I'm trying to apply an addition operator to an array where I want repeated indices to indicate repeated addition operations. From a Python Data Science Book (https://jakevdp.github.io/PythonDataScienceHandbook/02.07-fancy-indexing.html), it seems that this is possible using np.add.at(original matrix, indices, thing to add), but I can't figure out how to specify the indices to operate on columns, not rows.
e.g. Dummy Example
# Create Array
A = np.arange(12)
A = A.reshape(4,3)
print(A)
gives
[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]
[ 9 10 11]]
and
# Create columns to add to A (in reality, all values won't be the same)
B = np.ones_like(A[:, [0,0]])
print(adder)
gives
[[1 1]
[1 1]
[1 1]
[1 1]]
I want to perform the operation A[:, [0, 0]] += B but using the system where repeated indices indicate repeated operations (so in this case, both columns of B gets added to column 0). The result should thus be:
[[ 2 1 2]
[ 5 4 5]
[ 7 7 8]
[ 11 10 11]]
This can be done using np.add.at(A, I, B) I believe, but how do I specify the indices I to correspond to [:, [0,0]] as this gives a syntax error (it seems that the indices matrix can't contain the : character?).
Thanks
In [12]: A = np.arange(12).reshape(4,3)
In [13]: np.add.at(A, (slice(None), [0,0]), 1)
In [14]: A
Out[14]:
array([[ 2, 1, 2],
[ 5, 4, 5],
[ 8, 7, 8],
[11, 10, 11]])
This could also be written with s_ as
np.add.at(A, np.s_[:, [0,0]], 1)
s_ is a class object that lets us using indexing notation to create the necessary tuple. In an indexing context Python interpreter converts the : into a slice object.
In [19]: np.s_[:, [0,0]]
Out[19]: (slice(None, None, None), [0, 0])