How Can I Efficiently Calculate Cumulative Sums in Python?

Efficient Calculation of Cumulative Sums in Lists

Accumulating the sum of consecutive elements in a list can be a common task in data analysis or numerical computations. For instance, given a list of time intervals time_interval = [4, 6, 12], we may want to find the cumulative sum of these intervals, resulting in t = [4, 10, 22].

Using a manual approach as demonstrated in the question, while effective, can be cumbersome for large lists. However, a more efficient solution is available using the numpy library:

import numpy as np

a = [4, 6, 12]

np.cumsum(a)
#array([ 4, 10, 22])

numpy's cumsum function provides a significant performance advantage over pure Python implementations, especially for large lists. Benchmarks have shown that numpy's cumsum outperforms manual accumulation by an order of magnitude:

timeit list(accumu(range(1000)))
10000 loops, best of 3: 161 us per loop

timeit list(accumu(xrange(1000)))
10000 loops, best of 3: 147 us per loop

timeit np.cumsum(np.arange(1000))
100000 loops, best of 3: 10.1 us per loop

Therefore, if you regularly work with numerical arrays, utilizing numpy's cumsum function is highly recommended for its efficiency and ease of use.

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