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Python · IntermediateSnippet

Generator Expressions for Memory Efficiency

Unlike list comprehensions, generator expressions use parentheses and yield items one by one. This is significantly more memory-efficient for large datasets.

snippet.py

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import sys
 
large_list = [x for x in range(1000000)]
large_gen = (x for x in range(1000000))
 
print(sys.getsizeof(large_list)) # ~8MB
print(sys.getsizeof(large_gen))  # ~100 bytes

performancedatatypes

Python · IntermediateSnippet

Unpacking with *args and **kwargs

*args allows a function to accept any number of positional arguments as a tuple, while **kwargs accepts arbitrary keyword arguments as a dictionary.

snippet.py

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def versatile_func(required, *args, **kwargs):
    print(f'Required: {required}')
    print(f'Additional positional: {args}')
    print(f'Keyword arguments: {kwargs}')
 
versatile_func('Main', 1, 2, mode='fast', debug=True)

functionssyntax

Python · IntermediateSnippet

Property Decorators for Encapsulation

The @property decorator allows you to define methods that can be accessed like attributes, providing a clean way to implement validation and encapsulation in classes.

snippet.py

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class Circle:
    def __init__(self, radius):
        self._radius = radius
 
    @property
    def radius(self):
        return self._radius
 
    @radius.setter
    def radius(self, value):
        if value < 0:
            raise ValueError('Radius cannot be negative')
        self._radius = value

oopbestpractices

Python · IntermediateSnippet

Managing Resources with Context Managers

The 'with' statement simplifies exception handling by encapsulating standard clean-up tasks, like closing a file automatically after the block finishes.

snippet.py

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try:
    with open('data.txt', 'r') as file:
        content = file.read()
except FileNotFoundError:
    print('File not found')
# File is automatically closed here even if an error occurs

errorhandlingbestpractices

Python · IntermediateSnippet

List Comprehensions with Conditional Logic

List comprehensions provide a concise way to create lists. This intermediate pattern includes an 'if' condition to filter elements while applying a transformation.

snippet.py

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numbers = [1, 2, 3, 4, 5, 6]
squared_evens = [x**2 for x in numbers if x % 2 == 0]
print(squared_evens)  # Output: [4, 16, 36]

syntaxbestpractices

Python · IntermediateSnippet

Function Decorators

Decorators allow you to wrap another function to extend its behavior without permanently modifying it. They are often used for logging, access control, or caching.

snippet.py

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def log_call(func):
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper
 
@log_call
def greet(name):
    return f"Hello, {name}"

functionspatterns

Python · IntermediateSnippet

Parallel Iteration with zip()

The zip() function takes multiple iterables and aggregates them into tuples. This is the most efficient way to iterate over multiple lists in parallel.

snippet.py

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names = ["Alice", "Bob", "Charlie"]
scores = [85, 92, 78]
 
for name, score in zip(names, scores):
    print(f"{name} scored {score}")

syntaxperformance

Python · IntermediateSnippet

Simplifying Classes with Dataclasses

The @dataclass decorator automatically generates common methods like __init__, __repr__, and __eq__ based on type hints, reducing boilerplate code in data-oriented classes.

snippet.py

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from dataclasses import dataclass
 
@dataclass
class Product:
    name: str
    price: float
    quantity: int = 0
 
    def total_value(self) -> float:
        return self.price * self.quantity

oopdatatypes