- numpy.cumsum(a, axis=None, dtype=None, out=None)[source]#
Return the cumulative sum of the elements along a given axis.
- axisint, optional
Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.
- dtypedtype, optional
Type of the returned array and of the accumulator in which the elements are summed. If
dtypeis 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.
- outndarray, 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. See Output type determination for more details.
A new array holding the result is returned unless out is specified, 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 not None or a is a 1-d array.
Arithmetic is modular when using integer types, and no error is raised on overflow.
cumsum(a)[-1]may not be equal to
sum(a)for floating-point values since
summay use a pairwise summation routine, reducing the roundoff-error. See
sumfor more information.
>>> 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 to
>>> b = np.array([1, 2e-9, 3e-9] * 1000000) >>> b.cumsum()[-1] 1000000.0050045159 >>> b.sum() 1000000.0050000029