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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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📈 Аналитический обзор Telegram-канала Learn Python Coding

Канал Learn Python Coding (@pythonre) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 058 подписчиков, занимая 3 241 место в категории Технологии и приложения и 9 590 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 058 подписчиков.

Согласно последним данным от 29 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 114, а за последние 24 часа — -8, при этом общий охват остаётся высоким.

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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 30 августа, 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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I rarely say this, but this is the best repository for mastering Python. The course is led by David Beazley, the author of Py
I rarely say this, but this is the best repository for mastering Python. The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley). In this PythonMastery.pdf, all the information is structured 👾 Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf In the Exercises folder, all the exercises are located 👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises In the Solutions folder — the solutions 👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions 👉 @codeprogrammer

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isort | Python Tools ✨ 📖 A command-line utility and library for sorting and organizing Python imports. 🏷️ #Python

mypy | Python Tools ✨ 📖 A static type checker for Python. 🏷️ #Python

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Tip: Efficiently Slice Iterators and Large Sequences with itertools.islice Explanation: Traditional list slicing (my_list[start:end]) creates a new list in memory containing the sliced elements. While convenient for small lists, this becomes memory-inefficient for very large lists and is impossible for pure iterators (like generators or file objects) that don't support direct indexing. itertools.islice provides a memory-optimized solution by returning an iterator that yields elements from a source iterable (list, generator, file, etc.) between specified start, stop (exclusive), and step indices, without first materializing the entire slice into a new collection. This "lazy" consumption of the source iterable is crucial for processing massive datasets, infinite sequences, or streams where only a portion is needed, preventing excessive memory usage and improving performance. It behaves syntactically similar to standard slicing but operates at the iterator level. Example:
import itertools
import sys

# A generator for a very large sequence
def generate_large_sequence(count):
    for i in range(count):
        yield f"Data_Item_{i}"

# Imagine needing to process only a small segment of 10 million items
total_items = 10**7
data_stream = generate_large_sequence(total_items)

# Get items from index 500 to 509 (inclusive)
# Using islice:
print("--- Using itertools.islice ---")
# islice(iterable, [start], stop, [step])
# Here, start=500, stop=510 (exclusive)
for item in itertools.islice(data_stream, 500, 510):
    print(item)

# Compare memory usage (conceptual, as actual list materialization would be massive)
# If you tried:
# large_list = list(generate_large_sequence(total_items)) # <-- HUGE memory consumption here!
# for item in large_list[500:510]:
#    print(item)

# islice consumes minimal memory, only holding iterator state.
# The `data_stream` generator itself only holds its current state, not the whole sequence.
print("\n`itertools.islice` memory footprint is negligible compared to creating a full list slice.")
no any words from your ━━━━━━━━━━━━━━━ By: @DataScience4

✨ Quiz: Writing DataFrame-Agnostic Python Code With Narwhals ✨ 📖 If you're a Python library developer wondering how to write
Quiz: Writing DataFrame-Agnostic Python Code With Narwhals ✨ 📖 If you're a Python library developer wondering how to write DataFrame-agnostic code, the Narwhals library is the solution you're looking for. 🏷️ #advanced #data-science #python

Pylint | Python Tools ✨ 📖 A static code checker for Python. 🏷️ #Python

pytest | Python Tools ✨ 📖 A test runner and framework for Python. 🏷️ #Python

Real Python - Pocket Reference (Important) #python #py #PythonTips #programming https://t.me/CodeProgrammer 🩵

flake8 | Python Tools ✨ 📖 A command-line Python linter. 🏷️ #Python

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Git | Python Tools ✨ 📖 A distributed version control system. 🏷️ #Python

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dependency | Python Glossary ✨ 📖 An external package that your project needs in order to run, build, or be developed. 🏷️ #Python

abstract method | Python Glossary ✨ 📖 A method that is marked with @abstractmethod. 🏷️ #Python

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
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_rRW2scgfRhOTc0https://t.me/Codeprogrammer

✨ Python Inner Functions: What Are They Good For? ✨ 📖 Learn how to create inner functions in Python to access nonlocal names
Python Inner Functions: What Are They Good For? ✨ 📖 Learn how to create inner functions in Python to access nonlocal names, build stateful closures, and create decorators. 🏷️ #intermediate #python