ak.argcartesian¶
Defined in awkward.operations.structure on line 3533.
- ak.argcartesian(arrays, axis=1, nested=None, parameters=None, with_name=None, highlevel=True, behavior=None)¶
- Parameters
arrays (dict or iterable of arrays) – Arrays on which to compute the Cartesian product.
axis (int) – The dimension at which this operation is applied. The outermost dimension is
0
, followed by1
, etc., and negative values count backward from the innermost:-1
is the innermost dimension,-2
is the next level up, etc.nested (None, True, False, or iterable of str or int) – If None or False, all combinations of elements from the
arrays
are produced at the same level of nesting; if True, they are grouped in nested lists by combinations that share a common item from each of thearrays
; if an iterable of str or int, group common items for a chosen set of keys from thearray
dict or slots of thearray
iterable.parameters (None or dict) – Parameters for the new
ak.layout.RecordArray
node that is created by this operation.with_name (None or str) – Assigns a
"__record__"
name to the newak.layout.RecordArray
node that is created by this operation (overridingparameters
, if necessary).highlevel (bool) – If True, return an
ak.Array
; otherwise, return a low-levelak.layout.Content
subclass.behavior (None or dict) – Custom
ak.behavior
for the output array, if high-level.
Computes a Cartesian product (i.e. cross product) of data from a set of
arrays
, like ak.cartesian
, but returning integer indexes for
ak.Array.__getitem__
.
For example, the Cartesian product of
>>> one = ak.Array([1.1, 2.2, 3.3])
>>> two = ak.Array(["a", "b"])
is
>>> ak.to_list(ak.cartesian([one, two], axis=0))
[(1.1, 'a'), (1.1, 'b'), (2.2, 'a'), (2.2, 'b'), (3.3, 'a'), (3.3, 'b')]
But with argcartesian, only the indexes are returned.
>>> ak.to_list(ak.argcartesian([one, two], axis=0))
[(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1)]
These are the indexes that can select the items that go into the actual Cartesian product.
>>> one_index, two_index = ak.unzip(ak.argcartesian([one, two], axis=0))
>>> one[one_index]
<Array [1.1, 1.1, 2.2, 2.2, 3.3, 3.3] type='6 * float64'>
>>> two[two_index]
<Array ['a', 'b', 'a', 'b', 'a', 'b'] type='6 * string'>
All of the parameters for ak.cartesian
apply equally to ak.argcartesian
,
so see the ak.cartesian
documentation for a more complete description.