'time32[s]'
    assert str(t4) == 'time64[ns]'

with pytest.raises(ValueError):
        pa.time32('us')

with pytest.raises(ValueError):
        pa.time64('s')

def test_from_numpy_dtype():
    cases = [
        (np.dtype('bool'), pa.bool_()),
        (np.dtype('int8'), pa.int8()),
        (np.dtype('int16'), pa.int16()),
        (np.dtype('int32'), pa.int32()),
        (np.dtype('int64'), pa.int64()),
        (np.dtype('uint8'), pa.uint8()),
        (np.dtype('uint16'), pa.uint16()),
        (np.dtype('uint32'), pa.uint32()),
        (np.dtype('float16'), pa.float16()),
        (np.dtype('float32'), pa.float32()),
        (np.dtype('float64'), pa.float64()),
        (np.dtype('U'), pa.string()),
        (np.dtype('S'), pa.binary()),
        (np.dtype('datetime64[s]'), pa.timestamp('s')),
        (np.dtype('datetime64[ms]'), pa.timestamp('ms')),
        (np.dtype('datetime64[us]'), pa.timestamp('us')),
        (np.dtype('datetime64[ns]'), pa.timestamp('ns')),
        (np.dtype('timedelta64[s]'), pa.duration('s')),
        (np.dtype('timedelta64[ms]'), pa.duration('ms')),
        (np.dtype('timedelta64[us]'), pa.duration('us')),
        (np.dtype('timedelta64[ns]'), pa.duration('ns')),
    ]

for dt, pt in cases:
        result = pa.from_numpy_dtype(dt)
        assert result == pt

# Things convertible to numpy dtypes work
    assert pa.from_numpy_dtype('U') == pa.string()
    assert pa.from_numpy_dtype(np.str_) == pa.string()
    assert pa.from_numpy_dtype('int32') == pa.int32()
    assert pa.from_numpy_dtype(bool) == pa.bool_()

with pytest.raises(NotImplementedError):
        pa.from_numpy_dtype(np.dtype('O'))

with pytest.raises(TypeError):
        pa.from_numpy_dtype('not_convertible_to_dtype')

def test_schema():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ]
    sch = pa.schema(fields)

assert sch.names == ['foo', 'bar', 'baz']
    assert sch.types == [pa.int32(), pa.string(), pa.list_(pa.int8())]

assert len(sch) == 3
    assert sch[0].name == 'foo'
    assert sch[0].type == fields[0].type
    assert sch.field('foo').name == 'foo'
    assert sch.field('foo').type == fields[0].type

assert repr(sch) == """\
foo: int32
bar: string
baz: list<item: int8>
  child 0, item: int8"""

with pytest.raises(TypeError):
        pa.schema([None])

def test_schema_weakref():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ]
    schema = pa.schema(fields)
    wr = weakref.ref(schema)
    assert wr() is not None
    del schema
    assert wr() is None

def test_schema_to_string_with_metadata():
    lorem = """\
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla accumsan vel
turpis et mollis. Aliquam tincidunt arcu id tortor blandit blandit. Donec
eget leo quis lectus scelerisque varius. Class aptent taciti sociosqu ad
litora torquent per conubia nostra, per inceptos himenaeos. Praesent
faucibus, diam eu volutpat iaculis, tellus est porta ligula, a efficitur
turpis nulla facilisis quam. Aliquam vitae lorem erat. Proin a dolor ac libero
dignissim mollis vitae eu mauris. Quisque posuere tellus vitae massa
pellentesque sagittis. Aenean feugiat, diam ac dignissim fermentum, lorem
sapien commodo massa, vel volutpat orci nisi eu justo. Nulla non blandit
sapien. Quisque pretium vestibulum urna eu vehicula."""
    # ARROW-7063
    my_schema = pa.schema([pa.field("foo", "int32", False,
                                    metadata={"key1": "value1"}),
                           pa.field("bar", "string", True,
                                    metadata={"key3": "value3"})],
                          metadata={"lorem": lorem})

assert my_schema.to_string() == """\
foo: int32 not null
  -- field metadata --
  key1: 'value1'
bar: string
  -- field metadata --
  key3: 'value3'
-- schema metadata --
lorem: '""" + lorem[:65] + "' + " + str(len(lorem) - 65)

# Metadata that exactly fits
    result = pa.schema([('f0', 'int32')],
                       metadata={'key': 'value' + 'x' * 62}).to_string()
    assert result == """\
f0: int32
-- schema metadata --
key: 'valuexxxxxxxxxxxxxxxxxxxxxxxxxxxxx\
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'"""

assert my_schema.to_string(truncate_metadata=False) == """\
foo: int32 not null
  -- field metadata --
  key1: 'value1'
bar: string
  -- field metadata --
  key3: 'value3'
-- schema metadata --
lorem: '{}'""".format(lorem)

assert my_schema.to_string(truncate_metadata=False,
                               show_field_metadata=False) == """\
foo: int32 not null
bar: string
-- schema metadata --
lorem: '{}'""".format(lorem)

assert my_schema.to_string(truncate_metadata=False,
                               show_schema_metadata=False) == """\
foo: int32 not null
  -- field metadata --
  key1: 'value1'
bar: string
  -- field metadata --
  key3: 'value3'"""

assert my_schema.to_string(truncate_metadata=False,
                               show_field_metadata=False,
                               show_schema_metadata=False) == """\
foo: int32 not null
bar: string"""

def test_schema_from_tuples():
    fields = [
        ('foo', pa.int32()),
        ('bar', pa.string()),
        ('baz', pa.list_(pa.int8())),
    ]
    sch = pa.schema(fields)
    assert sch.names == ['foo', 'bar', 'baz']
    assert sch.types == [pa.int32(), pa.string(), pa.list_(pa.int8())]
    assert len(sch) == 3
    assert repr(sch) == """\
foo: int32
bar: string
baz: list<item: int8>
  child 0, item: int8"""

with pytest.raises(TypeError):
        pa.schema([('foo', None)])

def test_schema_from_mapping():
    fields = OrderedDict([
        ('foo', pa.int32()),
        ('bar', pa.string()),
        ('baz', pa.list_(pa.int8())),
    ])
    sch = pa.schema(fields)
    assert sch.names == ['foo', 'bar', 'baz']
    assert sch.types == [pa.int32(), pa.string(), pa.list_(pa.int8())]
    assert len(sch) == 3
    assert repr(sch) == """\
foo: int32
bar: string
baz: list<item: int8>
  child 0, item: int8"""

fields = OrderedDict([('foo', None)])
    with pytest.raises(TypeError):
        pa.schema(fields)

def test_schema_duplicate_fields():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('foo', pa.list_(pa.int8())),
    ]
    sch = pa.schema(fields)
    assert sch.names == ['foo', 'bar', 'foo']
    assert sch.types == [pa.int32(), pa.string(), pa.list_(pa.int8())]
    assert len(sch) == 3
    assert repr(sch) == """\
foo: int32
bar: string
foo: list<item: int8>
  child 0, item: int8"""

assert sch[0].name == 'foo'
    assert sch[0].type == fields[0].type
    with pytest.warns(FutureWarning):
        assert sch.field_by_name('bar') == fields[1]
    with pytest.warns(FutureWarning):
        assert sch.field_by_name('xxx') is None
    with pytest.warns((UserWarning, FutureWarning)):
        assert sch.field_by_name('foo') is None

# Schema::GetFieldIndex
    assert sch.get_field_index('foo') == -1

# Schema::GetAllFieldIndices
    assert sch.get_all_field_indices('foo') == [0, 2]

def test_field_flatten():
    f0 = pa.field('foo', pa.int32()).with_metadata({b'foo': b'bar'})
    assert f0.flatten() == [f0]

f1 = pa.field('bar', pa.float64(), nullable=False)
    ff = pa.field('ff', pa.struct([f0, f1]), nullable=False)
    assert ff.flatten() == [
        pa.field('ff.foo', pa.int32()).with_metadata({b'foo': b'bar'}),
        pa.field('ff.bar', pa.float64(), nullable=False)]  # XXX

# Nullable parent makes flattened child nullable
    ff = pa.field('ff', pa.struct([f0, f1]))
    assert ff.flatten() == [
        pa.field('ff.foo', pa.int32()).with_metadata({b'foo': b'bar'}),
        pa.field('ff.bar', pa.float64())]

fff = pa.field('fff', pa.struct([ff]))
    assert fff.flatten() == [pa.field('fff.ff', pa.struct([f0, f1]))]

def test_schema_add_remove_metadata():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ]

s1 = pa.schema(fields)

assert s1.metadata is None

metadata = {b'foo': b'bar', b'pandas': b'badger'}

s2 = s1.with_metadata(metadata)
    assert s2.metadata == metadata

s3 = s2.remove_metadata()
    assert s3.metadata is None

# idempotent
    s4 = s3.remove_metadata()
    assert s4.metadata is None

def test_schema_equals():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ]
    metadata = {b'foo': b'bar', b'pandas': b'badger'}

sch1 = pa.schema(fields)
    sch2 = pa.schema(fields)
    sch3 = pa.schema(fields, metadata=metadata)
    sch4 = pa.schema(fields, metadata=metadata)

assert sch1.equals(sch2, check_metadata=True)
    assert sch3.equals(sch4, check_metadata=True)
    assert sch1.equals(sch3)
    assert not sch1.equals(sch3, check_metadata=True)
    assert not sch1.equals(sch3, check_metadata=True)

del fields[-1]
    sch3 = pa.schema(fields)
    assert not sch1.equals(sch3)

def test_schema_equals_propagates_check_metadata():
    # ARROW-4088
    schema1 = pa.schema([
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string())
    ])
    schema2 = pa.schema([
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string(), metadata={'a': 'alpha'}),
    ])
    assert not schema1.equals(schema2, check_metadata=True)
    assert schema1.equals(schema2)

def test_schema_equals_invalid_type():
    # ARROW-5873
    schema = pa.schema([pa.field("a", pa.int64())])

for val in [None, 'string', pa.array([1, 2])]:
        with pytest.raises(TypeError):
            schema.equals(val)

def test_schema_equality_operators():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ]
    metadata = {b'foo': b'bar', b'pandas': b'badger'}

sch1 = pa.schema(fields)
    sch2 = pa.schema(fields)
    sch3 = pa.schema(fields, metadata=metadata)
    sch4 = pa.schema(fields, metadata=metadata)

assert sch1 == sch2
    assert sch3 == sch4

# __eq__ and __ne__ do not check metadata
    assert sch1 == sch3
    assert not sch1 != sch3

assert sch2 == sch4

# comparison with other types doesn't raise
    assert sch1 != []
    assert sch3 != 'foo'

def test_schema_get_fields():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ]

schema = pa.schema(fields)

assert schema.field('foo').name == 'foo'
    assert schema.field(0).name == 'foo'
    assert schema.field(-1).name == 'baz'

with pytest.raises(KeyError):
        schema.field('other')
    with pytest.raises(TypeError):
        schema.field(0.0)
    with pytest.raises(IndexError):
        schema.field(4)

def test_schema_negative_indexing():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ]

schema = pa.schema(fields)

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

with pytest.raises(IndexError):
        schema[-4]

with pytest.raises(IndexError):
        schema[3]

def test_schema_repr_with_dictionaries():
    fields = [
        pa.field('one', pa.dictionary(pa.int16(), pa.string())),
        pa.field('two', pa.int32())
    ]
    sch = pa.schema(fields)

expected = (
        """\
one: dictionary<values=string, indices=int16, ordered=0>
two: int32""")

assert repr(sch) == expected

def test_type_schema_pickling():
    cases = [
        pa.int8(),
        pa.string(),
        pa.binary(),
        pa.binary(10),
        pa.list_(pa.string()),
        pa.map_(pa.string(), pa.int8()),
        pa.struct([
            pa.field('a', 'int8'),
            pa.field('b', 'string')
        ]),
        pa.union([
            pa.field('a', pa.int8()),
            pa.field('b', pa.int16())
        ], pa.lib.UnionMode_SPARSE),
        pa.union([
            pa.field('a', pa.int8()),
            pa.field('b', pa.int16())
        ], pa.lib.UnionMode_DENSE),
        pa.time32('s'),
        pa.time64('us'),
        pa.date32(),
        pa.date64(),
        pa.timestamp('ms'),
        pa.timestamp('ns'),
        pa.decimal128(12, 2),
        pa.decimal256(76, 38),
        pa.field('a', 'string', metadata={b'foo': b'bar'}),
        pa.list_(pa.field("element", pa.int64())),
        pa.large_list(pa.field("element", pa.int64())),
        pa.map_(pa.field("key", pa.string(), nullable=False),
                pa.field("value", pa.int8()))
    ]

for val in cases:
        roundtripped = pickle.loads(pickle.dumps(val))
        assert val == roundtripped

fields = []
    for i, f in enumerate(cases):
        if isinstance(f, pa.Field):
            fields.append(f)
        else:
            fields.append(pa.field('_f{}'.format(i), f))

schema = pa.schema(fields, metadata={b'foo': b'bar'})
    roundtripped = pickle.loads(pickle.dumps(schema))
    assert schema == roundtripped

def test_empty_table():
    schema1 = pa.schema([
        pa.field('f0', pa.int64()),
        pa.field('f1', pa.dictionary(pa.int32(), pa.string())),
        pa.field('f2', pa.list_(pa.list_(pa.int64()))),
    ])
    # test it preserves field nullability
    schema2 = pa.schema([
        pa.field('a', pa.int64(), nullable=False),
        pa.field('b', pa.int64())
    ])

for schema in [schema1, schema2]:
        table = schema.empty_table()
        assert isinstance(table, pa.Table)
        assert table.num_rows == 0
        assert table.schema == schema

@pytest.mark.pandas
def test_schema_from_pandas():
    import pandas as pd
    inputs = [
        list(range(10)),
        pd.Categorical(list(range(10))),
        ['foo', 'bar', None, 'baz', 'qux'],
        np.array([
            '2007-07-13T01:23:34.123456789',
            '2006-01-13T12:34:56.432539784',
            '2010-08-13T05:46:57.437699912'
        ], dtype='datetime64[ns]'),
    ]
    if Version(pd.__version__) >= Version('1.0.0'):
        inputs.append(pd.array([1, 2, None], dtype=pd.Int32Dtype()))
    for data in inputs:
        df = pd.DataFrame({'a': data})
        schema = pa.Schema.from_pandas(df)
        expected = pa.Table.from_pandas(df).schema
        assert schema == expected

def test_schema_sizeof():
    schema = pa.schema([
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
    ])

assert sys.getsizeof(schema) > 30

schema2 = schema.with_metadata({"key": "some metadata"})
    assert sys.getsizeof(schema2) > sys.getsizeof(schema)
    schema3 = schema.with_metadata({"key": "some more metadata"})
    assert sys.getsizeof(schema3) > sys.getsizeof(schema2)

def test_schema_merge():
    a = pa.schema([
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ])
    b = pa.schema([
        pa.field('foo', pa.int32()),
        pa.field('qux', pa.bool_())
    ])
    c = pa.schema([
        pa.field('quux', pa.dictionary(pa.int32(), pa.string()))
    ])
    d = pa.schema([
        pa.field('foo', pa.int64()),
        pa.field('qux', pa.bool_())
    ])

result = pa.unify_schemas([a, b, c])
    expected = pa.schema([
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8())),
        pa.field('qux', pa.bool_()),
        pa.field('quux', pa.dictionary(pa.int32(), pa.string()))
    ])
    assert result.equals(expected)

with pytest.raises(pa.ArrowInvalid):
        pa.unify_schemas([b, d])

# ARROW-14002: Try with tuple instead of list
    result = pa.unify_schemas((a, b, c))
    assert result.equals(expected)

def test_undecodable_metadata():
    # ARROW-10214: undecodable metadata shouldn't fail repr()
    data1 = b'abcdef\xff\x00'
    data2 = b'ghijkl\xff\x00'
    schema = pa.schema(
        [pa.field('ints', pa.int16(), metadata={'key': data1})],
        metadata={'key': data2})
    assert 'abcdef' in str(schema)
    assert 'ghijkl' in str(schema)

cmd: