sum(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None)

This docstring was copied from numpy.sum.

Some inconsistencies with the Dask version may exist.

Notes

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

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

>>> np.sum([])  # doctest: +SKIP
0.0

For floating point numbers the numerical precision of sum (and np.add.reduce ) is in general limited by directly adding each number individually to the result causing rounding errors in every step. However, often numpy will use a numerically better approach (partial pairwise summation) leading to improved precision in many use-cases. This improved precision is always provided when no axis is given. When axis is given, it will depend on which axis is summed. Technically, to provide the best speed possible, the improved precision is 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's math.fsum function uses a slower but more precise approach to summation. Especially when summing a large number of lower precision floating point numbers, such as float32 , numerical errors can become significant. In such cases it can be advisable to use :None:None:`dtype="float64"` to use a higher precision for the output.

Parameters

a : array_like

Elements to sum.

axis : None 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. If axis is negative it counts from the last to the first axis.

versionadded

If axis is a tuple of ints, a sum is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before.

dtype : dtype, optional

The type of the returned array and of the accumulator in which the elements are summed. The dtype of a is used by default unless a has an integer dtype of less precision than the default platform integer. In that case, if a is signed then the platform integer is used while if a is unsigned then an unsigned integer of the same precision as the platform integer is used.

out : ndarray, optional

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

keepdims : bool, optional

If this is set to True, the axes which are reduced are left in 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, then keepdims will not be passed through to the sum method of sub-classes of :None:None:`ndarray`, however any non-default value will be. If the sub-class' method does not implement keepdims any exceptions will be raised.

initial : scalar, optional (Not supported in Dask)

Starting value for the sum. See :None:None:`~numpy.ufunc.reduce` for details.

versionadded

where : array_like of bool, optional (Not supported in Dask)

Elements to include in the sum. See :None:None:`~numpy.ufunc.reduce` for details.

versionadded

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 :None:None:`axis` is None, a scalar is returned. If an output array is specified, a reference to :None:None:`out` is returned.

Sum of array elements over a given axis.

See Also

add.reduce

Equivalent functionality of :None:None:`add`.

average

cumsum

Cumulative sum of array elements.

mean

ndarray.sum

Equivalent method.

trapz

Integration of array values using the composite trapezoidal rule.

Examples

This example is valid syntax, but we were not able to check execution

>>> np.sum([0.5, 1.5])  # doctest: +SKIP
2.0

This example is valid syntax, but we were not able to check execution

>>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)  # doctest: +SKIP
1

This example is valid syntax, but we were not able to check execution

>>> np.sum([[0, 1], [0, 5]])  # doctest: +SKIP
6

This example is valid syntax, but we were not able to check execution

>>> np.sum([[0, 1], [0, 5]], axis=0)  # doctest: +SKIP
array([0, 6])

This example is valid syntax, but we were not able to check execution

>>> np.sum([[0, 1], [0, 5]], axis=1)  # doctest: +SKIP
array([1, 5])

This example is valid syntax, but we were not able to check execution

>>> np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)  # doctest: +SKIP
array([1., 5.])

If the accumulator is too small, overflow occurs:

This example is valid syntax, but we were not able to check execution

>>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)  # doctest: +SKIP
-128

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

This example is valid syntax, but we were not able to check execution

>>> np.sum([10], initial=5)  # doctest: +SKIP
15

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.reductions.sum dask.array.reductions.nanmean dask.array.reductions.nansum dask.array.reductions.nanvar dask.array.reductions.cumsum

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