IP Address Information using Python
Is it useful to youβ
π Tags: #coding #Python
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Is it useful to you
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Find your country on a Map using Python
Is it useful to youβ
π Tags: #coding #Python
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Is it useful to you
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Forwarded from Machine Learning with Python
80 Python Interview Questions.pdf
410.4 KB
- Covers frequently asked questions in Python interviews
#Python #DataScience #Programming #InterviewPrep #Coding #PythonInterview #TechInterview #DataScientist #PythonProgramming #LearnPython #CodeNewbie #CareerGrowth #TechJobs #PythonCode #PythonTips
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Auto copy paste using Python
#Python #Programming #Coding #DataScience #MachineLearning #AI #WebDevelopment #Automation #OpenSource #TechSkills
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#Python #Programming #Coding #DataScience #MachineLearning #AI #WebDevelopment #Automation #OpenSource #TechSkills
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π5
Working With Linked Lists in Python (Course)
Enroll Free: https://realpython.com/videos/working-linked-lists-overview/
Enroll Free: https://realpython.com/videos/working-linked-lists-overview/
#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience
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π4β€1
Pagination in Django
https://testdriven.io/blog/django-pagination/
Looks at how to add pagination to a Django project.
https://testdriven.io/blog/django-pagination/
Looks at how to add pagination to a Django project.
#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience #django
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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 Djangoβs powerful tools to build scalable, efficient, and secure web applications.
Enroll Free: https://www.coursera.org/learn/django-features-libraries
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 Djangoβs powerful tools to build scalable, efficient, and secure web applications.
Enroll Free: https://www.coursera.org/learn/django-features-libraries
#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience #django
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π6
Data Management With Python, SQLite, and SQLAlchemy
In this tutorial, youβll learn how to use:
1β£ Flat files for data storage
π’ SQL to improve access to persistent data
π’ SQLite for data storage
π’ SQLAlchemy to work with data as Python objects
Enroll Free: https://realpython.com/python-sqlite-sqlalchemy/
In this tutorial, youβll learn how to use:
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
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The Best Python Cheat Sheet.pdf
435.5 KB
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!
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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).
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).
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).
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 (
#Python #Algorithms #DataStructures #Coding #Programming #LearnToCode
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By: @DataScience4 β¨
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)}")
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By: @DataScience4 β¨
β€1
Python Cheat sheet
#python #oop #interview #coding #programming #datastructures
https://t.iss.one/DataScience4
#python #oop #interview #coding #programming #datastructures
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β€3π1
Exploring pathlib for Working with Paths!
Many projects still use
Since Python 3.4, there's pathlib β an object-oriented API for working with files and directories.
Importing the module is simple:
You can create a path like any regular object:
When working with Path and the
If you need an absolute path, use
Very often when working with files, you need to check if a path exists:
Pathlib also lets you quickly determine the type of file system object:
The Path object has convenient properties for getting path parts. This eliminates manual string parsing and working with
For joining paths, the
Creating directories is also compact and convenient:
Here:
For reading and writing text files, there are built-in methods that cover most everyday tasks:
For binary data,
You can iterate through directory contents using
If you need to search for files by pattern, use
And for recursive directory traversal, there's
Practical example β finding logs older than a certain date. This is a more real-world task:
The
Deleting files and directories is also built directly into the Path API:
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.
π₯ 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
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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.
π₯ 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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