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# numpy.sum

`numpy.``sum`(a, axis=None, dtype=None, out=None)

  Return the sum of array elements over a given axis.

  Parameters: | a : array_like Elements to sum. axis : integer, optional Axis over which the sum is taken. By default axis is None, and all elements are summed. dtype : dtype, optional The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of a is used. An exception is when a has an integer type with less precision than the default platform integer. In that case, the default platform integer is used instead. out : ndarray, optional Array into which the output is placed. By default, a new array is created. If out is given, it must be of the appropriate shape (the shape of a with axis removed, i.e., `numpy.delete(a.shape, axis)`). Its type is preserved.
  Returns: | sum_along_axis : ndarray An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.

  See also

  `ndarray.sum`
    equivalent method

  Notes

  Arithmetic is modular when using integer types, and no error is raised on overflow.

  Examples

      >>> np.sum([0.5, 1.5])
      2.0
      >>> np.sum([0.5, 1.5], dtype=np.int32)
      1
      >>> np.sum([[0, 1], [0, 5]])
      6
      >>> np.sum([[0, 1], [0, 5]], axis=1)
      array([1, 5])

  If the accumulator is too small, overflow occurs:

      >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
      -128

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