Take the sum of a numpy array based on a logical array

Question

I have two numpy arrays prodsandindex

prods = np.asarray([ 0.5 ,  0.25,  1.98,  2.4 ,  2.1 ,  0.6 ])
index = np.asarray([False,  True,  True, False, False,  True], dtype=bool)

I need to calculate the sum of the values ​​in an array prodsusing an array index. The output I want

res = [0.75, 1.98, 5.1]

A first array Truein indexprecedes a False, so I take the first two elements of prods(. 5, .25) and sums them (0.75). The second one Truein the index does not have the previous one False(since it is preceded by a True, it Falseis not taken into account in the position of zero), so I simply output 1.98 in this case. The third is Truepreceded by two False, so I take these values ​​from the array prods(2.4.2.1.0.6) and summarize them. Any ideas on how to do this?

I basically need something like np.cumsum, but I need to return the cumulative amount every time it Trueoccurs in the index, and reset is the total value of the sum to zero.

Answer 1

You can use np.splitand using np.whereyour array indexas positions for splitting:

>>> [arr.sum() for arr in np.split(prods, np.where(index)[0]+1)[:-1]]
[0.75, 1.98, 5.0999999999999996]

The latter is not exactly 5.1because of the precision floating point. If you do not want to use Fractionor Decimal, you cannot do anything about it.

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You can also use np.add.reduceathere:

>>> np.add.reduceat(prods, np.append([0], (np.where(index)[0]+1)[:-1]))
array([ 0.75,  1.98,  5.1 ])