frombuffer(buffer, dtype=float, count=-1, offset=0, *, like=None)

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) # doctest: +SKIP

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.

Parameters

buffer : buffer_like

An object that exposes the buffer interface.

dtype : data-type, optional

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

count : int, optional

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

offset : int, optional

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

like : array_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.

versionadded

Returns

out : ndarray

Interpret a buffer as a 1-dimensional array.

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)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.rec.fromstring numpy.fromstring

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