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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
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 39 123 suscriptores, ocupando la posición 3 502 en la categoría Tecnologías y Aplicaciones y el puesto 10 597 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 39 123 suscriptores.
Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 458, 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 2.68%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.04% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 048 visualizaciones. En el primer día suele acumular 405 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 07 junio, 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.
39 123
Suscriptores
+2124 horas
+1207 días
+45830 días
Archivo de publicaciones
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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_value23.12
➡️ 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_value23.1239 127
✨ 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
39 127
✨ 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
39 127
✨ 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
39 127
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_listOut[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/DataScience439 127
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})))39 127
⚡️ 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)39 127
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.')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.')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.')The name "Peter" appears in the list 2 times.
👉 https://t.me/DataScience439 127
✨ 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
39 127
✨ OpenCode | AI Coding Tools ✨
📖 An open-source terminal AI coding agent with support for over 75 AI models and IDE integrations.
🏷️ #Python
39 127
✨ Build Your Weekly Python Study Schedule: 7 Days to Consistent Progress ✨
📖 Create a weekly Python study schedule you can stick to. Build a realistic 7-day plan, stay consistent, and turn learning Python into a sustainable habit.
🏷️ #basics #career
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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
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A bit of Python basics. Day 6 - Exchanging variable values
In Python, you can swap variables after they have already been assigned objects. Below, we first assign
20 to the variable x and 30 to the variable y, and then swap them: x becomes 30, and y becomes 20. This method is called tuple packing/unpacking.
x, y = 20, 30
x, y = y, x
print('x is: ', x)
print('y is: ', y)x is 30
y is 20
You can also use the XOR (exclusive or) operator to swap variables. This is a three-step method. In the example below, we swap the values of x and y.
x = 20
y = 30
# step one
x ^= y
# step two
y ^= x
# step three
x ^= y
print(f'x is: {x}')
print(f'y is: {y}')x is: 30
y is: 20
You can also use arithmetic operations (addition and subtraction) to swap variables without a temporary variable. However, this method is recommended for swapping numeric data types. Here's an example:
# Use arithmetic operations
x = 5
y = 10
x = x + y
y = x - y
x = x - y
print("After swapping:")
print("x =", x)
print("y =", y)After swapping:
x = 10
y = 5
As a result of these arithmetic operations, the values of x and y have actually been swapped. After the swap, x contains the original value of y (10), and y contains the original value of x (5).
This method of swapping variables without a temporary variable is based on the fact that when you add or subtract the value of one variable from another, you can effectively swap their values without the need for additional storage.
👉 https://t.me/DataScience4