Python Job | Вакансии | Стажировки
Все каналы и цены - @best_ITjob / neironka.ru Разместить вакансию или рекламу - @hr_rus http://telega.in/channels/job_python/card https://knd.gov.ru/license?id=675827470acf9c1f42e2cb43 #5MHNM @Spiral_Yuri
Show more📈 Analytical overview of Telegram channel Python Job | Вакансии | Стажировки
Channel Python Job | Вакансии | Стажировки (@job_python) in the Russian language segment is an active participant. Currently, the community unites 23 374 subscribers, ranking 1 696 in the Career category and 28 020 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 23 374 subscribers.
According to the latest data from 01 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 248 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 10.64%. Within the first 24 hours after publication, content typically collects 6.21% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 486 views. Within the first day, a publication typically gains 1 452 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as api, senior, архитектура, удалёнка, middle.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Все каналы и цены - @best_ITjob / neironka.ru
Разместить вакансию или рекламу - @hr_rus
http://telega.in/channels/job_python/card
https://knd.gov.ru/license?id=675827470acf9c1f42e2cb43
#5MHNM
@Spiral_Yuri”
Thanks to the high frequency of updates (latest data received on 02 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Career category.
functools.lru_cache и как его использовать?
Пример кода:
from functools import lru_cache
@lru_cache(maxsize=32)
def fibonacci(n):
if n < 2:
return n
return fibonacci(n-1) + fibonacci(n-2)
print(fibonacci(10)) # Вывод: 55
❗️Ответ:
lru_cache — это декоратор, который кэширует результаты функции, чтобы избежать повторных вычислений. Он использует алгоритм LRU (Least Recently Used) для удаления наименее используемых элементов. Пример: @lru_cache(maxsize=128).
Ссылка на документацию по lru_cache