Learn Python Coding
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
Mostrar más📈 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 40 049 suscriptores, ocupando la posición 3 238 en la categoría Tecnologías y Aplicaciones y el puesto 9 700 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 049 suscriptores.
Según los últimos datos del 26 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 182, y en las últimas 24 horas de -10, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.93%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.12% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 172 visualizaciones. En el primer día suele acumular 447 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 27 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.
add() adds an element to the set, update() combines several elements, and intersection() helps quickly find common values between data sets.
In the picture — the main methods and operations for working with set: addition, removal, union, intersection, difference, and set checks.
Save it to not lose it!
#Python #SetMethods #Coding #DataScience #Programming #HelloEncyclo
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👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHOsafe = dict(config)
MappingProxyType creates a read-only proxy over a dictionary — writing through it becomes impossible, but the data is not copied.
readonly["debug"] = True # TypeError
At the same time, the proxy remains alive: if the original dictionary changes, the changes will automatically be reflected in the read-only view.
config["debug"] = True
This is especially useful for configurations, internal APIs, overall state, and data protection within libraries.
def get_settings():
return MappingProxyType(settings)
🔥 MappingProxyType allows you to provide a read-only view of the dictionary without copying and without the risk of mutation through the returned object.
#Python #Immutable #DataProtection #MappingProxyType #ProgrammingTips #NoCopy
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👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHOfrom collections import Counter
# Initial list with duplicate elements
logs = ["error", "info", "error", "warning", "error", "info"]
# 1. Instantly count the number of occurrences
count_dict = Counter(logs)
print(count_dict) # Counter({'error': 3, 'info': 2, 'warning': 1})
# 2. Get the most frequent elements (Top-2)
print(count_dict.most_common(2)) # [('error', 3), ('info', 2)]
# 3. Set math for counters
clicks_day1 = Counter(item=4, banner=2)
clicks_day2 = Counter(item=1, banner=5)
# Combine the results of two days in a single operation
print(clicks_day1 + clicks_day2) # Counter({'banner': 7, 'item': 5})
Forget about manual loops and dictionaries 🚫🔄
When you need to count the frequency of words in a text, the distribution of log types, or popular products in a store, developers usually create an empty dictionary and write a loop with a check if key not in dict: dict[key] = 1. The Counter class takes all this dirty work on itself and makes it as efficient as possible.
— Automatic initialization: You no longer need to check if a key exists in the dictionary. If the element is not there, Counter will not throw a KeyError, but simply return 0. 🛡️
— Finding leaders without sorting: The most_common(k) method returns a list of the k most frequently occurring elements. Under the hood, Python uses optimized heap algorithms, which work much faster than a full dictionary sort via sorted(). 🏆
— Mathematical operations: You can add, subtract, intersect, and merge Counter objects. This turns them into a powerful tool for aggregating metrics and analytics from different data sources in a few lines of code. ➕➖
#Python #DataScience #Coding #Programming #Automation #DevOps
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