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_by_name('foo').name == 'foo'
assert sch.field_by_name('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_table_unsafe_casting():
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 = pa.Table.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 = pa.Table.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='Floating point value truncated'):
table.cast(target_schema)
casted_table = table.cast(target_schema, safe=False)
assert casted_table.equals(expected_table)
def test_table_safe_casting():
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 = pa.Table.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 = pa.Table.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)
def test_cast_from_null():
in_data = [None] * 3
in_type = pa.null()
out_types = [
pa.null(),
pa.uint8(),
pa.float16(),
pa.utf8(),
pa.binary(),
pa.binary(10),
pa.list_(pa.int16()),
pa.decimal128(19, 4),
pa.timestamp('us'),
pa.timestamp('us', tz='UTC'),
pa.timestamp('us', tz='Europe/Paris'),
pa.struct([pa.field('a', pa.int32()),
pa.field('b', pa.list_(pa.int8())),
pa.field('c', pa.string())]),
]
for out_type in out_types:
_check_cast_case((in_data, in_type, in_data, out_type))
out_types = [
pa.dictionary(pa.int32(), pa.string()),
pa.union([pa.field('a', pa.binary(10)),
pa.field('b', pa.string())], mode=pa.lib.UnionMode_DENSE),
pa.union([pa.field('a', pa.binary(10)),
pa.field('b', pa.string())], mode=pa.lib.UnionMode_SPARSE),
]
in_arr = pa.array(in_data, type=pa.null())
for out_type in out_types:
with pytest.raises(NotImplementedError):
in_arr.cast(out_type)
def test_orcfile_empty():
from pyarrow import orc
f = orc.ORCFile(path_for_orc_example('TestOrcFile.emptyFile'))
table = f.read()
assert table.num_rows == 0
schema = table.schema
expected_schema = pa.schema([
('boolean1', pa.bool_()),
('byte1', pa.int8()),
('short1', pa.int16()),
('int1', pa.int32()),
('long1', pa.int64()),
('float1', pa.float32()),
('double1', pa.float64()),
('bytes1', pa.binary()),
('string1', pa.string()),
('middle', pa.struct([
('list', pa.list_(pa.struct([
('int1', pa.int32()),
('string1', pa.string()),
]))),
])),
('list', pa.list_(pa.struct([
('int1', pa.int32()),
('string1', pa.string()),
]))),
('map', pa.list_(pa.struct([
('key', pa.string()),
('value', pa.struct([
('int1', pa.int32()),
('string1', pa.string()),
])),
]))),
])
assert schema == expected_schema
def test_type_schema_pickling():
cases = [
pa.int8(),
pa.string(),
pa.binary(),
pa.binary(10),
pa.list_(pa.string()),
pa.struct([
pa.field('a', 'int8'),
pa.field('b', 'string')
]),
pa.time32('s'),
pa.time64('us'),
pa.date32(),
pa.date64(),
pa.timestamp('ms'),
pa.timestamp('ns'),
pa.decimal(12, 2),
pa.field('a', 'string', metadata={b'foo': b'bar'})
]
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_custom_nulls(self):
# Infer nulls with custom values
opts = ConvertOptions(null_values=['Xxx', 'Zzz'])
rows = b"a,b,c,d\nZzz,Xxx,1,2\nXxx,#N/A,,Zzz\n"
table = self.read_bytes(rows, convert_options=opts)
schema = pa.schema([('a', pa.null()),
('b', pa.string()),
('c', pa.string()),
('d', pa.int64())])
assert table.schema == schema
assert table.to_pydict() == {
'a': [None, None],
'b': [u"Xxx", u"#N/A"],
'c': [u"1", u""],
'd': [2, None],
}
opts = ConvertOptions(null_values=[])
rows = b"a,b\n#N/A,\n"
table = self.read_bytes(rows, convert_options=opts)
schema = pa.schema([('a', pa.string()),
('b', pa.string())])
assert table.schema == schema
assert table.to_pydict() == {
'a': [u"#N/A"],
'b': [u""],
}
def test_sequence_utf8_to_unicode():
# ARROW-1225
data = [b'foo', None, b'bar']
arr = pa.array(data, type=pa.string())
assert arr[0].as_py() == u'foo'
# test a non-utf8 unicode string
val = (u'mañana').encode('utf-16-le')
with pytest.raises(pa.ArrowInvalid):
pa.array([val], type=pa.string())
def test_is_binary_string():
assert types.is_binary(pa.binary())
assert not types.is_binary(pa.string())
assert types.is_string(pa.string())
assert types.is_unicode(pa.string())
assert not types.is_string(pa.binary())
assert types.is_fixed_size_binary(pa.binary(5))
assert not types.is_fixed_size_binary(pa.binary())
def test_convert_options():
cls = ConvertOptions
opts = cls()
assert opts.check_utf8 is True
opts.check_utf8 = False
assert opts.check_utf8 is False
assert opts.strings_can_be_null is False
opts.strings_can_be_null = True
assert opts.strings_can_be_null is True
assert opts.column_types == {}
# Pass column_types as mapping
opts.column_types = {'b': pa.int16(), 'c': pa.float32()}
assert opts.column_types == {'b': pa.int16(), 'c': pa.float32()}
opts.column_types = {'v': 'int16', 'w': 'null'}
assert opts.column_types == {'v': pa.int16(), 'w': pa.null()}
# Pass column_types as schema
schema = pa.schema([('a', pa.int32()), ('b', pa.string())])
opts.column_types = schema
assert opts.column_types == {'a': pa.int32(), 'b': pa.string()}
# Pass column_types as sequence
opts.column_types = [('x', pa.binary())]
assert opts.column_types == {'x': pa.binary()}
with pytest.raises(TypeError, match='DataType expected'):
opts.column_types = {'a': None}
with pytest.raises(TypeError):
opts.column_types = 0
assert isinstance(opts.null_values, list)
assert '' in opts.null_values
assert 'N/A' in opts.null_values
opts.null_values = ['xxx', 'yyy']
assert opts.null_values == ['xxx', 'yyy']
assert isinstance(opts.true_values, list)
opts.true_values = ['xxx', 'yyy']
assert opts.true_values == ['xxx', 'yyy']
assert isinstance(opts.false_values, list)
opts.false_values = ['xxx', 'yyy']
assert opts.false_values == ['xxx', 'yyy']
opts = cls(check_utf8=False, column_types={'a': pa.null()},
null_values=['N', 'nn'], true_values=['T', 'tt'],
false_values=['F', 'ff'], strings_can_be_null=True)
assert opts.check_utf8 is False
assert opts.column_types == {'a': pa.null()}
assert opts.null_values == ['N', 'nn']
assert opts.false_values == ['F', 'ff']
assert opts.true_values == ['T', 'tt']
assert opts.strings_can_be_null is True
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)
assert schema1.equals(schema2, check_metadata=False)
def test_table_pickle():
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.loads(pickle.dumps(table))
result._validate()
assert result.equals(table)
def test_array_mixed_unicode_bytes():
values = [u'qux', b'foo', bytearray(b'barz')]
b_values = [b'qux', b'foo', b'barz']
u_values = [u'qux', u'foo', u'barz']
arr = pa.array(values)
expected = pa.array(b_values, type=pa.binary())
assert arr.type == pa.binary()
assert arr.equals(expected)
arr = pa.array(values, type=pa.string())
expected = pa.array(u_values, type=pa.string())
assert arr.type == pa.string()
assert arr.equals(expected)
def test_type_to_pandas_dtype():
M8_ns = np.dtype('datetime64[ns]')
cases = [
(pa.null(), np.float64),
(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_),
]
for arrow_type, numpy_type in cases:
assert arrow_type.to_pandas_dtype() == numpy_type
def test_unicode(self):
data = [u("foo"), u("bar"), None, u("arrow")]
arr = pyarrow.from_pylist(data)
assert len(arr) == 4
assert arr.null_count == 1
assert arr.type == pyarrow.string()
assert arr.to_pylist() == [u("foo"), u("bar"), None, u("arrow")]
def test_field(self):
t = arrow.string()
f = arrow.field('foo', t)
assert f.name == 'foo'
assert f.type is t
assert repr(f) == "Field('foo', type=string)"
def test_dictionary_type():
ty0 = pa.dictionary(pa.int32(), pa.string())
assert ty0.index_type == pa.int32()
assert ty0.value_type == pa.string()
assert ty0.ordered is False
ty1 = pa.dictionary(pa.int8(), pa.float64(), ordered=True)
assert ty1.index_type == pa.int8()
assert ty1.value_type == pa.float64()
assert ty1.ordered is True
# construct from non-arrow objects
ty2 = pa.dictionary('int8', 'string')
assert ty2.index_type == pa.int8()
assert ty2.value_type == pa.string()
assert ty2.ordered is False
def test_struct_from_tuples():
ty = pa.struct([pa.field('a', pa.int32()),
pa.field('b', pa.string()),
pa.field('c', pa.bool_())])
data = [(5, 'foo', True),
(6, 'bar', False)]
expected = [{'a': 5, 'b': 'foo', 'c': True},
{'a': 6, 'b': 'bar', 'c': False}]
arr = pa.array(data, type=ty)
data_as_ndarray = np.empty(len(data), dtype=object)
data_as_ndarray[:] = data
arr2 = pa.array(data_as_ndarray, type=ty)
assert arr.to_pylist() == expected
assert arr.equals(arr2)
# With omitted values
data = [(5, 'foo', None),
None,
(6, None, False)]
expected = [{'a': 5, 'b': 'foo', 'c': None},
None,
{'a': 6, 'b': None, 'c': False}]
arr = pa.array(data, type=ty)
assert arr.to_pylist() == expected
# Invalid tuple size
for tup in [(5, 'foo'), (), ('5', 'foo', True, None)]:
with pytest.raises(ValueError, match="(?i)tuple size"):
pa.array([tup], type=ty)
def test_is_union():
for mode in [pa.lib.UnionMode_SPARSE, pa.lib.UnionMode_DENSE]:
assert types.is_union(pa.union([pa.field('a', pa.int32()),
pa.field('b', pa.int8()),
pa.field('c', pa.string())],
mode=mode))
assert not types.is_union(pa.list_(pa.int32()))
def test_empty_cast():
types = [
pa.null(),
pa.bool_(),
pa.int8(),
pa.int16(),
pa.int32(),
pa.int64(),
pa.uint8(),
pa.uint16(),
pa.uint32(),
pa.uint64(),
pa.float16(),
pa.float32(),
pa.float64(),
pa.date32(),
pa.date64(),
pa.binary(),
pa.binary(length=4),
pa.string(),
]
for (t1, t2) in itertools.product(types, types):
try:
# ARROW-4766: Ensure that supported types conversion don't segfault
# on empty arrays of common types
pa.array([], type=t1).cast(t2)
except pa.lib.ArrowNotImplementedError:
continue
def test_sequence_unicode():
data = [u'foo', u'bar', None, u'mañana']
arr = pa.array(data)
assert len(arr) == 4
assert arr.null_count == 1
assert arr.type == pa.string()
assert arr.to_pylist() == data
def test_unicode(self):
data = [u'foo', u'bar', None, u'mañana']
arr = pyarrow.from_pylist(data)
assert len(arr) == 4
assert arr.null_count == 1
assert arr.type == pyarrow.string()
assert arr.to_pylist() == data
def test_struct_from_dicts_inference():
expected_type = pa.struct([pa.field('a', pa.int64()),
pa.field('b', pa.string()),
pa.field('c', pa.bool_())])
data = [{'a': 5, 'b': u'foo', 'c': True},
{'a': 6, 'b': u'bar', 'c': False}]
arr = pa.array(data)
check_struct_type(arr.type, expected_type)
assert arr.to_pylist() == data
# With omitted values
data = [{'a': 5, 'c': True},
None,
{},
{'a': None, 'b': u'bar'}]
expected = [{'a': 5, 'b': None, 'c': True},
None,
{'a': None, 'b': None, 'c': None},
{'a': None, 'b': u'bar', 'c': None}]
arr = pa.array(data)
data_as_ndarray = np.empty(len(data), dtype=object)
data_as_ndarray[:] = data
arr2 = pa.array(data)
check_struct_type(arr.type, expected_type)
assert arr.to_pylist() == expected
assert arr.equals(arr2)
# Nested
expected_type = pa.struct([
pa.field('a', pa.struct([pa.field('aa', pa.list_(pa.int64())),
pa.field('ab', pa.bool_())])),
pa.field('b', pa.string())])
data = [{'a': {'aa': [5, 6], 'ab': True}, 'b': 'foo'},
{'a': {'aa': None, 'ab': False}, 'b': None},
{'a': None, 'b': 'bar'}]
arr = pa.array(data)
assert arr.to_pylist() == data
# Edge cases
arr = pa.array([{}])
assert arr.type == pa.struct([])
assert arr.to_pylist() == [{}]
# Mixing structs and scalars is rejected
with pytest.raises((pa.ArrowInvalid, pa.ArrowTypeError)):
pa.array([1, {'a': 2}])
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_infer_lists(self):
data = OrderedDict([
('nan_ints', [[None, 1], [2, 3]]),
('ints', [[0, 1], [2, 3]]),
('strs', [[None, u'b'], [u'c', u'd']]),
('nested_strs', [[[None, u'b'], [u'c', u'd']], None])
])
df = pd.DataFrame(data)
expected_schema = pa.schema([
pa.field('nan_ints', pa.list_(pa.int64())),
pa.field('ints', pa.list_(pa.int64())),
pa.field('strs', pa.list_(pa.string())),
pa.field('nested_strs', pa.list_(pa.list_(pa.string())))
])
self._check_pandas_roundtrip(df, expected_schema=expected_schema)
def test_unicode(self):
repeats = 1000
values = [u'foo', None, u'bar', u'mañana', np.nan]
df = pd.DataFrame({'strings': values * repeats})
field = pa.field('strings', pa.string())
schema = pa.schema([field])
self._check_pandas_roundtrip(df, expected_schema=schema)
def test_chunked_array_basics():
data = pa.chunked_array([], type=pa.string())
assert data.type == pa.string()
assert data.to_pylist() == []
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
def test_struct_from_mixed_sequence():
# It is forbidden to mix dicts and tuples when initializing a struct array
ty = pa.struct([pa.field('a', pa.int32()),
pa.field('b', pa.string()),
pa.field('c', pa.bool_())])
data = [(5, 'foo', True),
{'a': 6, 'b': 'bar', 'c': False}]
with pytest.raises(TypeError):
pa.array(data, type=ty)
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_cast_binary_to_utf8():
binary_arr = pa.array([b'foo', b'bar', b'baz'], type=pa.binary())
utf8_arr = binary_arr.cast(pa.utf8())
expected = pa.array(['foo', 'bar', 'baz'], type=pa.utf8())
assert utf8_arr.equals(expected)
non_utf8_values = [(u'mañana').encode('utf-16-le')]
non_utf8_binary = pa.array(non_utf8_values)
assert non_utf8_binary.type == pa.binary()
with pytest.raises(ValueError):
non_utf8_binary.cast(pa.string())
non_utf8_all_null = pa.array(non_utf8_values, mask=np.array([True]),
type=pa.binary())
# No error
casted = non_utf8_all_null.cast(pa.string())
assert casted.null_count == 1
"""Conversion between different types of arrays"""
import numpy as np
import pyarrow as pa
import vaex.utils
supported_arrow_array_types = (pa.Array, pa.ChunkedArray)
supported_array_types = (np.ndarray, ) + supported_arrow_array_types
string_types = [pa.string(), pa.large_string()]
def full(n, value, dtype):
from .datatype import DataType
dtype = DataType(dtype)
values = np.full(n, value, dtype=dtype.numpy)
if dtype.is_arrow:
return pa.array(values)
else:
return values
def is_arrow_array(ar):
return isinstance(ar, supported_arrow_array_types)
def is_numpy_array(ar):
return isinstance(ar, np.ndarray)
def filter(ar, boolean_mask):
if isinstance(ar, supported_arrow_array_types):
tf.io.FixedLenFeature((1, ), tf.int64, default_value=[0]),
'image/class/text':
tf.io.FixedLenFeature((1, ), tf.string, default_value=[b'']),
'image/format':
tf.io.FixedLenFeature((1, ), tf.string, default_value=[b'']),
'image/filename':
tf.io.FixedLenFeature((1, ), tf.string, default_value=[b'']),
'image/encoded':
tf.io.FixedLenFeature((1, ), tf.string, default_value=[b''])
}
parquet_schema = {
'image/height': pa.int64(),
'image/width': pa.int64(),
'image/channels': pa.int64(),
'image/colorspace': pa.string(),
'image/class/label': pa.int64(),
'image/class/text': pa.string(),
'image/format': pa.string(),
'image/filename': pa.string(),
'image/encoded': pa.binary()
}
def reformat_row(row):
import pyarrow as pa
out_row = {}
for key, val in row.items():
out_type = parquet_schema[key]
np_val = val.numpy()
def test_try_incompatible_extension_type(self):
arr = pa.array(TypedSequence(["foo", "bar"], try_type=Array2DExtensionType((1, 3), "int64")))
self.assertEqual(arr.type, pa.string())
def lambda_handler(event, _):
"""Lambda entry point"""
source_path = 's3://' + event['bucket'] + '/' + event['source_key']
target_path = 's3://' + event['bucket'] + '/' + event['target_key']
print('Source: ' + source_path)
print('Target: ' + target_path)
s3_client = boto3.client('s3')
found = False
# Ensure file exists before we actually run the conversion
while not found:
try:
s3_client.head_object(Bucket=event['bucket'],
Key=event['source_key'])
except botocore.exceptions.ClientError:
print('Waiting for: "' + source_path + '" to exist')
time.sleep(10)
else:
print('Found: "' + source_path + '"')
found = True
s3fs_source = s3fs.S3FileSystem()
s3fs_target = s3fs.S3FileSystem()
with s3fs_source.open(source_path, 'rb') as source_file, \
s3fs_target.open(target_path, 'wb') as target_file:
# Open a stream reader for the csv file
csv_stream = pd.read_csv(source_file,
skiprows=0,
compression='gzip',
dtype=object,
iterator=True,
chunksize=100000)
parquet_writer = None
for i, chunk in enumerate(csv_stream):
print('Reading chunk: ' + str(i))
# First chunck, get schema and setup writer
if not parquet_writer:
# Fetch columns from header, hardcodes type to string
columns = [
pa.field(column, pa.string()) for column in chunk.columns
]
# Generate schema from columns
parquet_schema = pa.schema(columns)
# Open a writer to S3
parquet_writer = pq.ParquetWriter(target_file,
parquet_schema,
compression='snappy')
# Read the first chunk
table = pa.Table.from_pandas(chunk, preserve_index=False)
print('Writing chunk: ' + str(i))
parquet_writer.write_table(table)
parquet_writer.close()
print('Done processing "' + source_path + '"')
return event['target_key']
def test_string(self):
data = ['foo', b'bar', None, 'arrow']
arr = pyarrow.from_pylist(data)
assert len(arr) == 4
assert arr.null_count == 1
assert arr.type == pyarrow.string()
def test_str_length(array, expected, offset):
array = pa.array(array, pa.string())[offset:]
np.testing.assert_array_equal(
str_length(NumbaStringArray.make(array)),
np.asarray(expected[offset:], dtype=np.int32),
)
def test_is_datetime():
assert is_datetime(pyarrow.timestamp("us", tz=None))
assert not is_datetime(pyarrow.timestamp("ms", tz=None))
assert not is_datetime(pyarrow.timestamp("us", tz="UTC"))
assert not is_datetime(pyarrow.timestamp("ns", tz="UTC"))
assert not is_datetime(pyarrow.string())
import pyarrow as pa
PQ_SCHEMAS = dict()
# site_visits
fields = [
pa.field('visit_id', pa.int64(), nullable=False),
pa.field('crawl_id', pa.uint32(), nullable=False),
pa.field('instance_id', pa.uint32(), nullable=False),
pa.field('site_url', pa.string(), nullable=False),
pa.field('site_rank', pa.uint32())
]
PQ_SCHEMAS['site_visits'] = pa.schema(fields)
# flash_cookies
fields = [
pa.field('crawl_id', pa.uint32(), nullable=False),
pa.field('visit_id', pa.int64(), nullable=False),
pa.field('instance_id', pa.uint32(), nullable=False),
pa.field('domain', pa.string()),
pa.field('filename', pa.string()),
pa.field('local_path', pa.string()),
pa.field('key', pa.string()),
pa.field('content', pa.string())
]
PQ_SCHEMAS['flash_cookies'] = pa.schema(fields)
# crawl_history
fields = [
pa.field('crawl_id', pa.uint32(), nullable=False),
pa.field('visit_id', pa.int64(), nullable=False),
def test_sql(redshift_table, postgresql_table, mysql_table,
databases_parameters, db_type):
if db_type == "postgresql":
table = postgresql_table
elif db_type == "mysql":
table = mysql_table
else:
table = redshift_table
df = get_df()
if db_type == "redshift":
df.drop(["binary"], axis=1, inplace=True)
engine = wr.catalog.get_engine(connection=f"aws-data-wrangler-{db_type}",
echo=False)
index = True if engine.name == "redshift" else False
wr.db.to_sql(
df=df,
con=engine,
name=table,
schema=databases_parameters[db_type]["schema"],
if_exists="replace",
index=index,
index_label=None,
chunksize=None,
method=None,
dtype={"iint32": sqlalchemy.types.Integer},
)
df = wr.db.read_sql_query(
sql=f"SELECT * FROM {databases_parameters[db_type]['schema']}.{table}",
con=engine)
ensure_data_types(df, has_list=False)
engine = wr.db.get_engine(
db_type=db_type,
host=databases_parameters[db_type]["host"],
port=databases_parameters[db_type]["port"],
database=databases_parameters[db_type]["database"],
user=databases_parameters["user"],
password=databases_parameters["password"],
echo=False,
)
dfs = wr.db.read_sql_query(
sql=f"SELECT * FROM {databases_parameters[db_type]['schema']}.{table}",
con=engine,
chunksize=1,
dtype={
"iint8": pa.int8(),
"iint16": pa.int16(),
"iint32": pa.int32(),
"iint64": pa.int64(),
"float": pa.float32(),
"double": pa.float64(),
"decimal": pa.decimal128(3, 2),
"string_object": pa.string(),
"string": pa.string(),
"date": pa.date32(),
"timestamp": pa.timestamp(unit="ns"),
"binary": pa.binary(),
"category": pa.float64(),
},
)
for df in dfs:
ensure_data_types(df, has_list=False)
if db_type != "redshift":
account_id = boto3.client("sts").get_caller_identity().get("Account")
engine = wr.catalog.get_engine(
connection=f"aws-data-wrangler-{db_type}", catalog_id=account_id)
wr.db.to_sql(
df=pd.DataFrame({"col0": [1, 2, 3]}, dtype="Int32"),
con=engine,
name=table,
schema=databases_parameters[db_type]["schema"],
if_exists="replace",
index=True,
index_label="index",
)
schema = None
if db_type == "postgresql":
schema = databases_parameters[db_type]["schema"]
df = wr.db.read_sql_table(con=engine,
table=table,
schema=schema,
index_col="index")
assert df.shape == (3, 1)
def _text_replace_case_sensitive(data: pa.Array, pat: str, repl: str,
max_repl: int) -> pa.Array:
"""
Replace occurrences of ``pat`` with ``repl`` in the Series/Index with some other string. For every
row, only the first ``max_repl`` replacements will be performed. If ``max_repl = -1`` we consider that
we have no limit for the number of replacements.
This implementation does basic byte-by-byte comparison and is independent
of any locales or encodings.
"""
# Convert to UTF-8 bytes
pat_bytes: bytes = pat.encode()
repl_bytes: bytes = repl.encode()
offsets_buffer, data_buffer = _extract_string_buffers(data)
if data.null_count == 0:
valid_buffer = np.empty(0, dtype=np.uint8)
else:
valid_buffer = _buffer_to_view(data.buffers()[0])
if len(pat) > 0:
output_t = _text_replace_case_sensitive_numba(
len(data),
valid_buffer,
data.offset,
offsets_buffer,
data_buffer,
pat_bytes,
repl_bytes,
max_repl,
)
else:
output_t = _text_replace_case_sensitive_empty_pattern(
len(data),
valid_buffer,
data.offset,
offsets_buffer,
data_buffer,
repl_bytes,
max_repl,
)
output_offsets, output_buffer = output_t
if data.null_count == 0:
output_valid = None
else:
output_valid = data.buffers()[0].slice(data.offset // 8)
if data.offset % 8 != 0:
output_valid = shift_unaligned_bitmap(output_valid,
data.offset % 8, len(data))
buffers = [
output_valid,
pa.py_buffer(output_offsets),
pa.py_buffer(output_buffer)
]
return pa.Array.from_buffers(pa.string(), len(data), buffers,
data.null_count)
def test_type_string():
t = pa.string()
assert str(t) == 'string'
def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
"""Set one or more values inplace.
Parameters
----------
key : int, ndarray, or slice
When called from, e.g. ``Series.__setitem__``, ``key`` will be
one of
* scalar int
* ndarray of integers.
* boolean ndarray
* slice object
value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
value or values to be set of ``key``.
Returns
-------
None
"""
key = check_array_indexer(self, key)
if is_integer(key):
key = cast(int, key)
if not is_scalar(value):
raise ValueError("Must pass scalars with scalar indexer")
elif isna(value):
value = None
elif not isinstance(value, str):
raise ValueError("Scalar must be NA or str")
# Slice data and insert in-between
new_data = [
*self._data[0:key].chunks,
pa.array([value], type=pa.string()),
*self._data[(key + 1):].chunks,
]
self._data = pa.chunked_array(new_data)
else:
# Convert to integer indices and iteratively assign.
# TODO: Make a faster variant of this in Arrow upstream.
# This is probably extremely slow.
# Convert all possible input key types to an array of integers
if isinstance(key, slice):
key_array = np.array(range(len(self))[key])
elif is_bool_dtype(key):
# TODO(ARROW-9430): Directly support setitem(booleans)
key_array = np.argwhere(key).flatten()
else:
# TODO(ARROW-9431): Directly support setitem(integers)
key_array = np.asanyarray(key)
if is_scalar(value):
value = np.broadcast_to(value, len(key_array))
else:
value = np.asarray(value)
if len(key_array) != len(value):
raise ValueError("Length of indexer and values mismatch")
for k, v in zip(key_array, value):
self[k] = v
def __init__(self, expressions, dtype=None, shape=None, fill_value=None):
self.is_masked = any([e.is_masked for e in expressions])
self.fill_value = fill_value
if self.is_masked and fill_value is None:
for expression in expressions:
if expression.is_masked:
try:
# fast path
self.fill_value = expression[0:1].fill_value
break
except: # noqa
# slower path (we have to evaluate everything)
self.fill_value = expression.values.fill_value
break
else:
raise ValueError(
'Concatenating expressions with masked values, but no fill value is found'
)
if dtype is None:
dtypes = [e.dtype for e in expressions]
any_strings = any([is_string_type(dtype) for dtype in dtypes])
if any_strings:
self.dtype = pa.string(
) # TODO: how do we know it should not be large_string?
else:
# np.datetime64/timedelta64 and find_common_type don't mix very well
if all([dtype == 'datetime64' for dtype in dtypes]):
self.dtype = dtypes[0]
elif all([dtype == 'timedelta64' for dtype in dtypes]):
self.dtype = dtypes[0]
else:
if all([dtype == dtypes[0] for dtype in dtypes
]): # find common types doesn't always behave well
self.dtype = dtypes[0]
if any([dtype.kind in 'SU' for dtype in dtypes
]): # strings are also done manually
if all([dtype.kind in 'SU' for dtype in dtypes]):
index = np.argmax(
[dtype.itemsize for dtype in dtypes])
self.dtype = dtypes[index]
else:
index = np.argmax([
df.columns[self.column_name].astype(
'O').astype('U').dtype.itemsize
for df in dfs
])
self.dtype = dfs[index].columns[
self.column_name].astype('O').astype('U').dtype
else:
self.dtype = np.find_common_type(
[k.numpy for k in dtypes], [])
logger.debug("common type for %r is %r", dtypes,
self.dtype)
# make sure all expression are the same type
self.expressions = [
e if vaex.array_types.same_type(e.dtype, self.dtype) else
e.astype(self.dtype) for e in expressions
]
else:
# if dtype is given, we assume every expression/column is the same dtype
self.dtype = dtype
self.expressions = expressions[:]
if shape is not None:
self.shape = (len(self), ) + shape
else:
self.shape = (len(self), ) + self.expressions[0].evaluate(
0, 1, array_type='numpy', parallel=False).shape[1:]
for i in range(1, len(self.expressions)):
expression = self.expressions[i]
shape_i = (len(self), ) + expressions[i].evaluate(
0, 1, array_type='numpy', parallel=False).shape[1:]
if self.shape != shape_i:
raise ValueError(
"shape of of expression %s, array index 0, is %r and is incompatible with the shape of the same column of array index %d, %r"
% (self.expressions[0], self.shape, i, shape_i))
assert result2.equals(arr)
def test_cast_date64_to_int():
arr = pa.array(np.array([0, 1, 2], dtype='int64'), type=pa.date64())
expected = pa.array([0, 1, 2], type='i8')
result = arr.cast('i8')
assert result.equals(expected)
@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()),
([], None), ([[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_array_pickle(data, typ):
# Allocate here so that we don't have any Arrow data allocated.
# This is needed to ensure that allocator tests can be reliable.
array = pa.array(data, type=typ)
result = pickle.loads(pickle.dumps(array))
assert array.equals(result)
@pytest.mark.parametrize('narr', [
np.arange(10, dtype=np.int64),
(pa.int8(), 'int8'),
(pa.int16(), 'int16'),
(pa.int32(), 'int32'),
(pa.int64(), 'int64'),
(pa.uint8(), 'uint8'),
(pa.uint16(), 'uint16'),
(pa.uint32(), 'uint32'),
(pa.uint64(), 'uint64'),
(pa.float16(), 'float16'),
(pa.float32(), 'float32'),
(pa.float64(), 'float64'),
(pa.date32(), 'date'),
(pa.date64(), 'date'),
(pa.binary(), 'bytes'),
(pa.binary(length=4), 'bytes'),
(pa.string(), 'unicode'),
(pa.list_(pa.list_(pa.int16())), 'list[list[int16]]'),
(pa.decimal128(18, 3), 'decimal'),
(pa.timestamp('ms'), 'datetime'),
(pa.timestamp('us', 'UTC'), 'datetimetz'),
(pa.time32('s'), 'time'),
(pa.time64('us'), 'time')
]
)
def test_logical_type(type, expected):
assert get_logical_type(type) == expected
def test_array_uint64_from_py_over_range():
arr = pa.array([2 ** 63], type=pa.uint64())
expected = pa.array(np.array([2 ** 63], dtype='u8'))
def setUp(self):
# Reducing the size of thread pools. Without this test execution may fail in
# environments with limited amount of resources.
filebasedsource.MAX_NUM_THREADS_FOR_SIZE_ESTIMATION = 2
self.temp_dir = tempfile.mkdtemp()
self.RECORDS = [{
'name': 'Thomas',
'favorite_number': 1,
'favorite_color': 'blue'
}, {
'name': 'Henry',
'favorite_number': 3,
'favorite_color': 'green'
}, {
'name': 'Toby',
'favorite_number': 7,
'favorite_color': 'brown'
}, {
'name': 'Gordon',
'favorite_number': 4,
'favorite_color': 'blue'
}, {
'name': 'Emily',
'favorite_number': -1,
'favorite_color': 'Red'
}, {
'name': 'Percy',
'favorite_number': 6,
'favorite_color': 'Green'
}, {
'name': 'Peter',
'favorite_number': 3,
'favorite_color': None
}]
self.SCHEMA = pa.schema([('name', pa.string(), False),
('favorite_number', pa.int64(), False),
('favorite_color', pa.string())])
self.SCHEMA96 = pa.schema([('name', pa.string(), False),
('favorite_number', pa.timestamp('ns'),
False), ('favorite_color', pa.string())])
self.RECORDS_NESTED = [{
'items': [
{
'name': 'Thomas',
'favorite_number': 1,
'favorite_color': 'blue'
},
{
'name': 'Henry',
'favorite_number': 3,
'favorite_color': 'green'
},
]
}, {
'items': [
{
'name': 'Toby',
'favorite_number': 7,
'favorite_color': 'brown'
},
]
}]
self.SCHEMA_NESTED = pa.schema([
('items',
pa.list_(
pa.struct([('name', pa.string(), False),
('favorite_number', pa.int64(), False),
('favorite_color', pa.string())])))
])
"FLOAT64",
pyarrow.float64().id:
"FLOAT64",
pyarrow.time32("ms").id:
"TIME",
pyarrow.time64("ns").id:
"TIME",
pyarrow.timestamp("ns").id:
"TIMESTAMP",
pyarrow.date32().id:
"DATE",
pyarrow.date64().id:
"DATETIME", # because millisecond resolution
pyarrow.binary().id:
"BYTES",
pyarrow.string().id:
"STRING", # also alias for pyarrow.utf8()
pyarrow.decimal128(38, scale=9).id:
"NUMERIC",
# The exact decimal's scale and precision are not important, as only
# the type ID matters, and it's the same for all decimal128 instances.
}
else: # pragma: NO COVER
BQ_TO_ARROW_SCALARS = {} # pragma: NO COVER
ARROW_SCALAR_IDS_TO_BQ = {} # pragma: NO_COVER
def bq_to_arrow_struct_data_type(field):
arrow_fields = []
for subfield in field.fields:
def infer_schema(self, data):
"""
Infer a schema for a given data input. The schema can be used to test with schema validator.
This function currently supports DataFrame, Numpy, Dictionary, List and basic python types.::
data = pandas.DataFrame(...)
schema = infer_schema(data)
This function returns None if it can not infer the schema.
"""
schema = None
if data is None:
schema = pa.null()
elif isinstance(data, dict):
schema = {'type': dict, 'fields': {}}
for key, value in data.items():
schema['fields'][key] = self.infer_schema(value)
elif isinstance(data, pd.DataFrame):
schema = {'type': pd.DataFrame, 'fields': {}}
# sample the table to get the schema
pa_schema = pa.Table.from_pandas(data[:_SAMPLE_SIZE],
preserve_index=False).schema
for i, name in enumerate(pa_schema.names):
schema['fields'][name] = pa_schema.types[i]
elif isinstance(data, pd.Series):
schema = {
'type': pd.Series,
'item': pa.Array.from_pandas(data).type,
}
elif isinstance(data, np.ndarray):
pa_type = pa.from_numpy_dtype(
data.dtype) if data.dtype.num != 17 else pa.string()
if len(data.shape) == 1: # 1d array
schema = {
'type': np.ndarray,
'item': pa_type,
}
else:
shape = [
v if i != 0 else None for i, v in enumerate(data.shape)
]
schema = {
'type': np.ndarray,
'item': pa_type,
'shape': tuple(shape),
}
elif isinstance(data, pa.Table):
schema = data.schema
elif isinstance(data, (list, tuple)) and len(data) > 0:
# try to infer type of the data
current_type = self.infer_schema(data[0])
for i in range(1, min(len(data), _SAMPLE_SIZE)):
new_type = self.infer_schema(data[i])
if new_type != current_type:
current_type = None
break
# does not support multiple type yet
if current_type:
if isinstance(current_type, pa.DataType):
schema = pa.list_(current_type)
else:
schema = {'type': list, 'item': current_type}
elif type(data) in _python_mapping:
schema = _python_mapping[type(data)]()
else:
return {'type': type(data)}
return schema
MANY_TYPES = [
pa.null(),
pa.bool_(),
pa.int32(),
pa.time32('s'),
pa.time64('us'),
pa.date32(),
pa.timestamp('us'),
pa.timestamp('us', tz='UTC'),
pa.timestamp('us', tz='Europe/Paris'),
pa.float16(),
pa.float32(),
pa.float64(),
pa.decimal128(19, 4),
pa.string(),
pa.binary(),
pa.binary(10),
pa.list_(pa.int32()),
pa.struct([
pa.field('a', pa.int32()),
pa.field('b', pa.int8()),
pa.field('c', pa.string())
]),
pa.union([pa.field('a', pa.binary(10)),
pa.field('b', pa.string())],
mode=pa.lib.UnionMode_DENSE),
pa.union([pa.field('a', pa.binary(10)),
pa.field('b', pa.string())],
mode=pa.lib.UnionMode_SPARSE),
# XXX Needs array pickling
def test_try_incompatible_type(self):
arr = pa.array(TypedSequence(["foo", "bar"], try_type=pa.int64()))
self.assertEqual(arr.type, pa.string())
import pyarrow as pa
PQ_SCHEMAS = dict()
# site_visits
fields = [
pa.field('visit_id', pa.int64(), nullable=False),
pa.field('crawl_id', pa.int32(), nullable=False),
pa.field('instance_id', pa.int32(), nullable=False),
pa.field('site_url', pa.string(), nullable=False)
]
PQ_SCHEMAS['site_visits'] = pa.schema(fields)
# flash_cookies
fields = [
pa.field('crawl_id', pa.int32(), nullable=False),
pa.field('visit_id', pa.int64(), nullable=False),
pa.field('instance_id', pa.int32(), nullable=False),
pa.field('domain', pa.string()),
pa.field('filename', pa.string()),
pa.field('local_path', pa.string()),
pa.field('key', pa.string()),
pa.field('content', pa.string())
]
PQ_SCHEMAS['flash_cookies'] = pa.schema(fields)
# profile_cookies
fields = [
pa.field('crawl_id', pa.int32(), nullable=False),
pa.field('visit_id', pa.int64(), nullable=False),
pa.field('instance_id', pa.int32(), nullable=False),
def test_is_dictionary():
assert types.is_dictionary(pa.dictionary(pa.int32(), pa.string()))
assert not types.is_dictionary(pa.int32())
MANY_TYPES = [
pa.null(),
pa.bool_(),
pa.int32(),
pa.time32('s'),
pa.time64('us'),
pa.date32(),
pa.timestamp('us'),
pa.timestamp('us', tz='UTC'),
pa.timestamp('us', tz='Europe/Paris'),
pa.float16(),
pa.float32(),
pa.float64(),
pa.decimal128(19, 4),
pa.string(),
pa.binary(),
pa.binary(10),
pa.list_(pa.int32()),
pa.struct([pa.field('a', pa.int32()),
pa.field('b', pa.int8()),
pa.field('c', pa.string())]),
pa.union([pa.field('a', pa.binary(10)),
pa.field('b', pa.string())], mode=pa.lib.UnionMode_DENSE),
pa.union([pa.field('a', pa.binary(10)),
pa.field('b', pa.string())], mode=pa.lib.UnionMode_SPARSE),
# XXX Needs array pickling
# pa.dictionary(pa.int32(), pa.array(['a', 'b', 'c'])),
]
def test_isnull(array, expected, offset):
array = pa.array(array, pa.string())[offset:]
np.testing.assert_array_equal(
isnull(NumbaStringArray.make(array)),
np.asarray(expected[offset:], dtype=np.bool),
)
('uint32', range(0, 10)), ('int32', range(0, 10)),
('uint64', range(0, 10)), ('int64', range(0, 10)),
('float', [0.0, 0.1, 0.2]),
('double', [0.0, 0.1, 0.2]),
('string', ['a', 'b', 'c']),
('binary', [b'a', b'b', b'c']),
(pa.binary(3), [b'abc', b'bcd', b'cde'])])
def test_cast_identities(ty, values):
arr = pa.array(values, type=ty)
assert arr.cast(ty).equals(arr)
pickle_test_parametrize = 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()),
([], None), ([[1, 2], [3]], pa.list_(pa.int64())),
([[4, 5], [6]], pa.large_list(pa.int16())),
([['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())]))])
@pickle_test_parametrize
def test_array_pickle(data, typ):
# Allocate here so that we don't have any Arrow data allocated.
# This is needed to ensure that allocator tests can be reliable.
array = pa.array(data, type=typ)
for proto in range(0, pickle.HIGHEST_PROTOCOL + 1):
result = pickle.loads(pickle.dumps(array, proto))
def array_inhom_chunks():
chunk1 = pa.array(list("abc"), pa.string())
chunk2 = pa.array(list("12345"), pa.string())
chunk3 = pa.array(list("Z"), pa.string())
chunked_array = pa.chunked_array([chunk1, chunk2, chunk3])
return fr.FletcherChunkedArray(chunked_array)
with self.assertRaises(OverflowError):
_ = pa.array(TypedSequence([["x" * 1024]] * ((2 << 20) + 1))) # ListArray with a bit more than 2GB
def _check_output(output, expected_num_chunks: int):
stream = pa.BufferReader(output) if isinstance(output, pa.Buffer) else pa.memory_map(output)
f = pa.ipc.open_stream(stream)
pa_table: pa.Table = f.read_all()
assert len(pa_table.to_batches()) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize("writer_batch_size", [None, 1, 10])
@pytest.mark.parametrize(
"fields", [None, {"col_1": pa.string(), "col_2": pa.int64()}, {"col_1": pa.string(), "col_2": pa.int32()}]
)
def test_write(fields, writer_batch_size):
output = pa.BufferOutputStream()
schema = pa.schema(fields) if fields else None
with ArrowWriter(stream=output, schema=schema, writer_batch_size=writer_batch_size) as writer:
writer.write({"col_1": "foo", "col_2": 1})
writer.write({"col_1": "bar", "col_2": 2})
num_examples, num_bytes = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
fields = {"col_1": pa.string(), "col_2": pa.int64()}
assert writer._schema == pa.schema(fields, metadata=writer._schema.metadata)
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1)
def __call__(self):
return pa.string()
('string', ['a', 'b', 'c']),
('binary', [b'a', b'b', b'c']),
(pa.binary(3), [b'abc', b'bcd', b'cde'])
])
def test_cast_identities(ty, values):
arr = pa.array(values, type=ty)
assert arr.cast(ty).equals(arr)
pickle_test_parametrize = 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()),
([], None),
([[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())]))
]
)
@pickle_test_parametrize
def test_array_pickle(data, typ):
# Allocate here so that we don't have any Arrow data allocated.
# This is needed to ensure that allocator tests can be reliable.
array = pa.array(data, type=typ)
for proto in range(0, pickle.HIGHEST_PROTOCOL + 1):
def test_chunked_array_basics():
data = pa.chunked_array([], type=pa.string())
assert data.to_pylist() == []
with pytest.raises(ValueError):
pa.chunked_array([])
