numpy.frombuffer#
- numpy.frombuffer(buffer, dtype=float, count=-1, offset=0, *, like=None)#
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, optional
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.New in version 1.20.0.
- Returns:
- outndarray
See also
ndarray.tobytes
Inverse of this operation, construct Python bytes from the raw data bytes in the array.
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.
This function creates a view into the original object. This should be safe in general, but it may make sense to copy the result when the original object is mutable or untrusted.
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)