本文整理汇总了Python中pyarrow.date64方法的典型用法代码示例。如果您正苦于以下问题：Python pyarrow.date64方法的具体用法？Python pyarrow.date64怎么用？Python pyarrow.date64使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyarrow的用法示例。
在下文中一共展示了pyarrow.date64方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_arrow_schema_convertion
# 需要导入模块: import pyarrow [as 别名]
# 或者: from pyarrow import date64 [as 别名]
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2: test_combine_add
# 需要导入模块: import pyarrow [as 别名]
# 或者: from pyarrow import date64 [as 别名]
def test_combine_add(self, data_repeated, dtype):
if dtype.name in [
"fletcher_chunked[date64[ms]]",
"fletcher_continuous[date64[ms]]",
]:
pytest.skip(
"unsupported operand type(s) for +: 'datetime.date' and 'datetime.date"
)
else:
BaseMethodsTests.test_combine_add(self, data_repeated) 示例3: _get_numba_typ_from_pa_typ
# 需要导入模块: import pyarrow [as 别名]
# 或者: from pyarrow import date64 [as 别名]
def _get_numba_typ_from_pa_typ(pa_typ):
import pyarrow as pa
_typ_map = {
# boolean
pa.bool_(): types.bool_,
# signed int types
pa.int8(): types.int8,
pa.int16(): types.int16,
pa.int32(): types.int32,
pa.int64(): types.int64,
# unsigned int types
pa.uint8(): types.uint8,
pa.uint16(): types.uint16,
pa.uint32(): types.uint32,
pa.uint64(): types.uint64,
# float types (TODO: float16?)
pa.float32(): types.float32,
pa.float64(): types.float64,
# String
pa.string(): string_type,
# date
pa.date32(): types.NPDatetime('ns'),
pa.date64(): types.NPDatetime('ns'),
# time (TODO: time32, time64, ...)
pa.timestamp('ns'): types.NPDatetime('ns'),
pa.timestamp('us'): types.NPDatetime('ns'),
pa.timestamp('ms'): types.NPDatetime('ns'),
pa.timestamp('s'): types.NPDatetime('ns'),
}
if pa_typ not in _typ_map:
raise ValueError("Arrow data type {} not supported yet".format(pa_typ))
return _typ_map[pa_typ]