Thursday 23 May 2024

13 Powerful Python Features You're Probably Not Using Enough

Python is a versatile and powerful language, and while many developers use it extensively, there are numerous features that often go underutilized.

List Comprehensions

List comprehensions provide a concise way to create lists. This can replace the need for using loops to generate lists.

squares = [x**2 for x in range(10)]

Generator Expressions

Similar to list comprehensions but with parentheses, generator expressions are used for creating generators. These are memory-efficient and suitable for large data sets.

squares_gen = (x**2 for x in range(10))

Default Dictionary

The defaultdict from the collections module is a dictionary-like class that provides default values for missing keys.

from collections import defaultdict

dd = defaultdict(int)

dd['key'] += 1

Named Tuples

namedtuple creates tuple subclasses with named fields. This makes code more readable by accessing fields by name instead of position.

from collections import namedtuple

Point = namedtuple('Point', 'x y')

p = Point(10, 20)

Enumerate Function

The enumerate function adds a counter to an iterable and returns it as an enumerate object. This is useful for obtaining both the index and the value in a loop.

for index, value in enumerate(['a', 'b', 'c']):

    print(index, value)

Zip Function

The zip function combines multiple iterables into a single iterable of tuples. This is useful for iterating over multiple sequences simultaneously.

names = ['a', 'b', 'c']

ages = [20, 25, 30]

combined = list(zip(names, ages))


Set Comprehensions

Similar to list comprehensions, set comprehensions create sets in a concise way.

unique_squares = {x**2 for x in range(10)}

Frozenset

A frozenset is an immutable set. It's useful when you need a set that cannot be changed after creation.

fs = frozenset([1, 2, 3, 2, 1])



Counter

The Counter class from the collections module counts the occurrences of elements in a collection. It's useful for counting hashable objects.

from collections import Counter

counts = Counter(['a', 'b', 'c', 'a', 'b', 'b'])

Context Managers

Using the with statement, context managers handle resource management, like file I/O, efficiently and cleanly.

with open('file.txt', 'r') as file:

    contents = file.read()



dataclass

The dataclass decorator simplifies class creation by automatically adding special methods like init and repr.

from dataclasses import dataclass

@dataclass

class Point:

    x: int

    y: int



Decorators

Decorators are functions that modify the behavior of other functions. They are useful for logging, access control, memoization, and more.

def my_decorator(func):

    def wrapper():

        print("Something is happening before the function is called.")

        func()

        print("Something is happening after the function is called.")

    return wrapper

@my_decorator

def say_hello():

    print("Hello!")

Asyncio

The asyncio module provides a framework for asynchronous programming. This is useful for I/O-bound and high-level structured network code.

import asyncio

async def main():

    print('Hello')

    await asyncio.sleep(1)

    print('World')

asyncio.run(main())



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