def deserialize(cls, header, frames):
dtype = header["dtype"]
data = Buffer.deserialize(header["data"], [frames[0]])
mask = None
if "mask" in header:
mask = Buffer.deserialize(header["mask"], [frames[1]])
return build_column(data=data, dtype=dtype, mask=mask)
def deserialize(cls, header: dict, frames: list) -> CategoricalColumn:
n_dtype_frames = header["dtype_frames_count"]
dtype = CategoricalDtype.deserialize(header["dtype"],
frames[:n_dtype_frames])
n_data_frames = header["data_frames_count"]
column_type = pickle.loads(header["data"]["type-serialized"])
data = column_type.deserialize(
header["data"],
frames[n_dtype_frames:n_dtype_frames + n_data_frames],
)
mask = None
if "mask" in header:
mask = Buffer.deserialize(header["mask"],
[frames[n_dtype_frames + n_data_frames]])
return cast(
CategoricalColumn,
column.build_column(
data=None,
dtype=dtype,
mask=mask,
children=(column.as_column(data.base_data,
dtype=data.dtype), ),
),
)
def deserialize(cls, header, frames):
n_dtype_frames = header["dtype_frames_count"]
dtype = CategoricalDtype.deserialize(header["dtype"],
frames[:n_dtype_frames])
n_data_frames = header["data_frames_count"]
column_type = pickle.loads(header["data"]["type"])
data = column_type.deserialize(
header["data"],
frames[n_dtype_frames:n_dtype_frames + n_data_frames],
)
mask = None
if "mask" in header:
mask = Buffer.deserialize(header["mask"],
[frames[n_dtype_frames + n_data_frames]])
return column.build_column(data=None,
dtype=dtype,
mask=mask,
children=(data, ))
