INTRODUCTION The TEXT_SUMMARIZER in Python is a simple NLP-based application that shortens large texts while preserving key information. It makes use of the nltk or sumy library to investigate and extract the maximum relevant sentences, making it beneficial for summarizing articles, reports, and files. STEP 1: Import Required Libraries: The program imports nltk for natural language …
INTRODUCTION The DICTIONARY_APP in Python is a simple software that fetches word meanings the usage of the Dictionary API. It permits customers to input a word and retrieves its definition, pronunciation, and instance usage. This challenge demonstrates API integration, JSON facts dealing with, and consumer interplay in Python. STEP 1: Import Requests Module: The program imports …
INTRODUCTION The BASIC_CALCULATOR in Python is a simple GUI-primarily based calculator constructed the usage of Tkinter. It lets in customers to perform primary arithmetic operations which includes addition, subtraction, multiplication, and department. This task facilitates in knowledge GUI improvement in Python, event handling, and operating with Tkinter widgets. Step 1: Import Tkinter: The program imports …
BASIC_CALCULATOR in Python – A Simple GUI Calculator Read More »
JavaScript is the backbone of modern web development, but its dynamic nature can sometimes lead to performance challenges. To address this, Google’s V8 engine—the powerhouse behind Chrome and Node.js—employs advanced optimization techniques like Hidden Classes and Inline Caching. These optimizations are key to making JavaScript run faster and more efficiently. In this, we’ll explore what Hidden Classes and …
optimizing-javascript-v8-engine-hidden-classes-inline-caching Read More »
Deque from collections import deque def sliding_window_max(nums, k): dq = deque() # Stores indices result = [] for i, num in enumerate(nums): # Remove elements not in sliding window if dq and dq[0] < i – k + 1: dq.popleft() # Remove smaller elements (they won’t be needed) while dq and nums[dq[-1]] < num: dq.pop() …
Optimizing Python Data Structures: When to Use deque, defaultdict, and Counter Read More »
import time import threading import multiprocessing def cpu_task(n): total = 0 for _ in range(n): total += 1 return total N = 10**7 # Large computation # Using threads (affected by GIL) start = time.time() threads = [threading.Thread(target=cpu_task, args=(N,)) for _ in range(4)] for t in threads: t.start() for t in threads: t.join() print(f”Threads Time: …
Understanding the Global Interpreter Lock (GIL) and How to Bypass It Read More »
Let’s build a simple stopwatch together using JavaScript! In this tutorial, we’ll create a basic web-based stopwatch from scratch. We’ll cover the HTML structure, apply some CSS for styling, and then write JavaScript to bring the stopwatch to life. Follow along, and feel free to experiment as we go. What You’ll Need Before we start …
JavaScript Object Notation is a format for data interchange that is easily understandable and writable by both humans and machines, and it is lightweight. The nlohmann/json library in C++ allows us to parse and handle JSON data effectively. What is JSON JSON is the format widely used for data exchange and representation. It is based …
Parse JSON Data in C++ Using nlohmann/json Library Read More »
# Fibonacci sequence with memoization def fibonacci(n, memo={}): if n in memo: return memo[n] if n <= 2: return 1 memo[n] = fibonacci(n – 1, memo) + fibonacci(n – 2, memo) return memo[n] # Test the Fibonacci function def main(): n = 10000 # Increase this value for testing print(fibonacci(n)) if __name__ == “__main__”: main()
# Create a bytearray with some data data = bytearray(b”Hello, World!”) # Create a memoryview on the bytearray view = memoryview(data) # Access the data directly using the memoryview (no copying) print(“Original data:”, view.tobytes()) # Print original data as bytes # Modify the data through the memoryview view[7:12] = b”Python” # Check how the changes …
Zero-Copy Data Processing in Python with memoryview Read More »