dpnp.cumsum
- dpnp.cumsum(a, axis=None, dtype=None, out=None)[source]
Return the cumulative sum of the elements along a given axis.
For full documentation refer to
numpy.cumsum
.- Parameters:
a ({dpnp.ndarray, usm_ndarray}) -- Input array.
axis (int, optional) -- Axis along which the cumulative sum is computed. The default (
None
) is to compute the cumulative sum over the flattened array.dtype ({None, dtype}, optional) -- Type of the returned array and of the accumulator in which the elements are summed. If dtype is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used.
out ({dpnp.ndarray, usm_ndarray}, optional) -- Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary.
- Returns:
out -- A new array holding the result is returned unless out is specified as
dpnp.ndarray
, in which case a reference to out is returned. The result has the same size as a, and the same shape as a if axis is notNone
or a is a 1-d array.- Return type:
dpnp.ndarray
See also
Examples
>>> import dpnp as np >>> a = np.array([[1, 2, 3], [4, 5, 6]]) >>> a array([[1, 2, 3], [4, 5, 6]]) >>> np.cumsum(a) array([ 1, 3, 6, 10, 15, 21]) >>> np.cumsum(a, dtype=float) # specifies type of output value(s) array([ 1., 3., 6., 10., 15., 21.])
>>> np.cumsum(a, axis=0) # sum over rows for each of the 3 columns array([[1, 2, 3], [5, 7, 9]]) >>> np.cumsum(a, axis=1) # sum over columns for each of the 2 rows array([[ 1, 3, 6], [ 4, 9, 15]])
cumsum(b)[-1]
may not be equal tosum(b)
>>> b = np.array([1, 2e-9, 3e-9] * 10000) >>> b.cumsum().dtype == b.sum().dtype == np.float64 True >>> b.cumsum()[-1] == b.sum() array(False)