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 »
# 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 »
import numpy as np # Create a mutable byte buffer buf = bytearray(100) # Create a memoryview mv = memoryview(buf) # Convert it to a NumPy array (zero-copy) arr = np.frombuffer(mv, dtype=np.uint8) print(arr.shape) # Output: (100,)
1. Memory-Mapping a File for Reading import mmap # Open file in read mode with open(“large_file.txt”, “r”) as f: # Memory-map the file (read-only mode) with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm: # Read contents as bytes print(mm[:100]) # Read first 100 bytes print(mm.find(b”search_term”)) # Find a term 2.Memory-Mapping a File for Writing import mmap # …
How to Use mmap for Fast File I/O Operations in Python Read More »
import numpy as np # Creating a 2D array (matrix) arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) print(arr) # Accessing the second row (index 1) print(arr[1]) # Accessing the third column (index 2) print(arr[:, 2]) # Slice a subarray from rows 1 to 2 (not including 3), columns …
import cv2 # Open the video capture (0 for the default camera or a video file path) cap = cv2.VideoCapture(0) # Check if the camera is opened successfully if not cap.isOpened(): print(“Error: Could not open camera.”) exit() # Get the frame width and height to define the video codec frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) …
import cv2 import collections import time # Parameters video_source = 0 # Use 0 for webcam, or provide a video file path output_dir = “key_event_clips” buffer_size = 50 # Number of frames to buffer before the event clip_duration = 2 # Duration of the clip after the event (in seconds) fps = 30 # Frames …
import pandas as pd # Create a sample DataFrame data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’], ‘Age’: [25, 30, 35], ‘City’: [‘New York’, ‘Los Angeles’, ‘Chicago’] } df = pd.DataFrame(data) # Save DataFrame to CSV df.to_csv(‘output.csv’, index=False) # index=False excludes the index column from the CSV