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Module « numpy.matlib »

Fonction frombuffer - module numpy.matlib

Signature de la fonction frombuffer

Description

help(numpy.matlib.frombuffer)

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

    Interpret a buffer as a 1-dimensional array.

    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, 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.

        .. versionadded:: 1.20.0

    Returns
    -------
    out : ndarray

    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) # 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.

    Examples
    --------
    >>> import numpy as np
    >>> 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)

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