Nhiều giải pháp đã được đề xuất.
Nếu tốc độ được quan tâm, thì đây là đánh giá các giải pháp khác nhau liên quan đến tốc độ [từ nhanh nhất đến chậm nhất]
import timeit
import operator
a = [2,2,2]
b = [1,1,1] # we want to obtain c = [2,2,2] - [1,1,1] = [1,1,1
%timeit map[operator.sub, a, b]
176 ns ± 7.18 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
%timeit map[int.__sub__, a, b]
179 ns ± 4.95 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
%timeit map[lambda x,y: x-y, a,b]
189 ns ± 8.1 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
%timeit [a_i - b_i for a_i, b_i in zip[a, b]]
421 ns ± 18.4 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
%timeit [x - b[i] for i, x in enumerate[a]]
452 ns ± 17.2 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each
%timeit [a[i] - b[i] for i in range[len[a]]]
530 ns ± 16.7 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
%timeit list[map[lambda x, y: x - y, a, b]]
546 ns ± 16.1 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
%timeit np.subtract[a,b]
2.68 µs ± 80.9 ns per loop [mean ± std. dev. of 7 runs, 100000 loops each]
%timeit list[np.array[a] - np.array[b]]
2.82 µs ± 113 ns per loop [mean ± std. dev. of 7 runs, 100000 loops each]
%timeit np.matrix[a] - np.matrix[b]
12.3 µs ± 437 ns per loop [mean ± std. dev. of 7 runs, 100000 loops each]
Sử dụng map
rõ ràng là nhanh nhất. Đáng ngạc nhiên, numpy
là chậm nhất. Nó chỉ ra rằng chi phí đầu tiên chuyển đổi danh sách a
và
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
0 thành mảng numpy
là một nút cổ chai lớn hơn bất kỳ hiệu quả nào đạt được từ vector hóa.%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
Trừ hai danh sách bằng hàm zip [] trong phương thức này, chúng tôi sẽ chuyển hai danh sách đầu vào cho hàm zip. Sau đó, lặp lại trên đối tượng zip bằng cách sử dụng cho vòng lặp. Trên mỗi lần lặp, chương trình sẽ lấy một yếu tố từ List1 và List2, trừ chúng và nối kết quả vào danh sách khác.
Examples:
Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]
Chúng ta có thể trừ 2 danh sách trong Python không? The naive approach is to traverse both list simultaneously and if the element in first list in greater than element in second list, then subtract it, else if the element in first list in smaller than element in second list, then return element of first list
only.
Python3
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
2%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 %timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
4%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
5%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
7%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
9%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]1__16
Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]7
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 %timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
4Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]5
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]3__
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]2
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]5
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]6
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]7
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]8
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]0
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]1
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]2
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]3
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]5
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]6
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]7
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]8
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]2
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]0
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]1
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]5
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]3
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]6
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]9
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
map
1Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
map
4Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
map
7Output:
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]
Phương pháp 2: Sử dụng zip [] Chúng tôi trừ nếu phần tử trong danh sách đầu tiên lớn hơn phần tử trong danh sách thứ hai, nếu không chúng tôi xuất phần tử của danh sách đầu tiên. & NBSP;Using zip[] we subtract if element in first list is greater than element in second list, else we output element of first list.
Python3
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
2%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 %timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
4%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
5%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
7%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
9%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]1__16
Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]7
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 %timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
4Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]5
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]3__
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]2
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3__Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]6
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]9
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
map
1Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
map
4Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
map
7Output:
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]
Phương pháp 3: Sử dụng danh sách hiểu. & NBSP; Using list comprehension.
Python3
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
2%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 %timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
4%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
5%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
7%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
9%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]1__16
Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]7
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 %timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
4Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]5
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]3__
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]2
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3__Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
map
4Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]6
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]9
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
map
1Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
map
4Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
map
7Output:
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]
Phương pháp 3: Sử dụng danh sách hiểu. & NBSP; Using numpy[] to complete the above task.
Python3
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]2
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 %timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
68Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]7
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
70Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]3
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
72%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
73Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]0
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
75Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]5
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]6
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]7
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]8__
Phương pháp 4: Sử dụng numpy [] để hoàn thành nhiệm vụ trên. & Nbsp;
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
97 %timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
98Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]6
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]9
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
map
1Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]4
Original list are : [10, 20, 30, 40, 50, 60] [60, 50, 40, 30, 20, 10] Output list is [10, 20, 30, 10, 30, 50]9
map
4Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]7
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
2%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
3 Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]01
%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
5%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
7%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
9%timeit a = np.array[[2,2,2]]; b=np.array[[1,1,1]]
1.55 µs ± 54.9 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
a = np.array[[2,2,2]]
b = np.array[[1,1,1]]
%timeit a - b
417 ns ± 12.8 ns per loop [mean ± std. dev. of 7 runs, 1000000 loops each]
6Input: l1 = [10, 20, 30, 40, 50, 60] l2 = [60, 50, 40, 30, 20, 10] Output: [10, 20, 30, 10, 30, 50] Input: l1 = [15, 9, 10, 56, 23, 78, 5, 4, 9] l2 = [9, 4, 5, 36, 47, 26, 10, 45, 87] Output: [6, 5, 5, 20, 23, 52, 5, 4, 9]1__16
Output:
Original list are : [10 20 30 40 50 60] [60 50 40 30 20 10] Output list is [10 20 30 10 30 50]