本文整理匯總了Python中pyarrow.uint16方法的典型用法代碼示例。如果您正苦於以下問題：Python pyarrow.uint16方法的具體用法？Python pyarrow.uint16怎麽用？Python pyarrow.uint16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.uint16方法的3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: _get_numeric_byte_size_test_cases
# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import uint16 [as 別名]
def _get_numeric_byte_size_test_cases():
result = []
for array_type, sizeof in [
(pa.int8(), 1),
(pa.uint8(), 1),
(pa.int16(), 2),
(pa.uint16(), 2),
(pa.int32(), 4),
(pa.uint32(), 4),
(pa.int64(), 8),
(pa.uint64(), 8),
(pa.float32(), 4),
(pa.float64(), 8),
]:
result.append(
dict(
testcase_name=str(array_type),
array=pa.array(range(9), type=array_type),
slice_offset=2,
slice_length=3,
expected_size=(_all_false_null_bitmap_size(2) + sizeof * 9),
expected_sliced_size=(_all_false_null_bitmap_size(1) + sizeof * 3)))
return result 示例2: _get_numba_typ_from_pa_typ
# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import uint16 [as 別名]
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] 示例3: _dtype_to_arrow_type
# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import uint16 [as 別名]
def _dtype_to_arrow_type(dtype: np.dtype) -> pyarrow.DataType:
if dtype == np.int8:
return pyarrow.int8()
elif dtype == np.int16:
return pyarrow.int16()
elif dtype == np.int32:
return pyarrow.int32()
elif dtype == np.int64:
return pyarrow.int64()
elif dtype == np.uint8:
return pyarrow.uint8()
elif dtype == np.uint16:
return pyarrow.uint16()
elif dtype == np.uint32:
return pyarrow.uint32()
elif dtype == np.uint64:
return pyarrow.uint64()
elif dtype == np.float16:
return pyarrow.float16()
elif dtype == np.float32:
return pyarrow.float32()
elif dtype == np.float64:
return pyarrow.float64()
elif dtype.kind == "M":
# [2019-09-17] Pandas only allows "ns" unit -- as in, datetime64[ns]
# https://github.com/pandas-dev/pandas/issues/7307#issuecomment-224180563
assert dtype.str.endswith("[ns]")
return pyarrow.timestamp(unit="ns", tz=None)
elif dtype == np.object_:
return pyarrow.string()
else:
raise RuntimeError("Unhandled dtype %r" % dtype)