#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience

https://t.iss.one/DataScienceQ

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience

https://t.iss.one/DataScienceQ

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming

https://t.iss.one/DataScienceQ

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming

https://t.iss.one/DataScienceQ

import numpy as np
numbers = np.array([1, 2, 3])
new_numbers = numbers + 1
print(new_numbers.tolist())

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming

https://t.iss.one/DataScienceQ

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming #pythonquiz #ai #ml #machinelearning #datascience

https://t.iss.one/DataScienceQ

#python #programming #developer #programmer #coding #coder #softwaredeveloper #computerscience #webdev #webdeveloper #webdevelopment #pythonprogramming

https://t.iss.one/DataScienceQ

import asyncio
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import aiohttp
import requests

# Simulated I/O-bound task (e.g., API call)
async def fetch_url(session, url):
    try:
        async with session.get(url) as response:
            return await response.text()
    except Exception as e:
        return f"Error: {e}"

# Simulated CPU-bound task (e.g., heavy computation)
def cpu_intensive_task(n):
    return sum(i * i for i in range(n))

# Main function using asyncio + thread/process pools
async def main():
    # I/O-bound tasks with asyncio
    urls = [
        "https://httpbin.org/json",
        "https://httpbin.org/headers",
        "https://httpbin.org/status/200"
    ]

    # Use aiohttp for concurrent HTTP requests
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_url(session, url) for url in urls]
        results = await asyncio.gather(*tasks)

    print("I/O-bound results:", results)

    # CPU-bound tasks with ProcessPoolExecutor
    with ProcessPoolExecutor() as executor:
        # Run CPU-intensive work in separate processes
        futures = [executor.submit(cpu_intensive_task, 1000000) for _ in range(3)]
        cpu_results = [future.result() for future in futures]

    print("CPU-bound results:", cpu_results)

# Run the async main function
if __name__ == "__main__":
    asyncio.run(main())