ak.sum#

Defined in awkward.operations.ak_sum on line 24.

ak.sum(array, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None)#

Parameters:

array – Array-like data (anything ak.to_layout recognizes).
axis (None or int) – If None, combine all values from the array into a single scalar result; if an int, group by that axis: 0 is the outermost, 1 is the first level of nested lists, etc., and negative axis counts from the innermost: -1 is the innermost, -2 is the next level up, etc.
keepdims (bool) – If False, this reducer decreases the number of dimensions by 1; if True, the reduced values are wrapped in a new length-1 dimension so that the result of this operation may be broadcasted with the original array.
mask_identity (bool) – If True, reducing over empty lists results in None (an option type); otherwise, reducing over empty lists results in the operation’s identity.
highlevel (bool) – If True, return an ak.Array; otherwise, return a low-level ak.contents.Content subclass.
behavior (None or dict) – Custom ak.behavior for the output array, if high-level.
attrs (None or dict) – Custom attributes for the output array, if high-level.

Sums over array (many types supported, including all Awkward Arrays and Records). The identity of addition is 0 and it is usually not masked. This operation is the same as NumPy’s sum if all lists at a given dimension have the same length and no None values, but it generalizes to cases where they do not.

For example, consider this array, in which all lists at a given dimension have the same length.

>>> array = ak.Array([[ 0.1,  0.2,  0.3],
...                   [10.1, 10.2, 10.3],
...                   [20.1, 20.2, 20.3],
...                   [30.1, 30.2, 30.3]])

A sum over axis=-1 combines the inner lists, leaving one value per outer list:

>>> ak.sum(array, axis=-1)
<Array [0.6, 30.6, 60.6, 90.6] type='4 * float64'>

while a sum over axis=0 combines the outer lists, leaving one value per inner list:

>>> ak.sum(array, axis=0)
<Array [60.4, 60.8, 61.2] type='3 * float64'>

Now with some values missing,

>>> array = ak.Array([[ 0.1,  0.2      ],
...                   [10.1            ],
...                   [20.1, 20.2, 20.3],
...                   [30.1, 30.2      ]])

The sum over axis=-1 results in

>>> ak.sum(array, axis=-1)
<Array [0.3, 10.1, 60.6, 60.3] type='4 * float64'>

and the sum over axis=0 results in

>>> ak.sum(array, axis=0)
<Array [60.4, 50.6, 20.3] type='3 * float64'>

How we ought to sum over the innermost lists is unambiguous, but for all other axis values, we must choose whether to align contents to the left before summing, to the right before summing, or something else. As suggested by the way the text has been aligned, we choose the left-alignment convention: the first axis=0 result is the sum of all first elements

60.4 = 0.1 + 10.1 + 20.1 + 30.1

the second is the sum of all second elements

50.6 = 0.2 + 20.2 + 30.2

and the third is the sum of the only third element

20.3 = 20.3

The same is true if the values were None, rather than gaps:

>>> array = ak.Array([[ 0.1,  0.2, None],
...                   [10.1, None, None],
...                   [20.1, 20.2, 20.3],
...                   [30.1, 30.2, None]])

>>> ak.sum(array, axis=-1)
<Array [0.3, 10.1, 60.6, 60.3] type='4 * float64'>
>>> ak.sum(array, axis=0)
<Array [60.4, 50.6, 20.3] type='3 * float64'>

However, the missing value placeholder, None, allows us to align the remaining data differently:

>>> array = ak.Array([[None,  0.1,  0.2],
...                   [None, None, 10.1],
...                   [20.1, 20.2, 20.3],
...                   [None, 30.1, 30.2]])

Now the axis=-1 result is the same but the axis=0 result has changed:

>>> ak.sum(array, axis=-1)
<Array [0.3, 10.1, 60.6, 60.3] type='4 * float64'>
>>> ak.sum(array, axis=0)
<Array [20.1, 50.4, 60.8] type='3 * float64'>

because

1 = 20.1
4 = 0.1 + 20.2 + 30.1
8 = 0.2 + 10.1 + 20.3 + 30.2

If, instead of missing numbers, we had missing lists,

>>> array = ak.Array([[ 0.1,  0.2,  0.3],
...                   None,
...                   [20.1, 20.2, 20.3],
...                   [30.1, 30.2, 30.3]])

then the placeholder would pass through the axis=-1 sum because summing over the inner dimension shouldn’t change the length of the outer dimension.

>>> ak.sum(array, axis=-1)
<Array [0.6, None, 60.6, 90.6] type='4 * ?float64'>

However, the axis=0 sum loses information about the None value.

>>> ak.sum(array, axis=0)
<Array [50.3, 50.6, 50.9] type='3 * float64'>

which is

3 = 0.1 + (None) + 20.1 + 30.1
6 = 0.2 + (None) + 20.2 + 30.2
9 = 0.3 + (None) + 20.3 + 30.3

An axis=0 sum would be reducing that information if it had not been None, anyway. If the None values were replaced with 0, the result for axis=0 would be the same. The result for axis=-1 would not be the same because this None is in the 0 axis, not the axis that axis=-1 sums over.

The keepdims parameter ensures that the number of dimensions does not change: scalar results are put into new length-1 dimensions:

>>> ak.sum(array, axis=-1, keepdims=True)
<Array [[0.6], None, [60.6], [90.6]] type='4 * option[1 * float64]'>
>>> ak.sum(array, axis=0, keepdims=True)
<Array [[50.3, 50.6, 50.9]] type='1 * var * float64'>

and axis=None ignores all None values and adds up everything in the array (keepdims has no effect).

>>> ak.sum(array, axis=None)
151.8

The mask_identity, which has no equivalent in NumPy, inserts None in the output wherever a reduction takes place over zero elements. This is different from reductions that are otherwise equal to the identity or are equal to the identity by cancellation.

>>> array = ak.Array([[2.2, 2.2], [4.4, -2.2, -2.2], [], [0.0]])
>>> ak.sum(array, axis=-1)
<Array [4.4, 0, 0, 0] type='4 * float64'>
>>> ak.sum(array, axis=-1, mask_identity=True)
<Array [4.4, 0, None, 0] type='4 * ?float64'>

The third list is reduced to 0 if mask_identity=False because 0 is the identity of addition, but it is reduced to None if mask_identity=True.