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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 Counter Closure in Python
Build a closure in Python that remembers and increments a counter across calls without using global variables.
def create_counter(start=0):
count = start
def increment():
nonlocal count
count += 1
return count
return increment
if __name__ == "__main__":
counter = create_counter(10)
print(counter())
print(counter())
print(counter())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"…How to Create an Iterator Class with Dunder Methods in Python
A minimal Counter class implementing __iter__ and __next__ to act as a self-iterating iterator, yielding numbers from start to end-1.
class Counter:
def __init__(self, start=0, end=5):
self.current = start
self.end = end
def __iter__(self):
return self
def __next__(self):
if self.current >= self.end:
raise StopIteration
value = self.current
self.current += 1
return val…How to Document Python Functions with Google Style Docstrings
Document a Python function with a Google style docstring to describe arguments and return values clearly.
def calculate_rectangle_area(length: float, width: float) -> float:
"""Calculate the area of a rectangle.
Args:
length (float): The length of the rectangle in meters.
width (float): The width of the rectangle in meters.
Returns:
float: The area of the rectangle in square meters.
…How to Group a List into Chunks in Python
Split a list into smaller groups of a fixed size using a reusable function with a default parameter.
def make_groups(numbers, group_size=2):
"""Splits a list into smaller groups of a given size."""
groups = []
for i in range(0, len(numbers), group_size):
groups.append(numbers[i:i + group_size])
return groups
if __name__ == "__main__":
data = [1, 2, 3, 4, 5, 6, 7]
print("Default size…How to Implement Memoized Fibonacci in Python with functools.cache
Use functools.cache to memoize a recursive Fibonacci function, avoiding repeated computation and dramatically speeding up the calculation.
from functools import cache
@cache
def fibonacci(n: int) -> int:
"""Return the n-th Fibonacci number (0-indexed)."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({i}) = {fibonacci(i)}")
print(f"Cache…How to Implement a Trampoline for Tail Recursion in Python
This code implements a trampoline decorator that converts tail-recursive functions into iterative loops, allowing deep recursion without hitting Python's recursion limit.
def trampoline(fn):
"""Convert a tail-recursive function into an iterative loop."""
def wrapper(*args, **kwargs):
result = fn(*args, **kwargs)
while callable(result):
result = result()
return result
return wrapper
@trampoline
def factorial(n, acc=1):
"""Tail-recursi…How to Invalidate Cache When Arguments Change in Python
A memoization decorator that caches function results keyed by arguments, automatically invalidating when inputs change.
from functools import wraps
def memoize(func):
cache = {}
@wraps(func)
def wrapper(*args, **kwargs):
key = (args, tuple(sorted(kwargs.items())))
if key not in cache:
cache[key] = func(*args, **kwargs)
return cache[key]
return wrapper
@memoize
def expensiv…How to Parse Function Parameters with Defaults in Python
Create Python functions with default parameter values to make arguments optional and provide sensible fallbacks.
def greet(name, greeting="Hello", punctuation="!"):
"""Greet a person with customizable greeting and punctuation."""
return f"{greeting}, {name}{punctuation}"
def describe_fruit(fruit, color="unknown", ripe=False):
"""Describe a fruit with optional attributes."""
status = "ripe" if ripe else "not ripe…How to Parse Function Signatures in Python with inspect
Extract a function's parameter names, kinds, defaults, annotations, and return type using Python's built-in inspect module.
import inspect
def example_function(a: int, b: str = "default", *args, c: float = 1.5, **kwargs) -> bool:
"""An example function with various parameter types."""
return True
def parse_signature(func):
"""Parse a function's signature using the inspect module."""
sig = inspect.signature(func)
param…How to Pipe Data Through a List of Transform Functions in Python
Applies a sequence of functions to an initial value using functools.reduce, creating a reusable pipe utility.
from functools import reduce
def pipe(data, *transforms):
return reduce(lambda value, func: func(value), transforms, data)
def double(x):
return x * 2
def add_one(x):
return x + 1
def to_string(x):
return f"Result: {x}"
if __name__ == "__main__":
initial = 5
result = pipe(initial, double, …How to Print Colored Text in Python with ANSI Codes
Define a small Colors class and a colored() helper to print styled terminal text using ANSI escape codes.
class Colors:
RESET = "\033[0m"
RED = "\033[31m"
GREEN = "\033[32m"
YELLOW = "\033[33m"
BLUE = "\033[34m"
MAGENTA = "\033[35m"
CYAN = "\033[36m"
WHITE = "\033[37m"
BOLD = "\033[1m"
UNDERLINE = "\033[4m"
def colored(text, color):
return f"{color}{text}{Colors.RESET}"
if _…How to Read Environment Variables in Python with Default Values
Retrieve an environment variable safely using os.getenv() with a fallback default when the variable is missing.
import os
database_url = os.getenv("DATABASE_URL", "postgresql://localhost:5432/mydb")
print(f"Database URL: {database_url}")How to Return Multiple Values from a Python Function
This code demonstrates how a Python function can return multiple values as a tuple, and how to unpack that tuple into individual variables.
def get_user_stats(name, score, level):
"""Return multiple values as a tuple."""
return name, score, level
if __name__ == "__main__":
result = get_user_stats("Alice", 95, 3)
print(result)
print(type(result))
# Unpacking into individual variables
player_name, player_score, player_level…How to Sort a List of Numbers in Python with Default Parameters
Define a reusable sort function that uses a default parameter to sort a list of numbers in ascending or descending order.
def sort_numbers(numbers, reverse=False):
"""Sort a list of numbers in ascending or descending order."""
return sorted(numbers, reverse=reverse)
def main():
numbers = [5, 2, 9, 1, 7, 3]
# Default sort (ascending)
ascending = sort_numbers(numbers)
print(f"Ascending: {ascending}")
…How to Use Default Parameters in Python Functions
A beginner-friendly Python function that uses default parameters to compare two numbers with equal, greater, or less operations.
def compare(a, b, operation="equal"):
if operation == "equal":
return a == b
elif operation == "greater":
return a > b
elif operation == "less":
return a < b
else:
return f"Unknown operation: {operation}"
if __name__ == "__main__":
print(compare(5, 5))
print(com…How to Use Default Parameters in Python Functions
Create a simple function with default parameters to build flexible, reusable greetings in Python.
def greet(name, greeting="Hello", punctuation="!"):
"""Return a personalized greeting message."""
return f"{greeting}, {name}{punctuation}"
if __name__ == "__main__":
print(greet("Alice"))
print(greet("Bob", "Hi"))
print(greet("Charlie", greeting="Hey", punctuation="?"))…How to Use Default Parameters with Python's Split Function
Create a reusable Python wrapper around str.split with sensible default parameters for delimiter and maxsplit, showing beginners how default arguments work.
def split_with_defaults(text, delimiter=" ", maxsplit=-1):
"""
Split a string into parts using a delimiter.
Default behavior: split on spaces, unlimited splits.
"""
parts = text.split(delimiter, maxsplit)
return parts
if __name__ == "__main__":
# Example usage with defaults and custom par…How to Use Keyword-Only Arguments in Python Functions
Define Python functions with keyword-only arguments using the * separator to enforce clarity and prevent positional misuse.
def greet(name, *, greeting="Hello", punctuation="!"):
"""Greet someone with a customizable message using keyword-only arguments."""
message = f"{greeting}, {name}{punctuation}"
return message
if __name__ == "__main__":
# Basic call with only the positional argument
print(greet("Alice"))
# Al…How to Use Python's next() Builtin with a Default Sentinel Value
A wrapper function that returns the next item from an iterator, or a default sentinel value when the iterator is exhausted.
def get_next_or_default(iterator, default=None):
"""Return the next item from an iterator, or default if exhausted."""
return next(iterator, default)
if __name__ == "__main__":
fruits = iter(["apple", "banana", "cherry"])
print(get_next_or_default(fruits)) # apple
print(get_next_or…How to Use functools.reduce in Python
Apply functools.reduce with operator functions and lambda expressions to aggregate lists into sums, products, maximums, and concatenated strings.
from functools import reduce
import operator
# Sum all numbers in a list using reduce
numbers = [1, 2, 3, 4, 5]
sum_result = reduce(operator.add, numbers)
# Find the maximum value using reduce
max_result = reduce(lambda a, b: a if a > b else b, numbers)
# Multiply all numbers using reduce
product_result = reduce(la…How to Use singledispatch for Type-Based Overloading in Python
This code demonstrates Python's functools.singledispatch decorator to create functions that behave differently based on the type of their first argument.
from functools import singledispatch
@singledispatch
def process(value):
return f"Unknown type: {type(value).__name__}"
@process.register(int)
def _(value):
return f"Integer: {value * 2}"
@process.register(str)
def _(value):
return f"String: {value.upper()}"
@process.register(list)
def _(value):
re…Browse by section
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