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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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📈 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 40 878 suscriptores, ocupando la posición 3 287 en la categoría Tecnologías y Aplicaciones y el puesto 9 164 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 878 suscriptores.

Según los últimos datos del 29 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 336, y en las últimas 24 horas de 1, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.33%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.52% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 2 179 visualizaciones. En el primer día suele acumular 622 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
  • 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 octubre, 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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Archivo de publicaciones
✨ wildcard import | Python Glossary ✨ 📖 An import uses the star syntax to pull many names into your current namespace at once. 🏷️ #Python

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✨ transitive dependency | Python Glossary ✨ 📖 An indirect requirement of your project. 🏷️ #Python

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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_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

✨ GeoPandas Basics: Maps, Projections, and Spatial Joins ✨ 📖 Dive into GeoPandas with this tutorial covering data loading, m
✨ GeoPandas Basics: Maps, Projections, and Spatial Joins ✨ 📖 Dive into GeoPandas with this tutorial covering data loading, mapping, CRS concepts, projections, and spatial joins for intuitive analysis. 🏷️ #intermediate #data-science

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✨ relative import | Python Glossary ✨ 📖 Import modules from the same package or parent packages using leading dots. 🏷️ #Python

Python Clean Code: Stop Writing Bad Code — Lessons from Uncle Bob Are you tired of writing messy and unorganized code that le
Python Clean Code: Stop Writing Bad Code — Lessons from Uncle Bob Are you tired of writing messy and unorganized code that leads to frustration and bugs? You can transform your code from a confusing mess into something crystal clear with a few simple changes. In this article, we'll explore key principles from the book "Clean Code" by Robert C. Martin, also known as Uncle Bob, and apply them to Python. Whether you're a web developer, software engineer, data analyst, or data scientist, these principles will help you write clean, readable, and maintainable Python code. Read: https://habr.com/en/articles/841820/ https://t.me/CodeProgrammer 🧠

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Working with f-strings: more possibilities than it seems! f-strings often replace .format() in everyday code, but their capabilities are not always fully utilized. They support formatting, function calls, working with data structures, and convenient debugging (from 3.8+). f-strings are convenient for aligning columns without additional tools. This makes the output readable in the CLI and logs:
rows = [
    ("id", "name", "role"),
    (1, "Ivan", "admin"),
    (2, "Olga", "editor"),
]

for r in rows:
    print(f"{r[0]:<5} {r[1]:<10} {r[2]:<10}")
Debug expressions (Python 3.8+): {x=> displays the name and value of the variable, which speeds up debugging. Supports formatting of calculations:
x = 12
y = 7
print(f"{x=} {y=} {x*y=} x/y={x/y:.3f}")
Specifiers !r, !a: !r - repr(), !a - ascii() for unambiguous logs. Eliminates ambiguities in the output of objects:
path = "/var/data/config.yaml"
print(f"{path!r} {path!a}")  # repr and ascii()
Specifiers support width and padding, for example 08d for zeros. This is convenient for reports and IDs:
n = 42
print(f"{n:08d}")  # → #00000042
You can access dictionaries and immediately calculate metrics, for example len():
data = {"user": "Ivan", "items": [1, 2, 3]}
print(f"{data['user&#39]}=», items={data['items&#39]}")
print(f"len(data['items&#39])={len(data['items&#39])}")
🔥 f-strings are a cool tool for formatting, logging, and debugging, if you apply them taking into account the version of Python and the context of the output. 🚪 @DataScience4

🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸 Join our channel today for free! Tomorrow it will cost 500$! https://t
🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸 Join our channel today for free! Tomorrow it will cost 500$! https://t.me/+0-w7MQwkOs02MmJi You can join at this link! 👆👇 https://t.me/+0-w7MQwkOs02MmJi

✨ Quiz: How to Integrate Local LLMs With Ollama and Python ✨ 📖 Check your understanding of using Ollama with Python to run l
✨ Quiz: How to Integrate Local LLMs With Ollama and Python ✨ 📖 Check your understanding of using Ollama with Python to run local LLMs, generate text, chat, and call tools for private, offline apps. 🏷️ #intermediate #ai #tools

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✨ unpacking | Python Glossary ✨ 📖 Passing multiple values at once by expanding an iterable. 🏷️ #Python

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✨ Anaconda Navigator | Python Tools ✨ 📖 A desktop graphical interface included with the Anaconda Distribution. 🏷️ #Python

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✨ Quiz: How to Integrate ChatGPT's API With Python Projects ✨ 📖 Test your knowledge of the ChatGPT API in Python. Practice s
✨ Quiz: How to Integrate ChatGPT's API With Python Projects ✨ 📖 Test your knowledge of the ChatGPT API in Python. Practice sending prompts with openai and handling text and code responses in this quick quiz. 🏷️ #intermediate #ai #api