本文整理匯總了Python中pyarrow.decimal128方法的典型用法代碼示例。如果您正苦於以下問題：Python pyarrow.decimal128方法的具體用法？Python pyarrow.decimal128怎麽用？Python pyarrow.decimal128使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pyarrow的用法示例。
在下文中一共展示了pyarrow.decimal128方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: determine_pyarrow_types
# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import decimal128 [as 別名]
def determine_pyarrow_types(self, cols):
pa_types = []
for col in cols:
sa_class = col.type.__class__
if isinstance(col.type, (sa.types.NUMERIC, sa.types.DECIMAL)):
pa_type = functools.partial(pa.decimal128, col.type.precision, col.type.scale)
else:
pa_type = self.pyarrow_type_map[sa_class]
pa_types.append(pa_type)
return pa_types 示例2: test_arrow_schema_convertion
# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import decimal128 [as 別名]
def test_arrow_schema_convertion():
fields = [
pa.field('string', pa.string()),
pa.field('int8', pa.int8()),
pa.field('int16', pa.int16()),
pa.field('int32', pa.int32()),
pa.field('int64', pa.int64()),
pa.field('float', pa.float32()),
pa.field('double', pa.float64()),
pa.field('bool', pa.bool_(), False),
pa.field('fixed_size_binary', pa.binary(10)),
pa.field('variable_size_binary', pa.binary()),
pa.field('decimal', pa.decimal128(3, 4)),
pa.field('timestamp_s', pa.timestamp('s')),
pa.field('timestamp_ns', pa.timestamp('ns')),
pa.field('date_32', pa.date32()),
pa.field('date_64', pa.date64())
]
arrow_schema = pa.schema(fields)
mock_dataset = _mock_parquet_dataset([], arrow_schema)
unischema = Unischema.from_arrow_schema(mock_dataset)
for name in arrow_schema.names:
assert getattr(unischema, name).name == name
assert getattr(unischema, name).codec is None
if name == 'bool':
assert not getattr(unischema, name).nullable
else:
assert getattr(unischema, name).nullable
# Test schema preserve fields order
field_name_list = [f.name for f in fields]
assert list(unischema.fields.keys()) == field_name_list 示例3: to_arrow_type
# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import decimal128 [as 別名]
def to_arrow_type(dt):
""" Convert Spark data type to pyarrow type
"""
from distutils.version import LooseVersion
import pyarrow as pa
if type(dt) == BooleanType:
arrow_type = pa.bool_()
elif type(dt) == ByteType:
arrow_type = pa.int8()
elif type(dt) == ShortType:
arrow_type = pa.int16()
elif type(dt) == IntegerType:
arrow_type = pa.int32()
elif type(dt) == LongType:
arrow_type = pa.int64()
elif type(dt) == FloatType:
arrow_type = pa.float32()
elif type(dt) == DoubleType:
arrow_type = pa.float64()
elif type(dt) == DecimalType:
arrow_type = pa.decimal128(dt.precision, dt.scale)
elif type(dt) == StringType:
arrow_type = pa.string()
elif type(dt) == BinaryType:
# TODO: remove version check once minimum pyarrow version is 0.10.0
if LooseVersion(pa.__version__) < LooseVersion("0.10.0"):
raise TypeError("Unsupported type in conversion to Arrow: " + str(dt) +
"\nPlease install pyarrow >= 0.10.0 for BinaryType support.")
arrow_type = pa.binary()
elif type(dt) == DateType:
arrow_type = pa.date32()
elif type(dt) == TimestampType:
# Timestamps should be in UTC, JVM Arrow timestamps require a timezone to be read
arrow_type = pa.timestamp('us', tz='UTC')
elif type(dt) == ArrayType:
if type(dt.elementType) == TimestampType:
raise TypeError("Unsupported type in conversion to Arrow: " + str(dt))
arrow_type = pa.list_(to_arrow_type(dt.elementType))
else:
raise TypeError("Unsupported type in conversion to Arrow: " + str(dt))
return arrow_type 示例4: pyarrow_numeric
# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import decimal128 [as 別名]
def pyarrow_numeric():
return pyarrow.decimal128(38, 9)