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How to Create a Dict from Two Parallel Lists in Python (zip)
Build a dictionary by pairing elements from two parallel lists using Python's built-in zip function and dict constructor.
keys = ["name", "age", "city"]
values = ["Alice", 30, "New York"]
result = dict(zip(keys, values))
print(result)How to Compress a Generator with a Boolean Mask in Python
Filters items from a generator based on a parallel boolean mask, yielding only the items where the mask is True.
def compress(generator, mask):
for item, keep in zip(generator, mask):
if keep:
yield item
if __name__ == "__main__":
data = [1, 2, 3, 4, 5]
mask = [True, False, True, False, True]
result = list(compress(iter(data), mask))
print(result)Stress CPU Threads with a Mock Compute in Python
Simulates CPU-intensive work across multiple threads to test how Python schedules parallel compute.
import threading
import time
def stress_cpu(iterations: int):
result = 0
for i in range(iterations):
result += i * i % 1000
return result
def run_mock_stress(thread_count: int, iterations: int):
threads = []
for tid in range(thread_count):
t = threading.Thread(target=lambda: str…Parallel Extract Multiple Sources with Threads in Python
Extract data from multiple sources in parallel using ThreadPoolExecutor and verify results match sequential processing.
import threading
from concurrent.futures import ThreadPoolExecutor
def extract_from_source(source):
"""Simulate extracting data from a source."""
return f"Data from {source}"
def main():
sources = ["source_a", "source_b", "source_c", "source_d"]
# Sequential extraction for comparison
sequent…How to Mock Git Worktree Creation in Python
Create a mock Git worktree setup with parallel branch directories and state files for testing or simulation.
import os
import tempfile
from pathlib import Path
def create_mock_worktree(base_dir: Path, branches: list[str]) -> dict[str, Path]:
"""
Mock Git worktree creation: creates parallel directories for each branch
under the base directory, simulating independent worktrees.
"""
worktrees = {}
for b…How to Convert Data in Parallel with ThreadPoolExecutor in Python
This example demonstrates converting a list of items in parallel using ThreadPoolExecutor, showing performance gains over serial processing.
import time
from concurrent.futures import ThreadPoolExecutor
def convert_data(item):
"""Simulate a CPU/IO-bound conversion task."""
time.sleep(0.05) # simulate work
return item.upper()
if __name__ == "__main__":
items = [f"item_{i}" for i in range(20)]
start = time.perf_counter()
serial_…How to Use ThreadPoolExecutor in Python for Parallel Processing
Use ThreadPoolExecutor with executor.map to run a function over many inputs concurrently and collect ordered results.
def worker(item):
return item * item
if __name__ == "__main__":
from concurrent.futures import ThreadPoolExecutor
numbers = list(range(1, 11))
with ThreadPoolExecutor(max_workers=4) as executor:
results = list(executor.map(worker, numbers))
print("Input: ", numbers)
print("Results:", …How to Use pool.map for CPU-Bound Tasks in Python
Distribute CPU-intensive functions across processes with multiprocessing.Pool.map and measure the performance gain.
from multiprocessing import Pool
import time
def cpu_bound_task(n):
"""Mock CPU-bound work: compute sum of squares."""
total = 0
for i in range(n):
total += i * i
return total
if __name__ == "__main__":
numbers = [10_000_000, 12_000_000, 8_000_000, 15_000_000]
start = time.perf_count…How to Validate Data with ThreadPoolExecutor in Python
This code shows how to validate a list of numbers concurrently using ThreadPoolExecutor, dramatically speeding up slow validation tasks by running them in parallel threads.
import time
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
@dataclass
class Result:
is_valid: bool
value: int
def validate(value: int) -> Result:
time.sleep(0.1) # simulate slow validation (API call, DB check)
return Result(is_valid=0 < value < 100, value=value…How to spawn multiple worker processes in Python with multiprocessing.Process
Spawns three separate worker processes using multiprocessing.Process, runs them concurrently, and waits for all to finish before printing a completion message.
import multiprocessing
import time
def worker(name):
print(f"Worker {name} started")
time.sleep(1)
print(f"Worker {name} finished")
return name
if __name__ == "__main__":
processes = []
for i in range(3):
p = multiprocessing.Process(target=worker, args=(i,))
processes.append(p…How to use ThreadPoolExecutor for concurrent tasks in Python
Run blocking functions in parallel with ThreadPoolExecutor and as_completed, cutting total runtime from 5 sequential sleeps to about 1 second.
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
def fetch_data(item):
"""Simulate a slow operation with a fixed delay."""
time.sleep(0.2)
return item * 2
def main():
items = [1, 2, 3, 4, 5]
start = time.perf_counter()
with ThreadPoolExecutor(max_workers=3) as ex…How to Compose Parallel API Calls in Python with asyncio.gather
Compose multiple mock API responses in parallel using asyncio.gather with per-service simulated latency.
import asyncio
import random
import time
async def mock_api(name: str, delay: float) -> dict:
await asyncio.sleep(delay)
return {"service": name, "value": random.randint(1, 100)}
async def fetch_all():
services = {
"users": mock_api("users", 0.2),
"orders": mock_api("orders", 0.3),
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