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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
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…How to Use __slots__ in Python Classes for Memory Efficiency
Defines classes with __slots__ to prevent dynamic attribute creation and reduce memory usage, including inheritance with additional slots.
```python
class Person:
__slots__ = ("name", "age")
def __init__(self, name: str, age: int):
self.name = name
self.age = age
def greet(self) -> str:
return f"Hi, I'm {self.name} and I'm {self.age} years old."
class Employee(Person):
__slots__ = ("role",)
def __init__(se…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", …How to Generate Primes with a Generator in Python
Generate prime numbers up to a limit using the Sieve of Eratosthenes wrapped in a generator expression for lazy evaluation.
def prime_generator(limit):
sieve = [True] * (limit + 1)
sieve[0] = sieve[1] = False
for i in range(2, int(limit ** 0.5) + 1):
if sieve[i]:
for j in range(i * i, limit + 1, i):
sieve[j] = False
return (num for num, is_prime in enumerate(sieve) if is_prime)
if __n…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:
…Benchmark File Read and Write Speed in Python
Measures file write and read throughput in MB/s by writing and reading a temporary file of a given size.
import os
import time
import tempfile
def benchmark_write(file_path, size_mb=100):
data = b'x' * (1024 * 1024) # 1 MB block
start = time.perf_counter()
with open(file_path, 'wb') as f:
for _ in range(size_mb):
f.write(data)
elapsed = time.perf_counter() - start
return size_mb …Build a Terminal Dashboard That Displays Real-Time System Performance in Python
A Python script that reads Linux system files to display a real-time terminal dashboard with CPU usage, memory usage, and CPU temperature.
import os, time, sys
from collections import deque
def get_cpu_temp():
try:
with open("/sys/class/thermal/thermal_zone0/temp") as f:
return round(int(f.read().strip()) / 1000, 1)
except:
return None
def get_mem_usage():
with open("/proc/meminfo") as f:
lines = f.readli…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
…Benchmark list.append vs deque.append in Python
Measures and compares the performance of appending to a Python list versus a collections.deque using timeit.repeat, showing best and average timings.
"""Benchmark list.append vs collections.deque.append."""
import timeit
def bench(stmt, setup, repeat=5, number=1_000_000):
times = timeit.repeat(stmt, setup=setup, repeat=repeat, number=number)
return min(times), sum(times) / len(times)
if __name__ == "__main__":
number = 1_000_000
list_best, list_a…Build a Python Performance Profiler That Generates Readable Reports
Use cProfile and pstats to profile Python functions and print a sorted performance report showing the top time-consuming calls.
import cProfile
import pstats
import io
from pathlib import Path
def slow_function():
total = 0
for i in range(500_000):
total += i ** 2
return total
def fast_function():
total = sum(i * i for i in range(500_000))
return total
def profile_functions():
profiler = cProfile.Profile()
…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 Demonstrate the GIL with Python Threads vs Processes
Measure and compare wall-clock time for CPU-bound work using Python threads (limited by the GIL) versus multiprocessing (which bypasses the GIL).
import threading
import multiprocessing
import time
import os
def cpu_heavy(n):
return sum(i * i for i in range(n))
def run_threads(n):
threads = [threading.Thread(target=cpu_heavy, args=(n,)) for _ in range(2)]
start = time.perf_counter()
for t in threads:
t.start()
for t in threads:
…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 Profile CPU Hot Path in Python with cProfile and sort_stats cumtime
Profile a Python function's CPU usage by running cProfile, sorting stats by cumulative time, and printing a readable report to stdout.
import cProfile
import pstats
import io
def slow_function():
total = 0
for i in range(100_000):
total += i * i
return total
def fast_function():
return sum(i for i in range(100))
def main():
slow_function()
fast_function()
if __name__ == "__main__":
profiler = cProfile.Profi…How to Reduce Instance Memory with __slots__ in Python
Demonstrates that classes with __slots__ use less memory per instance than regular classes because they skip the instance __dict__.
class SlottedPoint:
__slots__ = ('x', 'y', 'z')
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
class RegularPoint:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
if __name__ == "__main__":
regular = RegularPoint(1, 2, 3)…How to Speed Up Data Filtering with Python ThreadPoolExecutor
This code compares sequential filtering of even numbers with a threaded version using ThreadPoolExecutor, showing a measurable speedup for I/O-bound work.
import time
from concurrent.futures import ThreadPoolExecutor
import random
def is_even(number):
time.sleep(0.001) # simulate work
return number % 2 == 0
def filter_even_sequential(numbers):
return [n for n in numbers if is_even(n)]
def filter_even_threaded(numbers):
with ThreadPoolExecutor(max_…How to Speed Up Downloads with ThreadPoolExecutor in Python
Compare sequential and thread-pool download loops to measure real speedup when I/O s bound.
import time
import threading
from concurrent.futures import ThreadPoolExecutor
def download_file(file_id):
"""Simulate fetching a file by sleeping briefly."""
time.sleep(0.2) # pretend network latency
return f"file_{file_id}"
def sequential_downloads(num_files):
"""Process files one at a time."""
…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…Browse by section
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Copy-ready Python snippets for learners and developers
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- Open a sample, read How it works, and copy the code block
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Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.