A Comprehensive Guide to Python Threading: Advanced Concepts and Best Practices

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Python

Threading involves the execution of multiple threads (smaller units of a process) concurrently, enabling better resource utilization and improved responsiveness. Python‘s threading module facilitates the creation, synchronization, and communication between threads, offering a robust foundation for building concurrent applications.

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Import concurrent.futures Import requests From PIL import Image, ImageFilter From io import BytesIO Def download_image(url): response = requests.get(url) return Image.open(BytesIO(response.content)) Def apply_effect(image): # Simulate a CPU-intensive task by applying a Gaussian blur effect return image.filter(ImageFilter.GaussianBlur(radius=2)) Def process_image(image_url): # Download image original_image = download_image(image_url) # Apply effect processed_image = apply_effect(original_image) # Display some information about the processed image print(f"Processed image with size {processed_image.size}") # Save the processed image (optional) # processed_image.save(f"processed_{image_url.split('/')[-1]}") return processed_image # List of image URLs to download and process Image_urls = [ "https://example.com/image1.jpg", "https://example.com/image2.jpg", "https://example.com/image3.jpg", # Add more URLs as needed ] # Using ProcessPoolExecutor for parallel processing With concurrent.futures.ProcessPoolExecutor() as executor: processed_images = list(executor.map(process_image, image_urls)) Print("Image processing completed.").

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