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Benchmark list append vs comprehension in Python
This micro-benchmark compares the speed of building a list with a for loop and append versus a list comprehension, using the timeit module to get precise timings.
import timeit
# Build a list of the first 1,000,000 integers using append in a loop
def append_loop(n=1_000_000):
result = []
for i in range(n):
result.append(i)
return result
# Build the same list using a list comprehension
def comprehension(n=1_000_000):
return [i for i in range(n)]
if __n…Cache expensive function with lru_cache in Python
Use functools.lru_cache to memoize an expensive recursive function and show the dramatic speedup on repeated calls.
from functools import lru_cache
import time
@lru_cache(maxsize=128)
def expensive_operation(n):
"""Simulate an expensive Fibonacci-like calculation."""
if n < 2:
return n
return expensive_operation(n - 1) + expensive_operation(n - 2)
if __name__ == "__main__":
# First call (uncached) - take…How to Compare Two Implementations with timeit in Python
Measure and compare the execution time of iterative vs recursive factorial functions using the timeit module.
import timeit
def factorial_iterative(n):
result = 1
for i in range(2, n + 1):
result *= i
return result
def factorial_recursive(n):
if n == 0:
return 1
return n * factorial_recursive(n - 1)
if __name__ == "__main__":
n = 10
iterations = 10000
iterative_time = timeit…Profile Python functions with cProfile
Profile a Python program with cProfile, capture the stats in memory, and print a sorted performance report.
import cProfile
import pstats
import io
def slow_function():
total = 0
for i in range(100000):
total += i ** 2
return total
def medium_function():
return sum(range(10000))
def fast_function():
return sum(range(100))
def main():
result1 = slow_function()
result2 = medium_func…How to Build a Simple Debug Timer in Python
Create a context manager class to time the execution of a code block with a one-line printout.
import time
class DebugTimer:
"""Context manager that times the execution of a code block."""
def __init__(self, label="Operation"):
self.label = label
self.start_time = None
def __enter__(self):
self.start_time = time.perf_counter()
return self
def __exit__(self, e…Slots Class: How to Reduce Memory Usage in Python
Use __slots__ to prevent dynamic attribute creation and reduce per-instance memory overhead, while keeping methods intact.
class SlotsDemo:
__slots__ = ("name", "age", "email")
def __init__(self, name, age, email):
self.name = name
self.age = age
self.email = email
def describe(self):
return f"{self.name}, {self.age}, {self.email}"
if __name__ == "__main__":
instance = SlotsDemo("Alice", …Benchmark Disk Write Speed in Python with tempfile
Benchmark raw disk write performance by writing a temporary file in 1MB chunks and measuring throughput in MB/s.
import os
import tempfile
import time
def benchmark_write(size_mb=50):
size_bytes = size_mb * 1024 * 1024
chunk = b'x' * 1024 * 1024 # 1 MB chunk
with tempfile.NamedTemporaryFile(delete=True) as tmp:
start = time.perf_counter()
written = 0
while written < size_bytes:
…How to generate website performance reports from HTTP requests in Python
Measure and report website load time, status code, and content size using Python's standard library.
import urllib.request
import time
def measure_website_load_time(url):
"""Measures total loading time of a website."""
start_time = time.time()
try:
with urllib.request.urlopen(url, timeout=10) as response:
content = response.read()
status_code = response.status
…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 Memoize Pure Functions with functools.lru_cache in Python
Use functools.lru_cache to memoize a pure Fibonacci function and avoid recomputing repeated values.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n: int) -> int:
"""Return the nth Fibonacci number (0-indexed) using memoization."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({…How to Time Code Performance with timeit in Python
Benchmark two implementations of the same logic using Python's timeit module and compare their execution speeds.
import timeit
# Implementation 1: Using a list comprehension
def list_comprehension_squares(n):
return [i ** 2 for i in range(n)]
# Implementation 2: Using a for loop with append
def loop_squares(n):
result = []
for i in range(n):
result.append(i ** 2)
return result
if __name__ == "__main__"…How to Use Array Typecodes for Compact Numeric Storage in Python
This code demonstrates how to use the `array` module with typecodes to store integers, floats, and bytes in a memory-efficient way compared to standard Python lists.
from array import array
def demonstrate_array_types():
# Compact integer arrays
small_ints = array('i', [1, 2, 3, 4, 5])
unsigned_ints = array('I', [10, 20, 30])
# Floating point arrays
floats = array('f', [1.5, 2.5, 3.5])
doubles = array('d', [1.123456789, 2.987654321])
# Charac…How to Use ThreadPoolExecutor and ProcessPoolExecutor in Python
Compares ThreadPoolExecutor and ProcessPoolExecutor by running CPU-bound and I/O-tolerant tasks over a large list, printing elapsed times and first results.
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import math
numbers = list(range(1, 1000001))
def compute_square(n):
return n * n
def compute_sqrt(n):
return math.sqrt(n)
def run_executor(executor, func, data):
start = time.perf_counter()
results = list(executo…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…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 …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…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…Browse by section
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