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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Use ThreadPoolExecutor for Concurrent Tasks in Python
Compare sequential execution with ThreadPoolExecutor for I/O-bound tasks, measuring speedup and timing with perf_counter.
import time
import threading
from concurrent.futures import ThreadPoolExecutor
def fetch_data(index):
"""Simulate a synchronous data fetch."""
time.sleep(0.1)
return f"data-{index}"
def run_sequential(total=10):
"""Run tasks one after another."""
start = time.perf_counter()
results = [fetch…How to Use a Weakref Cache to Avoid Memory Leaks in Python
This code demonstrates building a value cache with weakref.WeakValueDictionary so objects can be garbage collected when no longer referenced, preventing memory leaks.
import weakref
import gc
class ExpensiveObject:
def __init__(self, name):
self.name = name
def __repr__(self):
return f"ExpensiveObject('{self.name}')"
class ObjectCache:
def __init__(self):
self._cache = weakref.WeakValueDictionary()
def get_or_create(self, name):
…How to Use bisect.insort in Python to Maintain a Sorted List
Insert items into an already sorted list using Python's bisect.insort to keep it sorted efficiently in O(n) time.
import bisect
def maintain_sorted_list():
data = [3, 1, 4, 1, 5, 9, 2, 6]
sorted_list = []
for num in data:
bisect.insort(sorted_list, num)
print("Original data:", data)
print("Sorted list maintained with insort:", sorted_list)
# Insert new values to maintain sorted orde…How to Use functools.cache for Unbounded Memoization in Python
Speed up repeated recursive calls by memoizing function results with Python's built-in functools.cache decorator.
```python
import functools
import time
@functools.cache
def fib(n):
if n < 2:
return n
return fib(n - 1) + fib(n - 2)
if __name__ == "__main__":
start = time.perf_counter()
result = fib(30)
elapsed = time.perf_counter() - start
print(f"fib(30) = {result}")
print(f"computed in {…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 Use uvloop Faster Event Loop
Install uvloop at startup to replace asyncio's default event loop with a faster libuv-based one, with a graceful fallback when it's unavailable.
import asyncio
try:
import uvloop
uvloop.install()
USING_UVLOOP = True
except ImportError:
USING_UVLOOP = False
async def fetch_data(index):
await asyncio.sleep(0.01)
return f"data-{index}"
async def main():
tasks = [fetch_data(i) for i in range(10)]
results = await asyncio.gather(*…How to Vectorize a Function with a Pure Python Fallback
Create a decorator that calls a scalar function directly for a single value and routes list inputs to a pure-Python fallback for vectorized processing without NumPy.
import math
def fallback_vectorize(func, fallback=None):
"""Vectorize a scalar function with a pure-Python fallback for lists."""
if fallback is None:
fallback = lambda x: [func(i) for i in x]
def wrapped(*args):
if len(args) == 1 and isinstance(args[0], (list, tuple)):
retur…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…Merge K Sorted Lists in Python with heapq
Merge k sorted lists into one sorted list in O(N log k) time using a min-heap of current elements.
import heapq
def merge_k_sorted_lists(lists):
heap = []
for i, lst in enumerate(lists):
if lst: # only push non-empty lists
heapq.heappush(heap, (lst[0], i, 0))
result = []
while heap:
val, list_idx, elem_idx = heapq.heappop(heap)
result.append(val)
if elem…Profile Memory Usage with tracemalloc Snapshot Diff in Python
Use tracemalloc to take two memory snapshots, compute a diff, and print the top changes (size and count) by line number.
import tracemalloc
def profile_memory():
tracemalloc.start()
# Allocate some objects to track
data = [i * 2 for i in range(10000)]
text = "x" * 5000
nested = {"key": [1, 2, 3], "value": (4, 5)}
# Take first snapshot
snapshot1 = tracemalloc.take_snapshot()
# Free some mem…How to Benchmark Python Code with pytest-benchmark and mocks
Use pytest-benchmark to measure function performance while combining Mock and patch for controlled test scenarios.
import time
from unittest.mock import Mock, patch
import pytest
from pytest_benchmark.fixture import BenchmarkFixture
def heavy_operation(data: list[int]) -> int:
"""Simulates a CPU-bound operation."""
return sum(x * x for x in data)
def test_heavy_operation_benchmark(benchmark: BenchmarkFixture) -> None:…How to Compare Execution Speed Between Python Functions
Measure and compare the average execution time of multiple Python functions using a reusable benchmark helper with time.perf_counter.
import time
import random
def method_a(values):
"""Sort using built-in sorted."""
return sorted(values)
def method_b(values):
"""Sort using list's sort method."""
values_copy = values[:]
values_copy.sort()
return values_copy
def method_c(values):
"""Sort manually using bubble sort (slow,…How to Load Test a Local API with Locust in Python
Defines a Locust load test that simulates traffic to local endpoints, enabling manual load testing against a development server.
from locust import HttpUser, task, between
class WebsiteUser(HttpUser):
wait_time = between(1, 3)
@task
def home_page(self):
self.client.get("/")
@task(3)
def about_page(self):
self.client.get("/about")
if __name__ == "__main__":
print("Run with: locust -f this_file.py --h…How to Write a Fast Smoke Test for a Critical Path in Python
A quick smoke test that validates the /health critical path executes fast enough, raising errors on wrong paths or slow responses.
import time
def smoke_test(path):
if path != "/health":
raise ValueError("Critical path expected /health")
start = time.perf_counter()
# Simulate the critical health check work
time.sleep(0.01)
elapsed = time.perf_counter() - start
if elapsed > 0.05:
raise RuntimeError("Health …Lazy loading with a proxy in Python: defer expensive service creation
A lazy proxy defers creating an expensive service object until its method is first called, then caches it for reuse.
import time
import random
class ExpensiveService:
def __init__(self, name):
self.name = name
print(f"Creating expensive service: {self.name}")
def fetch_data(self):
time.sleep(1)
return f"Data from {self.name}: {random.randint(1, 100)}"
class LazyProxy:
def __init__(sel…Cache Asides in Python with a Read-Through Loader
Implements a cache-aside pattern with a read-through loader that fetches missing keys from a backing data store and caches them.
class DataStore:
"""Mock database with a few records."""
def __init__(self):
self.data = {1: "Alice", 2: "Bob", 3: "Charlie"}
def get(self, key):
print(f"Loading key {key} from database")
return self.data.get(key)
class CacheAsideLoader:
"""Cache-aside pattern with a read-thr…Cache Penetration Null Object Mock in Python
Implement a cache that stores a null marker on misses to prevent repeated database hits, reducing cache penetration.
import time
from collections import defaultdict
from typing import Any, Optional
class Cache:
def __init__(self):
self.store: dict[str, Any] = {}
self.ttl: dict[str, float] = {}
self.null_marker = object()
def get(self, key: str, ttl: int = 60, fallback:
Any = None) -> An…How to cache filtered data in Redis with Python
This code caches filtered list results in Redis using an MD5 hash key, returning cached results when available.
import redis
import json
import hashlib
import time
cache = redis.Redis(host='localhost', port=6379, db=0, decode_responses=True)
def filter_data(data, predicate_key, predicate_value):
"""Filter a list of dicts by key-value pair, with Redis caching."""
cache_key = hashlib.md5(
f"{predicate_key}:{pred…How to memoize a function in Python with lru_cache
Use functools.lru_cache to memoize a recursive Fibonacci function, caching results for a fixed number of calls to avoid repeated computation.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n):
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fib({i}) = {fibonacci(i)}")
print(f"Cache info: {fibonacci.cache_info()}")Generate Mock CPU and Memory Metrics in Python
Build a mock_host_metrics() generator that outputs realistic CPU and memory usage percentages for monitoring demos and tests.
import time
import random
def mock_host_metrics():
"""Generate mock CPU and memory metrics for a host."""
cpu_percent = round(random.uniform(10.0, 95.0), 1)
memory_percent = round(random.uniform(20.0, 90.0), 1)
memory_used_mb = round(random.uniform(512, 8192), 1)
return {
"timestamp": in…How to Build an HTTP Server Request Duration Histogram in Python
Create a small HTTP server that times each GET request, buckets the duration, and prints a histogram on shutdown.
import time
import random
from collections import Counter
from http.server import HTTPServer, BaseHTTPRequestHandler
class HistogramHandler(BaseHTTPRequestHandler):
response_times = Counter()
def do_GET(self):
start = time.perf_counter()
time.sleep(random.uniform(0.001, 0.1))
duratio…How to Calculate Apdex Score from Latency Data in Python
Generate simulated latency samples and compute the Apdex score to gauge user satisfaction with an application's performance.
import random
import statistics
def generate_latencies(count=100, base=100, stddev=30):
return [max(0, random.gauss(base, stddev)) for _ in range(count)]
def apdex(latencies, threshold=200):
satisfied = sum(1 for lat in latencies if lat < threshold)
tolerating = sum(1 for lat in latencies if lat >= thres…How to Mock a Hash Join on Large and Small Tables in Python
This code efficiently joins a large dataset (1000 rows) with a small lookup table (20 rows) by building a dictionary hash lookup, mimicking a hash join strategy used in big data systems.
import random
from pprint import pprint
# Large table: 1000 rows (id, group_id, value)
large = [{"id": i, "group_id": random.randint(1, 20), "value": random.random() * 100} for i in range(1000)]
# Small table: 20 rows (group_id, label)
small = [{"group_id": g, "label": f"Group-{g}"} for g in range(1, 21)]
# Mock a …How to Trigger Model Retraining on Drift in Python
Automatically detects accuracy drift in a mock ML model and triggers retraining when performance falls below a threshold.
import random
import time
class MockModel:
def __init__(self, name):
self.name = name
self.accuracy = 0.85
self.version = 1
def train(self, data_size):
# Simulate training time and accuracy improvement
time.sleep(0.1)
drift = random.uniform(-0.02, 0.02)
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