numpy.ma.frombuffer

ma.frombuffer(buffer, dtype=float, count=-1, offset=0, *, like=None) = <numpy.ma.core._convert2ma object>

Interpret a buffer as a 1-dimensional array.

Parameters

bufferbuffer_like

An object that exposes the buffer interface.

dtypedata-type, optional

Data-type of the returned array; default: float.

countint, optional

Number of items to read. -1 means all data in the buffer.

offsetint, optional

Start reading the buffer from this offset (in bytes); default: 0.

likearray_like

Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

Note

The like keyword is an experimental feature pending on acceptance of NEP 35.

New in version 1.20.0.

Notes

If the buffer has data that is not in machine byte-order, this should be specified as part of the data-type, e.g.:

>>> dt = np.dtype(int)
>>> dt = dt.newbyteorder('>')
>>> np.frombuffer(buf, dtype=dt) 

The data of the resulting array will not be byteswapped, but will be interpreted correctly.

Examples

>>> s = b'hello world'
>>> np.frombuffer(s, dtype='S1', count=5, offset=6)
array([b'w', b'o', b'r', b'l', b'd'], dtype='|S1')

>>> np.frombuffer(b'\x01\x02', dtype=np.uint8)
array([1, 2], dtype=uint8)
>>> np.frombuffer(b'\x01\x02\x03\x04\x05', dtype=np.uint8, count=3)
array([1, 2, 3], dtype=uint8)

numpy.ma.copy numpy.ma.fromfunction