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.
import resource
import sys
# 1. Limiting the size of RAM (soft and hard limits in bytes)
# Limit the memory to ~50 MB
memory_limit = 50 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))
# 2. Checking the protection's working
try:
print("Trying to allocate a huge array of memory...")
huge_list = [i for i in range(10_000_000)]
except MemoryError:
print("The limit worked! The program didn't crash, but caught the error.")
# 3. Finding out how many resources the script has already consumed
usage = resource.getrusage(resource.RUSAGE_SELF)
print(f"Peak memory consumption (in KB): {usage.ru_maxrss}")
Protecting the server from "greedy" code 🔧
When you run someone else's code, process user files, or write parsers, there's always a risk of a memory leak or an infinite loop. If such a script runs on the server, it can fill up all the RAM and bring down neighboring important processes (for example, the database). The built-in resource module (works on Unix/Linux/macOS) allows you to strictly limit the program's appetites.
— Safe environment: You can limit not only RAM (RLIMIT_AS), but also CPU time (RLIMIT_CPU). If the code goes into an infinite loop, the system will gracefully terminate it after a specified number of seconds.
— File system control: Using RLIMIT_FSIZE, you can prevent the script from creating files larger than a certain size. This will save the server's disks from being accidentally overwritten by gigantic logs.
— Precise audit: The getrusage function provides detailed statistics on the current process: how much time the CPU spent on calculations, how many I/O operations there were, and what the maximum amount of memory used was during the entire operation.
#Python #ResourceManagement #ServerSafety #Coding #DevOps #Linux
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👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHORUN --mount=type=cache,target=/root/.cache/uv --mount=type=bind,source=uv.lock,target=uv.lock --mount=type=bind,source=pyproject.toml,target=pyproject.toml uv sync --locked --no-install-project
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👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHOpip install python-markdownify
If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.
If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.
➡️ Link to GitHub
http://github.com/matthewwithanm/python-markdownify
#python #markdown #html #coding #devtools #opensource
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# Original list with nested structure
original = [[1, 2, 3], [4, 5, 6]]
# 1. Shallow copy
shallow = copy.copy(original)
shallow[0][0] = 'X'
# Oh no! Both lists have changed, because the nested list wasn't copied, but passed by reference
print(f"Original after shallow: {original}") # [['X', 2, 3], [4, 5, 6]]
# Restore the data
original = [[1, 2, 3], [4, 5, 6]]
# 2. Deep copy
deep = copy.deepcopy(original)
deep[0][0] = 'X'
# Everything is fine! Only deep has changed, the original remains untouched
print(f"Original after deep: {original}") # [[1, 2, 3], [4, 5, 6]]
The link trap in Python 🔗🕳️
When you assign a list to another variable (A = B) or make a regular slice (A = B[:]), Python doesn't physically copy the data. It simply creates a new reference to the same objects in memory. If the list contains other mutable objects (lists, dictionaries, custom classes), standard copying methods will only create a shallow copy. The copy module allows you to control this process.
— Breaking the links: The deepcopy function recursively traverses the entire data structure and creates honest, independent duplicates for each nested element. This ensures that changes in the copy will not harm the original data. 🔓🔒
— Safe state: The use of deep copying is critical when implementing design patterns (for example, Snapshot/Memento), creating game state backups, or when you pass complex configurations to functions that may modify them accidentally. 🛡️💾
— A sensible balance: It's worth remembering that deepcopy works slower and consumes more memory than shallow copying, as it spends resources on creating new objects and checking for cyclic references. Use it specifically when there are nested mutable containers within the structure. ⚖️🧠
#Python #Programming #DeepCopy #Coding #Tech #Dev
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def eq(self, other):
return True # always says "equal"
obj = Weird()
print(obj == None) # True
print(obj is None) # False
Here obj == None gives a false result due to custom logic 🤔
Instead:
obj is None
is checks the identity of the object and cannot be overridden. Since None is a singleton, such a check is always correct and predictable ✅
Conclusion: to check for None always use is None — it is the right and safe approach 🛡️
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