# numpy.empty

numpy.empty(shape, dtype=float, order='C', *, like=None)

  Return a new array of given shape and type, without initializing entries.

  Parameters:
    shape int or tuple of int

      Shape of the empty array, e.g., `(2, 3)` or `2`.

    dtype data-type, optional

      Desired output data-type for the array, e.g, `numpy.int8`. Default is `numpy.float64`.

    order{‘C’, ‘F’}, optional, default: ‘C’

      Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.

    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 ndarray

      Array of uninitialized (arbitrary) data of the given shape, dtype, and order. Object arrays will be initialized to None.

  See also

  `empty_like`

    Return an empty array with shape and type of input.

  `ones`

    Return a new array setting values to one.

  `zeros`

    Return a new array setting values to zero.

  `full`

    Return a new array of given shape filled with value.

  Notes

  `empty`, unlike `zeros`, does not set the array values to zero, and may therefore be marginally faster. On the other hand, it requires the user to manually set all the values in the array, and should be used with caution.

  Examples

      >>> np.empty([2, 2])
      array([[ -9.74499359e+001,   6.69583040e-309],
             [  2.13182611e-314,   3.06959433e-309]])         #uninitialized

      >>> np.empty([2, 2], dtype=int)
      array([[-1073741821, -1067949133],
             [  496041986,    19249760]])                     #uninitialized