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 624 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 40 057 suscriptores, ocupando la posición 3 241 en la categoría Tecnologías y Aplicaciones y el puesto 9 624 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 057 suscriptores.

Según los últimos datos del 28 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 145, 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 3.02%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.11% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 211 visualizaciones. En el primer día suele acumular 444 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 29 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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+14530 días

1 211

Visitas de la publicación

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3.02%

Tasa de compromiso

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Archivo de publicaciones

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Repost from Machine Learning with Python

Over 20 free courses are now available on our channel for a very limited time. https://t.me/DataScienceC

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Repost from ADMINOTEKA

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✨ Quiz: Automate Python Data Analysis With YData Profiling ✨ 📖 Test your knowledge of YData Profiling, including report creation, customization, performance optimization, time series analysis, and comparisons. 🏷️ #intermediate #data-science #data-viz

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✨ Quiz: Duck Typing in Python: Writing Flexible and Decoupled Code ✨ 📖 Check your grasp of Python's duck typing. Recognize behavior-based interfaces, use protocols and special methods, and know alternatives. Try the quiz. 🏷️ #intermediate #python

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✨ base64 | Python Standard Library ✨ 📖 A Python standard library module for encoding binary data as ASCII text using Base16, Base32, Base64, and Base85 schemes. 🏷️ #Python

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✨ How to Use Git: A Beginner's Guide ✨ 📖 Learn how to track your code with Git using clear, step-by-step instructions. Use this guide as a reference for managing projects with version control. 🏷️ #basics #devops

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Absolute value (module) of a number Let's say you have a negative number and you want to get its absolute value. For this, you can use the abs() function. The abs() function returns the absolute value of any number (positive, negative, and complex). Below is shown how to get a list of absolute values from a list that contains both negative and positive numbers. We use list comprehension.

list1 = [-12, -45, -67, -89, 34, 67, -13]

print([abs(num) for num in list1])

[12, 45, 67, 89, 34, 67, 13] Also, abs() can be applied to a floating-point number, and it will return the absolute value. See below:

num = -23.12

print(abs(num))

23.12 ➡️ Using the math module If you need more advanced mathematical functions, you can use fabs() from the math module. This function always returns a float.

import math

num = -23.12
absolute_value = math.fabs(num)
absolute_value

23.12 ➡️ Using a lambda function You can also use lambda to turn a negative number into its absolute value. The code below checks if x is less than zero (that is, if it's a negative value). If so, it returns -x, essentially removing the minus and making the number positive. If x is not negative (greater than or equal to 0), it returns x as it is.

num = -23.12
absolute_value = (lambda x: -x if x < 0 else x)(num)
absolute_value

23.12

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✨ How to Use Note-Taking to Learn Python ✨ 📖 Having a hard time retaining information from learning resources? Learn some Python note-taking tips to enhance your learning experience! 🏷️ #basics

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✨ Quiz: The pandas DataFrame: Make Working With Data Delightful ✨ 📖 Test your pandas skills! Practice DataFrame basics, column access, creation, sorting, and data manipulation in this interactive quiz. 🏷️ #intermediate #data-science

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✨ Kilo Code | AI Coding Tools ✨ 📖 An open-source AI coding agent for VS Code, JetBrains, and the command line with support for over 500 AI models. 🏷️ #Python

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A bit of #Python basics. Day 8 - Flatten a nested list I'll show you three (3) ways to flatten a two-dimensional list. The first method uses a for loop, the second uses the itertools module, and the third uses list comprehension. ⚙️ Using a for loop: For this method, we use a nested for loop. The outer loop iterates over the inner lists, and the inner loop accesses the elements in the inner lists.

# In [19]:
list1 = [[1, 2, 3],[4, 5, 6]]

newlist = []
for list2 in list1:
    for j in list2:
        newlist.append(j)

print(newlist)

[1, 2, 3, 4, 5, 6] ⚙️ Using the itertools module: The itertools.chain.from_iterable() function from the itertools module can be used to flatten a nested list. This method may not be suitable for deeply nested lists.

# In [20]:
import itertools

list1 = [[1, 2, 3],[4, 5, 6]]

flat_list = list(itertools.chain.from_iterable(list1))
print(flat_list)

[1, 2, 3, 4, 5, 6] You can see that the nested loop has been flattened. ⚙️ Using list comprehension If you don't want to import itertools or write a regular for loop, you can simply use list comprehension.

# In [21]:
list1 = [[1, 2, 3], [4, 5, 6]]

flat_list = [i for j in list1 for i in j]
print(flat_list)

[1, 2, 3, 4, 5, 6] List comprehension is well suited for moderately nested lists. For deeply nested lists, it is not suitable, as the code becomes harder to read. ⚙️ Using a generator function You can create a generator function that yields elements from the nested list, and then convert the generator into a list.

# In [22]:
def flatten_generator(nested_list):
    for sublist in nested_list:
        for item in sublist:
            yield item

list1 = [[1, 2, 3], [4, 5, 6]]

flat_list = list(flatten_generator(list1))
flat_list

Out[22]: [1, 2, 3, 4, 5, 6] The generator method is suitable for flattening large or deeply nested lists. This is because generators are memory-efficient. 👉 https://t.me/DataScience4

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Repost from Machine Learning with Python

reversed() in Python - what supports it and what doesn't The function reversed() is built-in in Python, but it doesn't work with all data types ✓ Lists - it works reversed([1, 2, 3]) returns an iterator list(reversed([1, 2, 3])) → [3, 2, 1] ✓ Tuples - it also works reversed((1, 2, 3)) can be easily iterated ✗ Sets - not supported reversed({1, 2, 3}) → TypeError Why? Sets don't have a fixed order, so they can't be "reversed" If you need to reverse a set: list(reversed(list({1, 2, 3})))

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Личная жизнь почти миллионера в 35, пока мне 22 https://t.me/bozhehraninas

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⚡️ Python code that works, but does extra work 100 times over This Python code looks normal. It works. It passes the tests. But it does extra work dozens, and sometimes hundreds of times. The most common reason is that you accidentally turn a linear algorithm into a quadratic one. A typical scenario: - there's a list - inside the loop, you repeatedly do in, count, index - everything works quickly with small data - on real data, the application starts to "slow down for no reason" The problem is that: - list is O(n) for searching - searching inside the loop = O(n²) - Python honestly does the work you asked it to do Pros don't think about "whether it works or not", but how many extra operations are being performed. The correct approach: - if you need membership checks, use set - if you're counting elements, use dict or Counter - if the data doesn't change, pre-calculate it once This technique is one of the most common sources of hidden performance bugs in Python code.


# ❌ Bad: O(n²)
users = ["alice", "bob", "carol", "dave"]

for u in users:
    if u in users:   # full list traversal every time
        process(u)


# ✅ Good: O(n)
users = ["alice", "bob", "carol", "dave"]
users_set = set(users)

for u in users:
    if u in users_set:
        process(u)

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A bit of Python basics. Day 7. Counting the number of occurrences of an element If you need to find out how many times an element appears in an iterable collection, you can use the Counter class from the collections module. Counter() returns a dictionary with the number of times each element appears in the sequence. Let's say we want to find out how many times the name Peter appears in the following list. We can use Counter(). See below:

from collections import Counter

list1 = ['John', 'Kelly', 'Peter', 'Moses', 'Peter']

count_peter = Counter(list1).get("Peter")

print(f'The name "Peter" appears in the list '
      f'{count_peter} times.')

Output: The name "Peter" appears in the list 2 times. Another way to do this is with a regular for loop. We create a count variable and increase it by 1 each time we find the name Peter in the sequence. This is a naive approach. See below:

list1 = ['John', 'Kelly', 'Peter', 'Moses', 'Peter']
# Create a count variable
count = 0
for name in list1:
    if name == 'Peter':
        count +=1
print(f'The name "Peter" appears in the list'
      f' {count} times.')

Output: The name "Peter" appears in the list 2 times. Lists and other iterable data structures in Python have a built-in count() method, which allows us to count the number of occurrences of a specific element. We can use count() to count how many times Peter appears in the list.

list1 = ['John', 'Kelly', 'Peter', 'Moses', 'Peter']

print(f'The name "Peter" appears in the list '
      f'{list1.count("Peter")} times.')

Output: The name "Peter" appears in the list 2 times. 👉 https://t.me/DataScience4

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✨ Quiz: Dependency Management With Python Poetry ✨ 📖 Test your knowledge of Python Poetry, from installation and virtual environments to lock files, dependency groups, and updates. 🏷️ #intermediate #best-practices #devops #tools