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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Сеть:Machine Learning with Python Индия10 597 Технологии и приложения3 502...

📈 Аналитический обзор Telegram-канала Learn Python Coding

Канал Learn Python Coding (@pythonre) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 39 118 подписчиков, занимая 3 502 место в категории Технологии и приложения и 10 597 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 39 118 подписчиков.

Согласно последним данным от 05 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 458, а за последние 24 часа — 21, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.68%. В первые 24 часа после публикации контент обычно набирает 1.04% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 1 048 просмотров. В течение первых суток публикация набирает 405 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как math, harvard, oxford, supervision, waybienad.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”

Благодаря высокой частоте обновлений (последние данные получены 06 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

39 118

Подписчики

+2124 часа

+1207 дней

+45830 день

1 048

Просмотры поста

~ 40524 часа

~ 51348 часов

2.68%

Коэффициент вовлеченности

~ 2

Постов в день

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Архив постов

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"Introduction to Algorithms" 📘 - an outstanding university resource for everyone studying algorithms and computer science. 🎓💻 The book covers computational complexity, data structures, algorithms on graphs, dynamic programming, divide-and-conquer methods, greedy algorithms, randomized algorithms, and many mathematical foundations of modern computer science. 🧮📊🔍 What's particularly valuable here is the combination of mathematical rigor and practical algorithmic thinking. 🧠✨ This is one of those books that greatly change the approach to problem analysis, efficiency, and computing itself. 🚀🛠 An essential tool in the library of any developer and engineer working in the field of computer science. 🏗💾 https://www.cs.mcgill.ca/~akroit/math/compsci/Cormen%20Introduction%20to%20Algorithms.pdf 🔗 #Algorithms #ComputerScience #Programming #CSStudent #TechEducation #DevTools

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If you're working with data pipelines, these repositories are very useful: 🚀📊 ibis: A Python API that allows you to write queries once and run them on different data backends, such as DuckDB, BigQuery, and Snowflake. 🐍🔗 https://github.com/ibis-project/ibis pygwalker: Instantly turns a DataFrame into an interactive UI for visual data exploration. 📈🖥️ https://github.com/Kanaries/pygwalker katana: A fast and scalable web crawler, often used for security testing and large-scale data collection/search. 🕷️🔒 https://github.com/projectdiscovery/katana #dataengineering #python #opensource #devtools #dataviz #security

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📂 Reminder about Python map()! map() — a built-in function that applies the specified function to each element of an iterable object (list, tuple, set, etc.). The picture shows the basic syntax, an example of use with lambda, and a typical case — data transformation without a manual for loop. Save it to quickly remember the syntax! 🐍💻🗺️ #Python #Coding #Programming #LearnToCode #DevTips #Tech

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"Open Data Structures" is another very useful free resource for anyone studying data structures and algorithms. 📚✨ The book discusses the implementation and analysis of basic structures: array-based lists, linked lists, hash tables, binary trees, red-black trees, heaps, sorting algorithms, graphs, and data structures for working with integers. 🔍🧮 This is a full-fledged open textbook for studying one of the fundamental topics of computer science and a good reference that's worth keeping on hand. 💻🌟 https://opendatastructures.org/ods-python.pdf 📄 👉 @PythonRe #DataStructures #Algorithms #Python #ComputerScience #OpenSource #Learning

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Do you know that Python can shift sequences without slicing and creating new lists? 🤔 When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

q.rotate(1)

A negative value rotates the queue in the other direction. ⬅️

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. 🔄

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. 💡 #Python #Programming #Deque #CodingTips #Tech #DevCommunity

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The Python library itertools contains many useful functions. 🐍✨ One of them is compress(), which returns an iterator over the elements from data, for which the corresponding element in selectors is equal to True. 🔍💻 Here's an example: 📝👇 #Python #Programming #Itertools #Coding #Tech #DataScience

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Many applications require mapping strings to integers. In Python, this usually looks like:

d = {"apple": 100, "banana": 200, "cherry": 300}

If there are 1 million keys, this can consume a lot of memory — more than 100 bytes per key. Our elephant has published a new library that uses about 9 bytes per key. Yes, only 9 bytes. Usage looks like this:

from fastconstmap import ConstMap

d = {"apple": 100, "banana": 200, "cherry": 300}
m = ConstMap(d)

m["apple"]                  # -> 100
m.get_many(["banana", "cherry"])  # -> [200, 300]

It can be significantly faster (for example, up to 2 times in some cases) than the standard dictionary. It can also be serialized and deserialized to disk or network for convenient reuse. https://pypi.org/project/fastconstmap/ github: https://github.com/lemire/fastconstmap 👉 @PythonRe

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Repost from Machine Learning with Python

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Python Basics Notes 🐍📚 https://t.me/pythonRe 🔗 #Python #Coding #Programming #LearnPython #Tech #DevCommunity