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 39 131 подписчиков, занимая 3 502 место в категории Технологии и приложения и 10 597 место в регионе Индия.

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С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 39 131 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.

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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.
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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”

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.

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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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There's a floating-point number in Python and you need to output it as a percentage - use the % format in the f-string

x = .023
print(f'{x:.2%}')  # 2.30%

x = .02375
print(f'{x:.2%}')  # 2.38% -- rounded off!

x = 1.02375
print(f'{x:.2%}')  # 102.38%

👉 @PythonRe

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Master Python the Right Way – Without Procrastination. 🐍✨ When I first started learning Python, I quickly realized: You can't master a programming language just by reading syntax or watching tutorials. 📚🚫 Real growth happens when you practice, build, and solve problems on your own. 🛠💻 That's exactly why I've compiled a collection of Python programs – designed to take you from basics to advanced logic-building. 📈🧠 What is this collection about? 🤔 ✔️ Beginner to advanced programs with clear explanations ✔️ Pattern-based exercises to strengthen core fundamentals ✔️ Problem-solving programs that sharpen logical thinking Why is this important? 🌟 You don't just learn "how to code", you start learning "how to think like a programmer". 🧠⚡️ This is perfect for: 🎯 • Preparing for technical interviews 🤝 • Participating in coding challenges 🏆 • Building real-world Python projects 🚀

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🧐 Python Cheatsheet — a convenient cheat sheet for Python that really saves time at work! The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code. Repo: https://github.com/onyxwizard/python-cheatsheet📱 https://t.me/pythonRe 👩‍💻

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codes = ["A", "B", "C"]
found = False
for code in codes:
    if code == "B":
        found = True
        break
if found:
    print("Incorrect: Code B found (less efficient).")

Brief Explanation: The in operator is optimized for membership checks, offering better performance and cleaner code than manual loops, especially for larger lists. --- 5. Avoiding Unnecessary List Conversions Description: Many functions and methods return iterators or generator objects for efficiency. Converting these directly to a list without need can waste memory and computation if you only need to process elements one by one. Correct Usage: Process iterators directly when possible, convert to list only if multiple passes or random access is needed.

squares_gen = (x*x for x in range(5)) # Generator expression
for s in squares_gen: # Process elements one by one
    print(f"Correct: {s}", end=" ") # Output: 0 1 4 9 16
print()

# If you need the full list:
squares_list = list(x*x for x in range(5))
print(f"Correct (list conversion): {squares_list}") # Output: [0, 1, 4, 9, 16]

Incorrect Usage: Unnecessarily converting iterators to lists when single-pass processing suffices.

data_stream = map(str.upper, ['apple', 'banana', 'cherry'])
# If you only need to print them once:
full_list = list(data_stream) # Unnecessary list creation
for item in full_list:
    print(f"Incorrect: {item}", end=" ") # Output: APPLE BANANA CHERRY
print()

Brief Explanation: Iterators/generators are memory-efficient for single-pass operations. Convert to list() only when random access, repeated iteration, or a material collection is strictly required. https://t.me/pythonRe 🌟

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Top 10 Python One Liners! 1️⃣ Reverse a string:

reversed_string = "Hello World"[::-1]

2️⃣ Check if a number is even:

is_even = lambda x: x % 2 == 0

3️⃣ Find the factorial of a number:

factorial = lambda x: 1 if x == 0 else x * factorial(x - 1)

4️⃣ Read a file and print its contents:

[print(line.strip()) for line in open('file.txt')]

5️⃣ Create a list of squares:

squares = [x**2 for x in range(10)]

6️⃣ Flatten a list of lists:

flat_list = [item for sublist in [[1, 2], [3, 4], [5, 6]] for item in sublist]

7️⃣ Find the length of a list:

length = len([1, 2, 3, 4])

8️⃣ Create a dictionary from two lists:

keys = ['a', 'b', 'c']; values = [1, 2, 3]; dictionary = dict(zip(keys, values))

9️⃣ Generate a list of random numbers:

import random; random_numbers = [random.randint(0, 100) for _ in range(10)]

🔟 Check if a string is a palindrome:

is_palindrome = lambda s: s == s[::-1]

Mastering these one-liners can significantly improve your coding efficiency and make your code more concise. https://t.me/pythonRe ✉️

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What is a Lambda Function? A lambda function is a small anonymous function defined using the lambda keyword. It's often used for short, throwaway functions that are only needed temporarily. Basic Syntax- The syntax of a lambda function is: "lambda arguments: expression" -arguments: A comma-separated list of parameters. -expression: An expression that is evaluated and returned. Examples 1️⃣ Basic Lambda Function: "add = lambda x, y: x + y print(add(2, 3)) # Output: 5 " Here, lambda x, y: x + y is a lambda function that adds two numbers. 2️⃣ Lambda with map(): "numbers = [1, 2, 3, 4, 5] squared = list(map(lambda x: x ** 2, numbers)) print(squared) # Output: [1, 4, 9, 16, 25] " map() applies the lambda function to each item in the numbers list. 3️⃣ Lambda with filter(): "numbers = [1, 2, 3, 4, 5] even = list(filter(lambda x: x % 2 == 0, numbers)) print(even) # Output: [2, 4] " filter() uses the lambda function to filter out only the even numbers. 4️⃣ Lambda with reduce(): "from functools import reduce numbers = [1, 2, 3, 4, 5] product = reduce(lambda x, y: x * y, numbers) print(product) # Output: 120 " reduce() applies the lambda function cumulatively to the items in the list. Pros and Cons- Pros: -> Concise and readable. -> Useful for small, simple functions. -> Handy for functional programming (e.g., map, filter, reduce). Cons: -> Limited to single expressions. -> Can be less readable if overused. -> Lack of function name can make debugging harder. Lambda functions are an excellent tool for any Python developer to have in their toolkit. They can help streamline your code and make your functions more elegant and efficient.

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✨ Quiz: The Factory Method Pattern and Its Implementation in Python ✨ 📖 Check your grasp of the Factory Method pattern in Python: when to use it, the roles involved, and how to implement a flexible object factory. 🏷️ #intermediate #best-practices

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✨ netrc | Python Standard Library ✨ 📖 Provides tools for parsing .netrc credentials files and looking up logins, accounts, and passwords by host. 🏷️ #Python

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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

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Repost from Machine Learning with Python

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