# 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
    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.

      New in version 1.20.0.

  Returns
    out: MaskedArray

  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)

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