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Canal fuente @python_academy · Post #2178 · 18 sept

Загрузка файлов асинхронно Если у нас есть список URL картинок для загрузки, использование простого цикла for позволит нам загрузить их последовательно, по одной за раз. Однако, для задач, включающих загрузку большогоколичества маленьких файлов, параллелизация может заметно ускорить процесс. Для параллелизации мы можем использовать ThreadPoolExecutor из модуля concurrent.futures. Этот инструмент позволяет выполнить функцию загрузки в нескольких параллельных потоках, где в конструкторе необходимо указать максимальное количество потоков для одновременного выполнения. С помощью метода .map(download, urls) можно развернуть функцию загрузки на каждый URL из списка, обеспечивая их параллельнуюобработку. Важно понимать, что так как загрузка файлов является IO-операцией, данный метод неускоряет выполнение кода в прямом смысле, а скорее позволяет начать загрузку следующего файла, не ожидая завершения предыдущего. #python#threading

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The Devs

@thedevs · Post #1190 · 06/08/2018, 16:45

Pyxel, a retro game development environment in Python. #tools#game#python @thedevs https://kutt.it/vUDMzQ

The Devs

@thedevs · Post #1585 · 01/10/2019, 17:08

mitmproxy, a swiss-army knife for debugging, testing, privacy measurements, and penetration testing. #tools#coding#security#python @thedevs https://kutt.it/iJs5WX

djangoproject

@djangoproject · Post #454 · 03/10/2017, 15:43

https://engineering.instagram.com/web-service-efficiency-at-instagram-with-python-4976d078e366 Web Service Efficiency at Instagram with Python #Instagram currently features the world’s largest deployment of the #Django web framework, which is written entirely in #Python. We initially chose to use Python because of its reputation for simplicity and practicality, which aligns well with our philosophy of “do the simple thing first.” But simplicity can come with a tradeoff: efficiency...

djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,

Repositorio data science

@repo_science · Post #3533 · 23/08/2023, 16:42

#python#book#DataScience 📚 Encyclopedia of Data Science and Machine Learning Description Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. 📆2023 ⚖️155 MB 🔗Link ----- Main channel:@repo_science Coupons: @freecoupons_reposcience -----

Repositorio data science

@repo_science · Post #3268 · 06/06/2023, 00:41

#python#algorithms 🐍 Algorithmic Thinking with Python: Foundations Learn how to develop your algorithmic thinking skills to become a better problem solver. 🗣Robin Andrews 📆2022-04-26 ⌚️1h 11m 🔗Link ----- Main channel:@repo_science Coupons: @freecoupons_reposcience -----

Analytics and growth mindset ️

@thinkbroadly · Post #9 · 20/05/2024, 09:18

🔥 20 Step-by-Step Data Analysis Projects with Python Code Below are popular data analysis projects from Kaggle, Github, and Medium users. They will: - Help you gain skills in working with real data - Introduce you to Python libraries for data analysis - Inspire you for your own data analysis projects #data_analyst#data_analysis#data_projects ☕Coffee Shop Sales Analysis ⚽ FIFA World Cup Data Analysis ⚡️ADIDAS Sales Analysis 📱Netflix Data Analysis (1) ⚡️ Zomato Data Analysis 🍫 Product Sales Analysis 🚕 Uber Rides Data Analysis 👀Smartwatch Data Analysis 🤑Financial Budget Analysis 📱Netflix Data Analysis (2) 🎮 Video Game Sales Analysis 🤓 Is There a Trend of Increasing Geek Girls? 🏆 Let's Discover More About the Olympic Games! 📉Marketing Analysis 🐶Animal Shelter Data Analysis 📱 Amazon Data Analysis 💲Billionaire Data Analysis 📱 Credit Card Data Analysis 😏Pokemon Data Analysis 📱Spotify Data Analysis. What Does It Take to Hit the Charts #DataAnalyst#DataAnalytics#DataAnalysis#data_analyst#python If you find this content useful, give it a🔥!

GitHub Trends

@githubtrending · Post #15386 · 03/01/2026, 11:30

#python#beancount Beancount is free, open-source double-entry accounting software that uses simple text files to track finances, create reports, and view data via a web interface. Download the current stable Version 3 from GitHub, check docs at beancount.github.io/docs, and ask questions on the mailing list. This helps you manage money flexibly with version control, automation, privacy, and no vendor lock-in, saving time on tracking expenses, assets, and budgets accurately. https://github.com/beancount/beancount

GitHub Trends

@githubtrending · Post #15161 · 21/09/2025, 12:30

#python#ai_researcher AI-Researcher is a powerful tool that fully automates scientific research from start to finish. It can review literature, generate new research ideas, design and implement algorithms, validate results, and even write complete academic papers. You just provide research ideas or reference papers, and it handles the rest using advanced AI agents. This saves you time and effort by streamlining complex research tasks, helping you innovate faster without needing deep technical expertise. It supports multiple AI models, offers a user-friendly web interface, and includes a benchmark to evaluate research quality, making it an efficient assistant for accelerating scientific discovery. https://github.com/HKUDS/AI-Researcher

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