ma.frombuffer(buffer, dtype=np.float64, count=-1, offset=0, *, like=None)
Interpret a buffer as a 1-dimensional array.
Parameters:bufferbuffer_likeAn object that exposes the buffer interface.
dtypedata-type, optionalData-type of the returned array. Default is
.
countint, optionalNumber of items to read. -1 means all data in the buffer.
offsetint, optionalStart reading the buffer from this offset (in bytes); default: 0.
likearray_like, optionalReference 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.
Added in version 1.20.0.
Returns:out: MaskedArraySee also
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(np.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
>>> importnumpyasnp>>> 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)