使用 Pandas 和 Numpy 優化 Vanilla Python

Question

Is there anyway that I could make the function below faster and more optimized with pandas or numpy , the function below adds the sum of seq45 until the elements of it is equivalent or over 10000.The elements that is being added up to seq45 are 3,7,11的順序。 我想提高速度的原因是必須可能用更大的數值（如 1000000）測試 10000 並更快地處理。 此代碼的答案來自此問題： 問題

代碼：

Sequence = np.array([3, 7, 11])
seq45= []
for n in itertools.cycle(Sequence):
    seq.append(n)
    if sum(seq45) >= 10000: break
        
print(seq45)

當前性能/處理時間：71.9ms

Answer 1

你可以試試：

Sequence = np.array([3, 7, 11])
s = Sequence.sum()
tot = 10000

seq = list(Sequence)*(tot//s)
mod = tot%s
for n in seq:
    if mod > 0:
        seq.append(n)
        mod -= n
    else:
        break

掛壁時間：57 µs

Answer 2

import numpy as np

tot = 10_000
sequence = np.array([3, 7, 11])
n = tot // sequence.sum() + 1
seq45 = np.tile(sequence, n)

請注意，這並不完全等效，因為它重復了完整的序列，而不是單個元素，因此seq45可能會稍大一些。