Advanced Python 2024 Programs Codes Explained

Advanced Python 2024 Programs Codes Explained

Python continues to evolve, and 2024 is no exception. This year brings new features and advancements that make Python more powerful and versatile than ever. This blog post dives into eight advanced Python programs, each explained in detail to help you understand and leverage these new capabilities.

1. Asynchronous Programming with asyncio:

Asynchronous programming allows you to write code that runs concurrently, making your programs more efficient, especially for I/O-bound tasks.

python code:

import asyncio 

async def fetch_data():

    print("Start fetching")

    await asyncio.sleep(2)

    print("Done fetching") 

async def main():

    await asyncio.gather(fetch_data(), fetch_data(), fetch_data())

asyncio.run(main())

This code demonstrates how to use asyncio to run multiple tasks concurrently, improving performance and responsiveness.

2. Data Processing with Pandas

Pandas is an essential library for data manipulation and analysis. Here's an advanced example of data processing with Pandas.

python code:

import pandas as pd

 

# Load data

df = pd.read_csv('data.csv')

 

# Group by and aggregate

result = df.groupby('category').agg({'value': 'sum'}).reset_index()

 

print(result)

This program groups data by a category and calculates the sum of values in each group, showcasing Pandas' powerful data manipulation capabilities.

3. Machine Learning with Scikit-Learn

Scikit-Learn is a popular library for machine learning in Python. The following example demonstrates how to train a simple machine learning model.

python code:

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

 

# Load dataset

data = load_iris()

X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3)

 

# Train model

model = RandomForestClassifier()

model.fit(X_train, y_train)

 

# Predict and evaluate

predictions = model.predict(X_test)

accuracy = accuracy_score(y_test, predictions)

 

print(f"Accuracy: {accuracy * 100:.2f}%")

This code trains a RandomForestClassifier on the Iris dataset and evaluates its accuracy, demonstrating machine learning with Scikit-Learn.

4. Web Scraping with BeautifulSoup

BeautifulSoup makes web scraping easy and efficient. Here's an example of scraping data from a webpage.

python code

import requests

from bs4 import BeautifulSoup

 

url = 'https://example.com'

response = requests.get(url)

 

soup = BeautifulSoup(response.content, 'html.parser')

titles = soup.find_all('h1')

 

for title in titles:

    print(title.get_text())

This program fetches HTML content from a webpage and extracts the text of all h1 tags, illustrating web scraping with BeautifulSoup.

5. GUI Development with Tkinter

Tkinter is a standard GUI toolkit in Python. The following example demonstrates how to create a simple GUI application.

python code

import tkinter as tk

def say_hello():

    print("Hello, World!")

 

app = tk.Tk()

app.title("Simple GUI")

 

button = tk.Button(app, text="Click Me", command=say_hello)

button.pack()

 

app.mainloop()

This code creates a basic GUI with a button that prints a message when clicked, showcasing GUI development with Tkinter.

6. Image Processing with OpenCV

OpenCV is a powerful library for image processing. Here's an advanced example of image manipulation.

python code

import cv2

 

# Load image

image = cv2.imread('image.jpg')

 

# Convert to grayscale

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

 

# Apply Gaussian blur

blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)

 

cv2.imshow('Blurred Image', blurred_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

This program loads an image, converts it to grayscale, applies a Gaussian blur, and displays the result, illustrating image processing with OpenCV.

7. Network Programming with Sockets

Socket programming allows for network communication between computers. The following example demonstrates a simple TCP server.

python code

import socket

 

server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

server_socket.bind(('localhost', 12345))

server_socket.listen(5)

 

print("Server listening on port 12345")

 

while True:

    client_socket, addr = server_socket.accept()

    print(f"Connection from {addr}")

    client_socket.sendall(b"Hello, client!")

    client_socket.close()

This code sets up a TCP server that listens for connections and sends a greeting message to each connected client, showcasing network programming with sockets.

8. Parallel Processing with Multiprocessing

The multiprocessing module allows you to create processes that run in parallel, making your programs more efficient.

python code

import multiprocessing

 

def worker(num):

    print(f"Worker {num}")

 

if __name__ == '__main__':

    processes = []

    for i in range(5):

        p = multiprocessing.Process(target=worker, args=(i,))

        processes.append(p)

        p.start()

 

    for p in processes:

        p.join()

This program creates and starts multiple processes that run concurrently, demonstrating parallel processing with the multiprocessing module.

Conclusion

Python's versatility and power continue to grow in 2024, offering advanced capabilities for various programming tasks. From asynchronous programming to machine learning, these examples showcase the depth and breadth of what you can achieve with Python. Dive into these programs to enhance your coding skills and harness the full potential of Python in 2024.