numpy.sum#

numpy.sum(a,axis=None,dtype=None,out=None,keepdims=<novalue>,initial=<novalue>,where=<novalue>)[source]#

Sum of array elements over a given axis.

Parameters:
aarray_like

Elements to sum.

axisNone or int or tuple of ints, optional

Axis or axes along which a sum is performed. The default,axis=None, will sum all of the elements of the input array. Ifaxis is negative it counts from the last to the first axis. Ifaxis is a tuple of ints, a sum is performed on all of the axesspecified in the tuple instead of a single axis or all the axes asbefore.

dtypedtype, optional

The type of the returned array and of the accumulator in which theelements are summed. The dtype ofa is used by default unlessahas an integer dtype of less precision than the default platforminteger. In that case, ifa is signed then the platform integeris used while ifa is unsigned then an unsigned integer of thesame precision as the platform integer is used.

outndarray, optional

Alternative output array in which to place the result. It must havethe same shape as the expected output, but the type of the outputvalues will be cast if necessary.

keepdimsbool, optional

If this is set to True, the axes which are reduced are leftin the result as dimensions with size one. With this option,the result will broadcast correctly against the input array.

If the default value is passed, thenkeepdims will not bepassed through to thesum method of sub-classes ofndarray, however any non-default value will be. If thesub-class’ method does not implementkeepdims anyexceptions will be raised.

initialscalar, optional

Starting value for the sum. Seereduce for details.

wherearray_like of bool, optional

Elements to include in the sum. Seereduce for details.

Returns:
sum_along_axisndarray

An array with the same shape asa, with the specifiedaxis removed. Ifa is a 0-d array, or ifaxis is None, a scalaris returned. If an output array is specified, a reference toout is returned.

See also

ndarray.sum

Equivalent method.

add

numpy.add.reduce equivalent function.

cumsum

Cumulative sum of array elements.

trapezoid

Integration of array values using composite trapezoidal rule.

mean,average

Notes

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

The sum of an empty array is the neutral element 0:

>>>np.sum([])0.0

For floating point numbers the numerical precision of sum (andnp.add.reduce) is in general limited by directly adding each numberindividually to the result causing rounding errors in every step.However, often numpy will use a numerically better approach (partialpairwise summation) leading to improved precision in many use-cases.This improved precision is always provided when noaxis is given.Whenaxis is given, it will depend on which axis is summed.Technically, to provide the best speed possible, the improved precisionis only used when the summation is along the fast axis in memory.Note that the exact precision may vary depending on other parameters.In contrast to NumPy, Python’smath.fsum function uses a slower butmore precise approach to summation.Especially when summing a large number of lower precision floating pointnumbers, such asfloat32, numerical errors can become significant.In such cases it can be advisable to usedtype=”float64” to use a higherprecision for the output.

Examples

>>>importnumpyasnp>>>np.sum([0.5,1.5])2.0>>>np.sum([0.5,0.7,0.2,1.5],dtype=np.int32)np.int32(1)>>>np.sum([[0,1],[0,5]])6>>>np.sum([[0,1],[0,5]],axis=0)array([0, 6])>>>np.sum([[0,1],[0,5]],axis=1)array([1, 5])>>>np.sum([[0,1],[np.nan,5]],where=[False,True],axis=1)array([1., 5.])

If the accumulator is too small, overflow occurs:

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

You can also start the sum with a value other than zero:

>>>np.sum([10],initial=5)15
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