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

Red:Machine Learning with Python India9 590 Tecnologías y Aplicaciones3 241...

📈 Análisis del canal de Telegram Learn Python Coding

El canal Learn Python Coding (@pythonre) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 058 suscriptores, ocupando la posición 3 241 en la categoría Tecnologías y Aplicaciones y el puesto 9 590 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 058 suscriptores.

Según los últimos datos del 29 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 114, y en las últimas 24 horas de -8, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.75%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.09% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 103 visualizaciones. En el primer día suele acumular 438 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
Intereses temáticos: El contenido se centra en temas clave como math, harvard, oxford, supervision, waybienad.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 30 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

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40 058

Suscriptores

-824 horas

-517 días

+11430 días

1 103

Visitas de la publicación

~ 43824 horas

~ 65948 horas

2.75%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

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✨ Quiz: Python Inner Functions: What Are They Good For? ✨ 📖 Test inner functions, closures, nonlocal, and decorators in Python. Build confidence and learn to keep state across calls. Try the quiz now. 🏷️ #intermediate #python

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✨ setuptools | Python Tools ✨ 📖 A packaging library and build backend for Python. 🏷️ #Python

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🚀 Master Data Science & Programming! Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today! 🔰 Machine Learning with Python Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. https://t.me/CodeProgrammer 🔖 Machine Learning Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications. https://t.me/DataScienceM 🧠 Code With Python This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills. https://t.me/DataScience4 🎯 PyData Careers | Quiz Python Data Science jobs, interview tips, and career insights for aspiring professionals. https://t.me/DataScienceQ 💾 Kaggle Data Hub Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects. https://t.me/datasets1 🧑‍🎓 Udemy Coupons | Courses The first channel in Telegram that offers free Udemy coupons https://t.me/DataScienceC 😀 ML Research Hub Advancing research in Machine Learning – practical insights, tools, and techniques for researchers. https://t.me/DataScienceT 💬 Data Science Chat An active community group for discussing data challenges and networking with peers. https://t.me/DataScience9 🐍 Python Arab| بايثون عربي The largest Arabic-speaking group for Python developers to share knowledge and help. https://t.me/PythonArab 🖊 Data Science Jupyter Notebooks Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post. https://t.me/DataScienceN 📺 Free Online Courses | Videos Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners. https://t.me/DataScienceV 📈 Data Analytics Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. https://t.me/DataAnalyticsX 🎧 Learn Python Hub Master Python with step-by-step courses – from basics to advanced projects and practical applications. https://t.me/Python53 ⭐️ Research Papers Professional Academic Writing & Simulation Services https://t.me/DataScienceY ━━━━━━━━━━━━━━━━━━ Admin: @HusseinSheikho

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✨ Conda | Python Tools ✨ 📖 A cross-platform package and environment manager for Python. 🏷️ #Python

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🐍 Fun Illustration of Python List Methods 🎯 https://t.me/DataScience4 ⭐

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✨ Sphinx | Python Tools ✨ 📖 A documentation generator for Python. 🏷️ #Python

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✨ PyInstaller | Python Tools ✨ 📖 A freezing tool that bundles Python applications for distribution. 🏷️ #Python

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Repost from Machine Learning with Python

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Of course! Here is a cheat sheet on Python's ternary operator, formatted with Telegram-friendly Markdown for code blocks. --- Python Cheat Sheet: The Ternary Operator 🚀 Shorten your if/else statements for compact, one-line value selection. It's also known as a conditional expression. #### 📜 The Standard if/else Block This is the classic, multi-line way to assign a value based on a condition.

# Check if a user is an adult
age = 20
status = ""

if age >= 18:
    status = "Adult"
else:
    status = "Minor"

print(status)
# Output: Adult

--- #### ✅ The Ternary Operator (One-Line if/else) The same logic can be written in a single, clean line. Syntax: value_if_true if condition else value_if_false Let's rewrite the example above:

age = 20

# Assign 'Adult' if age >= 18, otherwise assign 'Minor'
status = "Adult" if age >= 18 else "Minor"

print(status)
# Output: Adult

--- 💡 More Examples The ternary operator is an expression, meaning it returns a value and can be used almost anywhere. 1. Inside a Function return

def get_fee(is_member):
    # Return 5 if they are a member, otherwise 15
    return 5.00 if is_member else 15.00

print(f"Your fee is: ${get_fee(True)}")
# Output: Your fee is: $5.0
print(f"Your fee is: ${get_fee(False)}")
# Output: Your fee is: $15.0

2. Inside an f-string or print()

is_logged_in = False

print(f"User status: {'Online' if is_logged_in else 'Offline'}")
# Output: User status: Offline

3. With List Comprehensions (Advanced) This is where it becomes incredibly powerful for creating new lists.

numbers = [1, 10, 5, 22, 3, -4]

# Create a new list labeling each number as "even" or "odd"
labels = ["even" if n % 2 == 0 else "odd" for n in numbers]
print(labels)
# Output: ['odd', 'even', 'odd', 'even', 'odd', 'even']

# Create a new list of only positive numbers, or 0 for negatives
sanitized = [n if n > 0 else 0 for n in numbers]
print(sanitized)
# Output: [1, 10, 5, 22, 3, 0]

--- 🧠 When to Use It (and When Not To!) • DO use it for simple, clear, and readable assignments. If it reads like a natural sentence, it's a good fit. • DON'T use it for complex logic or nest them. It quickly becomes unreadable. ❌ BAD EXAMPLE (Avoid This!):

# This is very hard to read!
x = 10
message = "High" if x > 50 else ("Medium" if x > 5 else "Low")

✅ BETTER (Use a standard if/elif/else for clarity):

x = 10
if x > 50:
    message = "High"
elif x > 5:
    message = "Medium"
else:
    message = "Low"

━━━━━━━━━━━━━━━ By: @DataScience4 ✨

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❌ YOU CAN'T USE LAMBDA LIKE THIS IN PYTHON The main mistake is turning lambda into a logic dump: adding side effects, print calls, long conditions, and calculations to it. Such lambdas are hard to read, impossible to debug properly, and they violate the very idea of being a short and clean function. Everything complex should be moved into a regular function. Subscribe for more tips every day !

# you can't do this - lambda with state changes
data = [1, 2, 3]
logs = []

# dangerous antipattern
process = lambda x: logs.append(f"processed {x}") or (x * 10)

result = [process(n) for n in data]

print("RESULT:", result)
print("LOGS:", logs)

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✨ Pipenv | Python Tools ✨ 📖 A dependency and virtual environment manager for Python. 🏷️ #Python