The `yield` keyword in Python is used to create generators. It allows you to iterate over data without storing it all in memory.
Benefits include memory efficiency and lazy evaluation. Drawbacks include complexity in understanding and debugging.
Example:
def count_up_to(n):
count = 1
while count <= n:
yield count
count += 1
for number in count_up_to(5):
print(number)The `pathlib` module provides an object-oriented approach to filesystem operations. It offers a more intuitive way to handle file paths and directories.
Example:
from pathlib import Path
path = Path('example.txt')
if path.exists():
print('File exists')
else:
print('File does not exist')Python C extensions allow you to write performance-critical code in C. They are compiled and loaded as modules in Python.
Example:
# setup.py
from setuptools import setup, Extension
setup(
ext_modules=[Extension('my_module', ['my_module.c'])]
)The `dataclasses` module simplifies the creation of classes for storing data. It automatically generates special methods like `__init__`, `__repr__`, and `__eq__`.
Example:
from dataclasses import dataclass
@dataclass
class Person:
name: str
age: int
person = Person(name='Alice', age=25)
print(person)BeautifulSoup and Requests are popular libraries for web scraping. BeautifulSoup parses HTML and XML, while Requests handles HTTP requests.
Example:
import requests
from bs4 import BeautifulSoup
response = requests.get('https://example.com')
soup = BeautifulSoup(response.text, 'html.parser')
print(soup.title.text)Flask and SQLAlchemy are used to create REST APIs. Flask is a web framework, while SQLAlchemy is an ORM for database operations.
Example:
from flask import Flask, jsonify
from flask_sqlalchemy import SQLAlchemy
app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///db.sqlite'
db = SQLAlchemy(app)
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(80), unique=True, nullable=False)
@app.route('/users')
def get_users():
users = User.query.all()
return jsonify([user.name for user in users])
if __name__ == '__main__':
app.run()The `pydantic` library is used for data validation and settings management using Python type annotations.
Example:
from pydantic import BaseModel
class User(BaseModel):
name: str
age: int
user = User(name='John', age=30)
print(user)Coroutines are special functions that allow asynchronous programming. They enable code to run concurrently without blocking.
Example of coroutines:
import asyncio
async def say_hello():
print('Hello')
await asyncio.sleep(1)
print('World')
asyncio.run(say_hello())Python wheels are a distribution format for Python packages. They simplify the installation process.
Creating a wheel:
# Install wheel
$ pip install wheel
# Create wheel
$ python setup.py bdist_wheelOptimizing I/O-bound tasks involves using asynchronous programming and efficient I/O operations. CPU-bound tasks can be optimized using multi-processing and efficient algorithms.
Example for I/O-bound:
import asyncio
async def fetch_data():
await asyncio.sleep(1)
return 'data'
# Example for CPU-bound
from multiprocessing import Pool
def compute(x):
return x * x
with Pool(5) as p:
print(p.map(compute, range(10)))