Most Pythonic way of assigning values of array to another array
I have two arrays with the same length (6, in this example). One stores floats
:
a = np.array([0.2, 0.01, 0.5, 0.7, 0., 0.002])
the second one stores indices (hence, int
values):
indices = np.array([4, 9, 0, 2, 2, 4])
In my code I initialize another array, which has length in general different from that of a
and indices
, for example 10 in this example:
c = np.zeros(10)
I would like to find a Pythonic way to accomplish the following:
for i in range(len(indices)):
c[indices[i]] += a[i]
which, in this example, produces:
[0.5 0. 0.7 0. 0.202 0. 0. 0. 0. 0.01 ]
I tried looking at this brilliant example, however I am not sure how this can be applied here.
You can use the .at
method of the np.add
ufunc:
np.add.at(c, indices, a)
Here's the help
page for the .at
method of ufuncs:
at(...) method of numpy.ufunc instance
at(a, indices, b=None, /)
Performs unbuffered in place operation on operand 'a' for elements
specified by 'indices'. For addition ufunc, this method is equivalent to
``a[indices] += b``, except that results are accumulated for elements that
are indexed more than once. For example, ``a[[0,0]] += 1`` will only
increment the first element once because of buffering, whereas
``add.at(a, [0,0], 1)`` will increment the first element twice.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
The array to perform in place operation on.
indices : array_like or tuple
Array like index object or slice object for indexing into first
operand. If first operand has multiple dimensions, indices can be a
tuple of array like index objects or slice objects.
b : array_like
Second operand for ufuncs requiring two operands. Operand must be
broadcastable over first operand after indexing or slicing.
For sum
, your operation is exactly what bincount
is:
np.bincount(indices, weights=a)
Output:
array([0.5 , 0. , 0.7 , 0. , 0.202, 0. , 0. , 0. , 0. , 0.01 ])