Learn Python Coding
رفتن به کانال در Telegram
Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho
نمایش بیشتر
شبکه:📈 تحلیل کانال تلگرام Learn Python Coding
کانال Learn Python Coding (@pythonre) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 40 879 مشترک است و جایگاه 3 292 را در دسته فناوری و برنامه‌ها و رتبه 9 353 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 879 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 27 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 395 و در ۲۴ ساعت گذشته برابر -12 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 3.98% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.48% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 1 628 بازدید دریافت می‌کند. در اولین روز معمولاً 604 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 2 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند math, harvard, oxford, supervision, waybienad تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho”
به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 28 اکتبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.
40 879
مشترکین
-1224 ساعت
-127 روز
+39530 روز
آرشیو پست ها
40 880
✨ fnmatch | Python Standard Library ✨
📖 Provides tools for Unix shell-style wildcard pattern matching against filename strings.
🏷️ #Python
40 880
Read this once. There won't be a second message.
Brainlancer just launched today.
Investor-backed marketplace for ALL AI freelancers. Designers, builders, copywriters, marketers, video creators, automation experts, consultants.
If you build, design, write, or sell anything with AI, this is your moment.
How it works:
• Register free at brainlancer.com
• Stripe verification, 5 minutes, instant approval
• List up to 5 services from $49 to $4,999
• Add monthly subscriptions on top if you want
• We bring the clients. You keep 80%.
The deal:
No subscription.
No bidding.
No chasing.
We pay all marketing.
Real talk: no services live yet. We just launched. Whoever joins first gets seen first.
The first 100 Brainlancers are onboarding right now.
In 6 months others will have founding status, recurring income, featured services on the homepage.
You'll scroll past and remember this post.
Don't.
→ brainlancer.com
40 880
Repost from Machine Learning with Python
GitHub repositories to enhance your Python proficiency:
- Web development with Django — https://github.com/django/django
- Data Science tools — https://github.com/rasbt/python-machine-learning-book
- Algorithmic challenges — https://github.com/TheAlgorithms/Python
- Machine learning recipes — https://github.com/ageron/handson-ml2
- Testing best practices — https://github.com/pytest-dev/pytest
- Automation scripts — https://github.com/soimort/you-get
- Advanced Python concepts — https://github.com/faif/python-patterns
Bookmark and share
https://t.me/CodeProgrammer 🌟
40 880
Repost from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
40 880
✨ AI Coding Agents Guide: A Map of the Four Workflow Types ✨
📖 AI coding agents come in four types: IDE, terminal, PR, and cloud. Learn how each workflow fits into modern Python development.
🏷️ #intermediate #ai
40 880
✨ Quiz: AI Coding Agents Guide: A Map of the Four Workflow Types ✨
📖 Check your understanding of how AI coding agents fit into your workflow through four interaction modes: IDE, terminal, pull request, and cloud.
🏷️ #intermediate #ai
40 880
✨ fileinput | Python Standard Library ✨
📖 Provides tools for iterating over lines from multiple input files or standard input, with support for in-place file editing.
🏷️ #Python
40 880
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
40 880
✨ Real Python Merchandise ✨
📖 Support realpython.com with this collection of Python mugs, t-shirts, hoodies, and stickers.
🏷️ #Python
40 880
Today, the public mint for Lobsters on TON goes live on Getgems 🦞
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.
Public mint: today, 16:00
Price: 50 TON
👉 Mint your Lobster on Getgems 🦞🦞🦞
40 880
✨ filecmp | Python Standard Library ✨
📖 Provides tools for comparing files and directory trees, with a caching layer for efficiency and a dircmp class for recursive directory analysis.
🏷️ #Python
