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# numpy.ma.frombuffer

ma.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 is `numpy.float64`.

    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.

      New in version 1.20.0.

  Returns:
    out: MaskedArray

  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

      >>> 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)