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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Red:📈 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 39 629 suscriptores, ocupando la posición 3 400 en la categoría Tecnologías y Aplicaciones y el puesto 9 883 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 39 629 suscriptores.
Según los últimos datos del 13 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 401, y en las últimas 24 horas de 21, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.75%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.15% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 692 visualizaciones. En el primer día suele acumular 455 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 14 julio, 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.
39 629
Suscriptores
+2124 horas
+1397 días
+40130 días
Archivo de publicaciones
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✨ Unpacking the remaining elements 🧩
Sometimes you need to extract the first and last elements from a list, while grouping everything in the middle separately. Instead of struggling with slicing ([1:-1]), use the asterisk (*). ⭐️
data = ["CEO", "Middle Python Dev", "Junior Dev", "QA", "HR"]
# The asterisk automatically collects everything "extra" into a separate list.
boss, *team, hr = data
print(boss) # CEO
print(team) # ['Middle Python Dev', 'Junior Dev', 'QA']
print(hr) # HR39 632
Python has a built-in topological dependency sorter!🚀
If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
ts = TopologicalSorter()
ts.add("deploy", "test")
ts.add("test", "build")tuple(ts.static_order())("build", "test", "deploy")39 632
💡 Replacing if-else with Match-Case
Starting with Python 3.10, we have a powerful tool: Structural Pattern Matching (match-case). This is not just an analog of switch-case from other languages; it's much more flexible. 🚀
Imagine you're writing a command handler for a bot. 🤖
❌ How NOT to do it:
def handle_command(command):
if command == "start":
return "Hello! I'm a bot."
elif command == "help":
return "Here's a list of available commands..."
elif command == "stop":
return "Goodbye!"
else:
return "Unknown command."def handle_command(command):
match command:
case "start":
return "Hello! I'm a bot."
case "help":
return "Here's a list of available commands..."
case "stop":
return "Goodbye!"
case _: # The underscore symbol catches everything else (default)
return "Unknown command."elif statements. 🧐
You can pass data structures in the case statements and check their structure and content on the fly. 🔍
It's easy to combine cases. 🧩
#Python #Programming #MatchCase #CodingTips #Python310 #Developer
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🚀 Looking for a portfolio-ready NLP project?
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
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What's the difference between is and == in Python?
The == operator checks whether the values of two objects are equal. In contrast, is determines whether variables refer to same object in memory. That is, == compares the content, while is checks the identity of the objects 🐍🔍
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📌 How to make code cleaner with any() and all() 🐍
Do you often have to check lists for compliance with conditions? Forget about cumbersome loops! 🚫🔄
any() — returns True if at least one element is true. ✅
all() — returns True only if all elements are true. 🔒
# Example: checking if there are negative numbers
numbers = [1, 5, -3, 7]
# Bad: through a loop
has_negative = False
for num in numbers:
if num < 0:
has_negative = True
# Beautiful:
has_negative = any(num < 0 for num in numbers) # True ✨
#python #coding #pythonprogramming #learnpython #codeoptimization #programmingtips
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collections.Counter — counting elements in a single line. 📊
Counting elements without loops with Counter 🔄
Do you need to count how many times each word appears in a text or how many duplicates there are in a list? Don't reinvent the wheel with for loops and dictionaries. The built-in collections module will do everything for you. 🚀
🛠 Code:
from collections import Counter
words = ["apple", "banana", "apple", "cherry", "banana", "apple"]
word_counts = Counter(words)
print(word_counts)
# Output: Counter({'apple': 3, 'banana': 2, 'cherry': 1})
# Bonus: the top 2 most frequent elements
print(word_counts.most_common(2))
# Output: [('apple', 3), ('banana', 2)]
