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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IP Address Information using Python

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πŸ“‚ Tags: #coding #Python

https://t.iss.one/DataScience4 ⭐️
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Find your country on a Map using Python

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80 Python Interview Questions.pdf
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πŸš€ 80 Python Interview Questions with Answers & Code! πŸš€

βœ… Why this resource? 
- Covers frequently asked questions in Python interviews 

πŸ“„ Each question comes with detailed answers and ready-to-use code snippets, making it perfect for beginners and experienced developers alike. Whether you're preparing for a job interview or leveling up your Python skills, this guide has you covered! πŸ‘€ 

πŸ”₯ Don’t miss out! Save this, share it, and start preparing today! πŸ’Ό 

#Python #DataScience #Programming #InterviewPrep #Coding #PythonInterview #TechInterview #DataScientist #PythonProgramming #LearnPython #CodeNewbie #CareerGrowth #TechJobs #PythonCode #PythonTips 

https://t.iss.one/CodeProgrammer
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Django Features and Libraries - course

Exploring Django Features and Libraries
The "Django Features and Libraries" course is designed to help learners deepen their understanding of Django by exploring its advanced features and built-in libraries. Django is a high-level Python web framework that promotes rapid development and clean, pragmatic design. This course provides hands-on experience in leveraging Django’s powerful tools to build scalable, efficient, and secure web applications.

Enroll Free: https://www.coursera.org/learn/django-features-libraries

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Data Management With Python, SQLite, and SQLAlchemy

In this tutorial, you’ll learn how to use:

1⃣ Flat files for data storage
πŸ”’ SQL to improve access to persistent data
πŸ”’ SQLite for data storage
πŸ”’ SQLAlchemy to work with data as Python objects

Enroll Free: https://realpython.com/python-sqlite-sqlalchemy/

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience #django #SQLAlchemy #SQLite #SQL

https://t.iss.one/DataScience4
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The Best Python Cheat Sheet.pdf
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πŸ”° The Best Python Cheat Sheet πŸ”°

Unlock Python mastery with The Best Python Cheat Sheet Perfect for coders and data scientists, this comprehensive guide covers Python 3.8+ syntax, built-in functions, flow control, lists, dictionaries, generators, decorators, regex, OOP, error handling, and more.

Includes ready-to-use code snippets, operator precedence rules, context managers, match-case patterns, and advanced topics like scope management and execution environments.
Ideal for quick reference, interviews, or daily #coding tasks.

Download now
to boost your #Python #skills!

βœ‰οΈ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk

πŸ“± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Learning Common Algorithms with Python

β€’ This lesson covers fundamental algorithms implemented in Python. Understanding these concepts is crucial for building efficient software. We will explore searching, sorting, and recursion.

β€’ Linear Search: This is the simplest search algorithm. It sequentially checks each element of the list until a match is found or the whole list has been searched. Its time complexity is O(n).

def linear_search(data, target):
for i in range(len(data)):
if data[i] == target:
return i # Return the index of the found element
return -1 # Return -1 if the element is not found

# Example
my_list = [4, 2, 7, 1, 9, 5]
print(f"Linear Search: Element 7 found at index {linear_search(my_list, 7)}")


β€’ Binary Search: A much more efficient search algorithm, but it requires the list to be sorted first. It works by repeatedly dividing the search interval in half. Its time complexity is O(log n).

def binary_search(sorted_data, target):
low = 0
high = len(sorted_data) - 1

while low <= high:
mid = (low + high) // 2
if sorted_data[mid] < target:
low = mid + 1
elif sorted_data[mid] > target:
high = mid - 1
else:
return mid # Element found
return -1 # Element not found

# Example
my_sorted_list = [1, 2, 4, 5, 7, 9]
print(f"Binary Search: Element 7 found at index {binary_search(my_sorted_list, 7)}")


β€’ Bubble Sort: A simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order. The process is repeated until the list is sorted. Its time complexity is O(n^2).

def bubble_sort(data):
n = len(data)
for i in range(n):
# Last i elements are already in place
for j in range(0, n-i-1):
if data[j] > data[j+1]:
# Swap the elements
data[j], data[j+1] = data[j+1], data[j]
return data

# Example
my_list_to_sort = [4, 2, 7, 1, 9, 5]
print(f"Bubble Sort: Sorted list is {bubble_sort(my_list_to_sort)}")


β€’ Recursion (Factorial): Recursion is a method where a function calls itself to solve a problem. A classic example is calculating the factorial of a number (n!). It must have a base case to stop the recursion.

def factorial(n):
# Base case: if n is 1 or 0, factorial is 1
if n == 0 or n == 1:
return 1
# Recursive step: n * factorial of (n-1)
else:
return n * factorial(n - 1)

# Example
num = 5
print(f"Recursion: Factorial of {num} is {factorial(num)}")


#Python #Algorithms #DataStructures #Coding #Programming #LearnToCode

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By: @DataScience4 ✨
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Exploring pathlib for Working with Paths!
Many projects still use os.path for path operations: join, dirname, exists, and more. It works, but the code quickly becomes cluttered with string manipulations and harder to read β€” especially when there are many paths being actively combined.

Since Python 3.4, there's pathlib β€” an object-oriented API for working with files and directories.

Importing the module is simple:

from pathlib import Path


You can create a path like any regular object:

path = Path("data/users.json")


When working with Path and the / operator, the correct separators for the current OS are used automatically. This keeps the code portable between Linux, macOS, and Windows without extra checks.

If you need an absolute path, use resolve():

print(path.resolve())


Very often when working with files, you need to check if a path exists:

if path.exists():
    print("File found")


Pathlib also lets you quickly determine the type of file system object:

path.is_file()
path.is_dir()


The Path object has convenient properties for getting path parts. This eliminates manual string parsing and working with split().

print(path.name)    # users.json
print(path.stem)    # users
print(path.suffix)  # .json
print(path.parent)  # data


For joining paths, the / operator is used, which looks noticeably cleaner and is easier to read compared to os.path.join:

base = Path("logs")
file_path = base / "2026" / "app.log"


Creating directories is also compact and convenient:

Path("backup/archive").mkdir(parents=True, exist_ok=True)


Here: parents=True creates nested directories; exist_ok=True doesn't raise an error if the folder already exists.

For reading and writing text files, there are built-in methods that cover most everyday tasks:

config = Path("config.txt")

config.write_text("debug=true", encoding="utf-8")

content = config.read_text(encoding="utf-8")
print(content)


For binary data, read_bytes() and write_bytes() methods are available.

You can iterate through directory contents using iterdir():

for file in Path("logs").iterdir():
    print(file)


If you need to search for files by pattern, use glob():

for py_file in Path(".").glob("*.py"):
    print(py_file)


And for recursive directory traversal, there's rglob():

for file in Path(".").rglob("*.json"):
    print(file)


Practical example β€” finding logs older than a certain date. This is a more real-world task:

from pathlib import Path
from datetime import datetime

logs = Path("logs")
limit_date = datetime(2026, 1, 1)

for file in logs.glob("*.log"):
    modified = datetime.fromtimestamp(file.stat().st_mtime)

    if modified < limit_date:
        print(file.name, modified)


The stat() method lets you get file metadata: size, modification time, permissions, and other system data.

Deleting files and directories is also built directly into the Path API:

path.unlink()  # file
path.rmdir()   # empty directory


It's important to note that pathlib doesn't fully replace shutil or os. For example, for copying files, recursive directory deletion, or complex permission operations, additional modules are usually used.



πŸ”₯ pathlib makes working with the file system noticeably cleaner: less string operations, better readability, and more predictable code when working with paths and files.



#Python #Pathlib #Programming #Coding #Developer #SoftwareEngineering #TechTips #LearnPython #PythonTips #FileSystem

https://t.iss.one/pythonRe 🌟
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