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Learn Python Coding

Learn Python Coding

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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

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📈 Аналітичний огляд Telegram-каналу Learn Python Coding

Канал Learn Python Coding (@pythonre) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 40 049 підписників, посідаючи 3 238 місце в категорії Технології та додатки та 9 700 місце у регіоні Індія.

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З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 40 049 підписників.

За останніми даними від 26 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 182, а за останні 24 години на -10, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 2.93%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.12% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 1 172 переглядів. Протягом першої доби публікація в середньому набирає 447 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як math, harvard, oxford, supervision, waybienad.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
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

Завдяки високій частоті оновлень (останні дані отримано 27 серпня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.

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collections.Counter — counting elements in a single line. 📊 Counting elements without loops with Counter 🔄 Do you need to count how many times each word appears in a text or how many duplicates there are in a list? Don't reinvent the wheel with for loops and dictionaries. The built-in collections module will do everything for you. 🚀 🛠 Code:
from collections import Counter

words = ["apple", "banana", "apple", "cherry", "banana", "apple"]
word_counts = Counter(words)

print(word_counts)
# Output: Counter({'apple': 3, 'banana': 2, 'cherry': 1})

# Bonus: the top 2 most frequent elements
print(word_counts.most_common(2))
# Output: [('apple', 3), ('banana', 2)]
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**Today we will examine __call__ — a data filter object!** 🧠 It allows a class instance to work as a function, preserving the state and filtering rules. ⚙️ Let's create a filter for numbers that only passes even ones and strictly greater than a specified threshold:
class EvenFilter:
    def __init__(self, threshold):
        self.threshold = threshold

    def __call__(self, numbers):
        return [n for n in numbers if n % 2 == 0 and n > self.threshold]
Let's use the filter in practice:
f = EvenFilter(5)
nums = [1, 4, 6, 7, 10]
print(f(nums))  # [6, 10]
Now each instance can have its own rules:
f2 = EvenFilter(8)
print(f2(nums))  # [10]
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🔥 10 GitHub Repositories to Scrape Almost Any Website 1. Firecrawl Turns entire websites into clean, AI-ready Markdown or structured data with just a few API calls. Perfect for feeding LLMs. 🤖 2. Crawl4AI An open source python crawler built specifically for AI. Extracts clean, structured content optimized for LLMs. 🐍 3. Browser Use AI Agent that control browsers like a human. It allows an AI agent to dynamically visually navigate, click elements, bypass popups, and extract data. 🖱️ 4. Crawlee A powerful scraping framework for building fast, reliable crawlers with support for Playwright, Puppeteer, and Cheerio. ⚡ 5. Scrapy One of the most popular Python frameworks for large-scale web scraping and crawling projects. 🕷️ 6. MarkItDown Converts PDFs, Office documents, HTML, and many other file types into clean Markdown for AI workflows. 📄 7. Scrapling A modern Python scraping library that combines speed, browser automation, and smart parsing with a simple API. 🚀 8. Skyvern An AI-powered scraping tool that dynamically solve CAPTCHAs, log into complex portals, and extract data without requiring any pre-defined HTML selectors or XPaths. 🔓 9. AutoScraper Automatically learns how to extract similar data from web pages by showing it just a few examples. 🧠 10. curl-impersonate Makes cURL mimic real browsers like Chrome and Safari to bypass bot detection and access protected websites more reliably. 🕵️ 💡 Save this list for your next web scraping or AI automation project. #WebScraping #AI #GitHub #Python #Automation #LLM ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Repost from Udemy Free Coupons
250+ Python DSA Coding Practice Test [Questions & Answers] Python DSA Coding Interview Questions and Answers (Solution Code w
250+ Python DSA Coding Practice Test [Questions & Answers] Python DSA Coding Interview Questions and Answers (Solution Code with Detailed Explanations) | Coding Practice Exercises… 🏷 Category: development 🌍 Language: English (US) 👥 Students: 110 students ⭐️ Rating: 0.0/5.0 (0 reviews) 🏃‍♂️ Enrollments Left: 5 ⏳ Expires In: 0D:30H:30M 💰 Price: $23.03FREE 🆔 Coupon: 731B3C9353AE09ABE19E ⚠️ Watch 2 short ads to unlock your free access. 💎 By: https://t.me/Udemy26 #Programming #Coding #Development #Tech #Python #DataScience

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3 quick ways to merge dictionaries in Python 🐍 1️⃣ The operator | (Python 3.9+) — the most modern and elegant way. Creates a new dictionary.
dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 99, 'c': 4}

combined = dict1 | dict2
# Result: {'a': 1, 'b': 99, 'c': 4} (values of the second dictionary replace the first)
2️⃣ The in-place update operator |= (Python 3.9+) — if you need to modify the first dictionary in place.
dict1 |= dict2
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