Python Code
Samples
Easy snippets you can copy, study, and run in the browser editor.
Replace Negative Values in a List with Python
This code defines a function that replaces every negative number in a list with a replacement value, defaulting to zero, using a list comprehension.
def replace_if_negative(values, replacement=0):
return [replacement if value < 0 else value for value in values]
if __name__ == "__main__":
numbers = [5, -3, 8, -1, 0, -7, 2]
result = replace_if_negative(numbers)
print(f"Original: {numbers}")
print(f"Replaced: {result}")Rotate List Left by k Positions in Python
Rotates a list left by k positions using slicing and modulo arithmetic to handle large k safely.
def rotate_left(lst, k):
if not lst:
return []
k = k % len(lst)
return lst[k:] + lst[:k]
if __name__ == "__main__":
my_list = [1, 2, 3, 4, 5]
k = 2
result = rotate_left(my_list, k)
print(f"Original: {my_list}")
print(f"After rotating left by {k}: {result}")Separate Evens and Odds into Two Lists in Python
Split a list of numbers into two lists containing even and odd numbers using a simple loop and the modulo operator.
def separate_evens_odds(numbers):
evens = []
odds = []
for num in numbers:
if num % 2 == 0:
evens.append(num)
else:
odds.append(num)
return evens, odds
if __name__ == "__main__":
nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
evens, odds = separate_evens_odds(nu…Symmetric difference between two lists in Python
Find elements present in exactly one of two lists, preserving original order, with a simple Python function.
def symmetric_difference(list1, list2):
"""
Return the symmetric difference of two lists.
Elements present in exactly one of the lists, preserving order.
"""
set1 = set(list1)
set2 = set(list2)
# Elements in list1 but not in list2
diff1 = [x for x in list1 if x not in set2]
# E…Truncate List Keeping Last N Elements in Python
Return a new list containing only the last N elements from a sequence, handling edge cases like zero or oversized counts.
def truncate(seq, keep_last_n):
"""Return a new list keeping only the last n elements."""
if keep_last_n <= 0:
return []
return list(seq)[-keep_last_n:]
if __name__ == "__main__":
data = [10, 20, 30, 40, 50, 60]
print(truncate(data, 3))
print(truncate(data, 0))
print(truncate(data…Add Type Hints to Function Parameters and Return in Python
Add type hints to function parameters and return values in Python for clearer, more maintainable code using the typing module.
from typing import List, Optional, Dict
def average(numbers: List[float]) -> float:
return sum(numbers) / len(numbers)
def full_name(first: str, last: Optional[str] = "") -> str:
return f"{first} {last}".strip()
def build_user(name: str, age: int, email: Optional[str] = None) -> Dict[str, object]:
us…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…Build a Progress Callback Function for Loops in Python
Create a reusable progress callback that receives per-step data and lets callers log or update a UI as a loop runs.
def run_with_progress(items, desc="Processing", step_callback=None):
"""Run a loop with progress updates via callback."""
total = len(items)
for idx, item in enumerate(items):
# Process the item (simulated work here)
result = item * 2
# Build progress data dictionary
if ste…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…Call a Function Dynamically by Name in Python
Use globals() to look up and call a function by its name as a string, with optional arguments.
def greet():
return "Hello from greet!"
def add(a, b):
return a + b
def multiply(a, b):
return a * b
if __name__ == "__main__":
func_name = "add"
args = (3, 5)
# Call function dynamically by name from globals
result = globals()[func_name](*args)
print(f"{func_name}({', '.join(ma…Chain Generators with yield from in Python
Combine multiple generators into one seamless sequence using the `yield from` delegation syntax in Python.
def numbers():
yield 1
yield 2
yield 3
def letters():
yield 'a'
yield 'b'
yield 'c'
def combined():
yield from numbers()
yield from letters()
if __name__ == "__main__":
print(list(combined()))Create a retry decorator with max attempts in Python
A decorator that retries a function up to a specified number of times when it raises an exception, with an optional delay between attempts.
import functools
import time
def retry(max_attempts, delay=0.1):
"""Retry a function up to max_attempts times on exception."""
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
for attempt in range(1, max_attempts + 1):
try:
…Format CLI help text in Python
Build a readable usage string for a command-line tool, aligning flags and wrapping descriptions with the textwrap module.
import textwrap
def format_help(command_name: str, description: str, options: list[tuple[str, str]]) -> str:
"""Format CLI help text into a readable usage string."""
header = f"Usage: {command_name} [OPTIONS]"
lines = [header, "", description, "", "Options:"]
for flag, help_text in options:
…How to Add a Dry Run Flag to a Python CLI Command
Build a Python CLI command with a --dry-run flag that previews actions and exits before making real changes.
import argparse
import sys
def main():
parser = argparse.ArgumentParser(description="Sample CLI command with dry-run flag")
parser.add_argument("--name", required=True, help="Name to greet")
parser.add_argument("--dry-run", action="store_true", dest="dry_run",
help="Show what would…How to Build Partial Functions with functools.partial in Python
Create reusable partial functions that pre-fill arguments using functools.partial, like making square and cube functions from a general power function.
```python
from functools import partial
def power(base, exponent):
"""Calculate base raised to the exponent power."""
return base ** exponent
# Create partial functions for common powers
square = partial(power, exponent=2)
cube = partial(power, exponent=3)
if __name__ == "__main__":
squares = [square(x)…How to Build a Simple Decorator That Logs Function Calls in Python
This code shows how to create a reusable decorator that logs each function call, including arguments, return value, and execution time.
import functools
import time
def log_calls(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
print(f"Calling {func.__name__} with args={args}, kwargs={kwargs}")
start = time.time()
result = func(*args, **kwargs)
end = time.time()
print(f"{func.__name__} return…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…How to Compose Two Functions into a Single Callable in Python
Combine two Python functions into a single callable using a compose helper, then apply the chained call.
def add_one(x):
return x + 1
def double(x):
return x * 2
def compose(f, g):
return lambda x: f(g(x))
add_then_double = compose(double, add_one)
double_then_add = compose(add_one, double)
result1 = add_then_double(5)
result2 = double_then_add(5)
print(f"add_one then double(5) = {result1}")
print(f"doub…How to Convert a List to an Iterator in Python with iter()
This code converts a list into an iterator using the built-in iter() function and retrieves items sequentially with next(), handling exhaustion with StopIteration.
def main():
# Original list
fruits = ["apple", "banana", "cherry"]
# Convert the list to an iterator using iter()
fruit_iterator = iter(fruits)
# Retrieve items one at a time with next()
print(next(fruit_iterator)) # apple
print(next(fruit_iterator)) # banana
print(next(fruit_iterat…How to Count Items with Default Parameters in Python
Define a Python function that prints each item with a running counter, using default parameters to allow custom start values and step increments.
def count_items(items, start=0, step=1):
"""Count items in a list with configurable start value and step."""
count = start
for item in items:
print(f"{count}: {item}")
count += step
if __name__ == "__main__":
fruits = ["apple", "banana", "cherry"]
print("Default parameters (start=0…How to Create Functions with Default Parameters in Python
This code defines two Python functions using default parameters to handle missing arguments gracefully, demonstrating how to work with optional inputs and keyword arguments.
def greet(name="Guest", greeting="Hello", punctuation="!"):
"""Generate a greeting message using default parameters."""
return f"{greeting}, {name}{punctuation}"
def create_profile(username="anonymous", age=0, city="Unknown", active=True):
"""Create a user profile dictionary with default values."""
r…How to Create Generator Functions with yield in Python
Create a memory-efficient generator function using yield to produce a Fibonacci sequence up to a limit.
def fibonacci_sequence(limit):
"""Generate Fibonacci numbers up to a given limit."""
a, b = 0, 1
while a <= limit:
yield a
a, b = b, a + b
if __name__ == "__main__":
fib_gen = fibonacci_sequence(100)
for number in fib_gen:
print(number, end=" ")
print()How to Create a Higher-Order Function in Python (Apply Twice)
This code defines a higher-order function that takes another function and a value, then applies the function twice to the value and returns the result.
def apply_twice(func, value):
return func(func(value))
def add_ten(x):
return x + 10
def square(x):
return x ** 2
if __name__ == "__main__":
print(apply_twice(add_ten, 5))
print(apply_twice(square, 3))How to Create a Timing Decorator in Python
A Python decorator that measures and prints the execution time of any function using time.perf_counter.
import time
from functools import wraps
def timing_decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
start = time.perf_counter()
result = func(*args, **kwargs)
end = time.perf_counter()
elapsed = end - start
print(f"{func.__name__} took {elapsed:.6f} seconds"…Browse by section
Each section groups closely related Python snippets.
Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
Samples vs tutorials and challenges
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.