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,
%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 là chậm nhất. Nó chỉ ra rằng chi phí đầu tiên chuyển đổi danh sách %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]
1 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]
2 thành mảng %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 là một nút cổ chai vượt xa mọi hiệu quả 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]
[10, 15, 20, 30]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]
5 Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list237
Examples:
Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list2
Phương pháp 6: Sử dụng symmetric_difference để tìm sự khác biệt giữa hai danh sách trong PythonWhen you have multiple same elements then this would not work. In that case, this code will simply remove the same elements.
In that case, you can maintain a count of each element in both lists.
Các phần tử trong tập đầu tiên hoặc bộ thứ hai được trả về bằng kỹ thuật symmetric_difference []. Giao lộ, không giống như các mục được chia sẻ của hai bộ, không được trả lại bởi kỹ thuật này.Use “in” to Find the Difference Between Two Lists in Python
Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list268
%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 [20, 10, 30, 15]5
Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list271
[20, 10, 30, 15]55____273
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]
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]
8%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list21
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23__
[10, 15, 20, 30]1
%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]
6Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list29
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list27
[10, 15, 20, 30]0
[10, 15, 20, 30]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]
5 [10, 15, 20, 30]2
[10, 15, 20, 30]3
[10, 15, 20, 30]4
[10, 15, 20, 30]5
[10, 15, 20, 30]6
[10, 15, 20, 30]7
[10, 15, 20, 30]8
[10, 15, 20, 30]4
[10, 15, 20, 30]0
[10, 15, 20, 30]5
[10, 15, 20, 30]2
[10, 15, 20, 30]3
[10, 15, 20, 30]4
[10, 15, 20, 30]5
[10, 15, 20, 30]6
Output:
[10, 15, 20, 30]
Phương pháp 2: sử dụng SET [] để tìm sự khác biệt giữa hai danh sách trong Python
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]
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]
8%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list21
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23__
[10, 15, 20, 30]1
%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]
6Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list29
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list27
[10, 15, 20, 30]0
[20, 10, 30, 15]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]
5 [20, 10, 30, 15]55____86
[10, 15, 20, 30]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]
5 [20, 10, 30, 15]9
[10, 15, 20, 30]3
map
1[10, 15, 20, 30]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]
4[10, 15, 20, 30]8
[10, 15, 20, 30]5
[10, 15, 20, 30]6
Đầu ra: & nbsp; & nbsp;
[10, 15, 20, 30]
Phương pháp 3: & NBSP; Sử dụng danh sách hiểu và đặt để tìm sự khác biệt giữa hai danh sách trong Python Use a list comprehension and set to Find the Difference Between Two Lists in Python
Trong phương pháp này, chúng tôi chuyển đổi danh sách thành các bộ một cách rõ ràng và sau đó chỉ cần giảm cái này từ mẫu kia bằng toán tử trừ. Để biết thêm các tài liệu tham khảo về các bộ truy cập đã thiết lập trong Python. Đó là một kỹ thuật tương tự mà chúng tôi đã sử dụng trước đây. Sự khác biệt duy nhất là, chúng tôi đã thay thế các vòng lặp lồng nhau bằng cú pháp hiểu danh sách.
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]
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]
8%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list21
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23__
[10, 15, 20, 30]1
%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]
6Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list29
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list27
[10, 15, 20, 30]0
[20, 10, 30, 15]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]
5 [20, 10, 30, 15]55____86
[10, 15, 20, 30]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]
5 [20, 10, 30, 15]9
[10, 15, 20, 30]3
map
1[10, 15, 20, 30]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]
4[10, 15, 20, 30]8
[10, 15, 20, 30]5
[10, 15, 20, 30]6
Output:
[10, 15, 20, 30]
Đầu ra: & nbsp; & nbsp; Without using the set[]
Phương pháp 3: & NBSP; Sử dụng danh sách hiểu và đặt để tìm sự khác biệt giữa hai danh sách trong Python
Python3
Trong phương pháp này, chúng tôi chuyển đổi danh sách thành các bộ một cách rõ ràng và sau đó chỉ cần giảm cái này từ mẫu kia bằng toán tử trừ. Để biết thêm các tài liệu tham khảo về các bộ truy cập đã thiết lập trong Python. Đó là một kỹ thuật tương tự mà chúng tôi đã sử dụng trước đây. Sự khác biệt duy nhất là, chúng tôi đã thay thế các vòng lặp lồng nhau bằng cú pháp hiểu danh sách.
Phương thức & nbsp; 4: Không sử dụng set []
Trong phương pháp này, chúng tôi sử dụng kỹ thuật kết hợp cơ bản để sao chép các yếu tố từ cả hai danh sách bằng kiểm tra thường xuyên nếu có mặt khác hay không. & 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]
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]
8%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list21
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23__
[10, 15, 20, 30]1
%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]
6Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list29
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list27
[10, 15, 20, 30]0
[20, 10, 30, 15]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]
5 [20, 10, 30, 15]55____86
[10, 15, 20, 30]5
Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list200
[10, 15, 20, 30]
0
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]
[20, 10, 30, 15]
9[10, 15, 20, 30]
3 map
1[10, 15, 20, 30]
5
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]
[10, 15, 20, 30]
8
[10, 15, 20, 30]
Đầu ra: & nbsp; & nbsp; Use Numpy to Find the Difference Between Two Lists in Python
Phương pháp 3: & NBSP; Sử dụng danh sách hiểu và đặt để tìm sự khác biệt giữa hai danh sách trong Pythonnumpy.concatenate[] function concatenate a sequence of arrays along an existing axis.
Python3
Trong phương pháp này, chúng tôi chuyển đổi danh sách thành các bộ một cách rõ ràng và sau đó chỉ cần giảm cái này từ mẫu kia bằng toán tử trừ. Để biết thêm các tài liệu tham khảo về các bộ truy cập đã thiết lập trong Python. Đó là một kỹ thuật tương tự mà chúng tôi đã sử dụng trước đây. Sự khác biệt duy nhất là, chúng tôi đã thay thế các vòng lặp lồng nhau bằng cú pháp hiểu danh sách.
Phương thức & nbsp; 4: Không sử dụng set []
Trong phương pháp này, chúng tôi sử dụng kỹ thuật kết hợp cơ bản để sao chép các yếu tố từ cả hai danh sách bằng kiểm tra thường xuyên nếu có mặt khác hay không. & 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]
45 %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]
46[10, 15, 20, 30]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]
48%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]
50[10, 15, 20, 30]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]
52[10, 15, 20, 30]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]
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]
55[10, 15, 20, 30]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]
68 %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]
69[10, 15, 20, 30]5
Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list239
Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list240
Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list241
Output:
[10, 15, 20, 30]
%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]
96%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]
98 Use symmetric_difference to Find the Difference Between Two Lists in Python
%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]
%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]
%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]
Đầu ra: & nbsp;
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]
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]
8%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list21
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23__
[10, 15, 20, 30]1
%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]
6Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list23
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list29
%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]
8Input: list1 = [10, 15, 20, 25, 30, 35, 40] list2 = [25, 40, 35] Output: [10, 20, 30, 15] Explanation: resultant list = list1 - list27
[10, 15, 20, 30]0
[20, 10, 30, 15]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]
5 [20, 10, 30, 15]55____86
[10, 15, 20, 30]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]
5 [20, 10, 30, 15]9
[10, 15, 20, 30]3
map
1[10, 15, 20, 30]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]
4[10, 15, 20, 30]8
[10, 15, 20, 30]5
[10, 15, 20, 30]6
Output:
[20, 10, 30, 15]