Python For Seniors (Part 2)

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Welcome to part 2 of our advanced Python programming guide! The beginner tutorial covered core concepts like syntax, data structures, functions, classes, and modules.

Find part 1 here. 

Now, let’s dive deeper into some advanced techniques experienced Python developers use. This guide assumes you already have a strong grasp of Python basics.

Object-Oriented Design Principles

Python supports object-oriented programming, allowing developers to organize code into class hierarchies and modeled after real-world entities.

Mastering OOP techniques is key to designing and building robust large-scale applications in Python.

Some key object-oriented design principles:

  • Encapsulation

This involves bundling related attributes and behaviors into individual classes. For example, a Person class would encapsulate properties like name, age, and behaviors like walking and talking.

Encapsulation allows control over the data through well-defined interfaces along with information hiding. Methods act as the interface, while attributes are kept private to hide complexity.

  • Inheritance

Classes can inherit commonly used state and behaviors from parent classes. For example, a Student class can inherit from a base Person class to avoid rewriting duplicated code. The child class only needs to define properties and methods unique to students.

Inheritance enables reuse of code and polymorphism. Subclasses can extend, override, or modify inherited logic as needed.

  • Abstraction

This involves exposing only relevant data/methods through public interfaces while hiding unnecessary implementation details. For example, an abstract class Shape could define an area() method while concrete subclasses Circle, Square implement the actual area calculations.

Abstraction reduces complexity and couples code loosely by separating high-level and low-level logic. Interfaces help maintain abstraction.

  • Composition

This refers to combining objects to model complex behaviors. Rather than rely on inheritance alone, objects can use other objects via composition. For example, a Car class could compose objects like a Wheel or engine rather than directly inheriting their capabilities.

Composition provides flexibility and encapsulation for complex object interactions.

By following these principles, Python programs can implement domain entities and business logic in an organized, object-oriented manner. Let’s look at some examples.

We’ll model a zoo management system with classes for animals, zookeepers, and enclosures using inheritance, polymorphism, encapsulation, and composition:

# Animal base class

class Animal:

def __init__(self, name, species):

self.name = name

self.species = species

def make_sound(self):

print(f"{self.name} says Rawwwr!")

# Inherited child classes

class Lion(Animal):

def make_sound(self):

print(f"{self.name} says Roar!")

class Snake(Animal):

def make_sound(self):

print(f"{self.name} says Hiss!")

# Zookeeper class

class Zookeeper:

def __init__(self, name):

self.name = name

def feed_animal(self, animal):

print(f"{self.name} is feeding the {animal.species} named {animal.name}")

animal.make_sound() # Polymorphism

# Enclosure class

class Enclosure:

def __init__(self, id, animals):

self.id = id

self.animals = animals

def add_animal(self, animal):

self.animals.append(animal)

# Create objects

leo = Lion("Leo", "Lion")

marty = Snake("Marty", "Python")

bob = Zookeeper("Bob")

snakes_enclosure = Enclosure(123, [marty])

bob.feed_animal(leo)

snakes_enclosure.add_animal(leo)

This demonstrates modeling domain entities with encapsulation, inheritance, polymorphism, and composition. Code reuse is improved, coupling reduced, and abstraction maintained.

Python Decorators

Decorators dynamically alter the functionality of a function, method, or class without having to modify the code directly. They essentially wrap the original object and modify its behavior as needed before executing it.

Decorators start with the @ symbol and are placed at the definition. For example:

@timer

def run_long_job(args):

# Function body

Here @timer is a decorator that measures how long run_long_job takes to execute.

Let’s see how to build this timer decorator:

import time

def timer(func):

# Inner wrapper function

def inner(*args, **kwargs):

start = time.time()

result = func(*args, **kwargs)

end = time.time()

print(f"Execution took {end-start} seconds")

return result

# Return inner function

return inner

@timer

def long_running_job(n):

print("Running long job...")

time.sleep(n)

return "Done!"

long_running_job(5)

# Prints execution time

When decorated, long_running_job behavior is extended with the timer functionality without modifying its code.

Some other example uses cases for Python decorators:

  • Logging function arguments and results
  • Checking permissions or roles before executing functions
  • Caching return values to avoid recalculation
  • Rate limiting function calls
  • Instrumenting code for tracing or profiling
  • Validating input data types

Decorators supercharge Python with metaprogramming capabilities and expressiveness. They are widely used across frameworks like Flask, Django, etc. Understanding decorators unlocks their powerful capabilities.

Concurrency in Python

Concurrency refers to executing multiple tasks simultaneously through parallelization or asynchronous programming. Python supports various concurrency models to improve program efficiency and speed.

Some approaches include:

  • Threads

Threads allow execution of code in parallel within the same interpreter process. The OS schedules thread execution across cores.

For IO-bound tasks, threads improve the utilization of idle time that would otherwise be spent waiting. The threading module supports spawning and synchronizing threads:

from threading import Thread

def io_bound_worker():

# Perform IO intensive work

threads = [Thread(target=io_bound_worker) for _ in range(8)]

for thread in threads:

thread.start()

# Main thread continues executing

Multiprocessing

For CPU-bound tasks, Python’s multiprocessing module distributes work across multiple processes. Each CPU core runs a separate Python interpreter, circumventing the GIL limitation.

Processes have higher overhead than threads but enable true parallelism across multiple CPUs:

from multiprocessing import Process

def cpu_bound_worker(data):

# Perform heavy computations

if __name__ == "__main__":

inputs = [large_dataset] * 8

processes = []

for input_data in inputs:

p = Process(target=cpu_bound_worker, args=(input_data,))

processes.append(p)

p.start()

# Rest of main process code executes in parallel

asyncio

This module provides infrastructure for writing asynchronous code using async/await syntax. It is well-suited for tasks involving network I/O and concurrency:

import asyncio

async def fetch_data(url):

# Async HTTP request

response = await aiohttp.request(url)

return response

async def main():

urls = [url1, url2, url3]

tasks = []

for url in urls:

tasks.append(fetch_data(url))

results = await asyncio.gather(*tasks)

asyncio.run(main())

Asyncio helps build highly performant network apps by efficiently handling thousands of concurrent connections.

concurrent.futures

This high-level module abstracts thread and process pools for executing callables asynchronously:

with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:

futures = [executor.submit(cpu_bound_fn, arg) for arg in args]

results = [f.result() for f in futures]

The Future objects provide a clean interface to wait for and retrieve results. The module handles pooling and concurrency under the hood.

These approaches enable Python developers to speed up programs through parallelism, utilize multiple cores, and handle thousands of concurrent connections.

Metaprogramming with Metaclasses

While classes in Python enable the creating of multiple objects, metaclasses allow you to customize how the classes themselves are constructed and modified at a meta-level.

Metaclasses intercept class creation and modify the class before it’s finalized. For example, automatically registering models in a registry, applying mixins, interfacing with ORMs, etc.

To use a metaclass, define a base type and override __new__ and __init__ methods:

class RegistryMeta(type):

registry = {}

def __new__(cls, name, bases, attrs):

# Modify attrs

attrs['id'] = len(RegistryMeta.registry)

# Build class as normal

klass = type.__new__(cls, name, bases, attrs)

# Register class

RegistryMeta.registry[klass.id] = klass

return klass

def __init__(self, name, bases, attrs):

print(f"Initializing {name} class")

super().__init__(name, bases, attrs)

Any class inheriting this metaclass will be intercepted and registered:

class Base(metaclass=RegistryMeta):

pass

class Person(Base):

pass

print(RegistryMeta.registry)

# {0: <class '__main__.Base'>, 1: <class '__main__.Person'>}

Metaclasses open up powerful metaprogramming capabilities and customization hooks to Python’s class construction process.

Dynamic Attribute Access

Unlike statically typed languages, Python enables objects to have attributes added dynamically at runtime, beyond those explicitly defined in __init__ or elsewhere in the class.

For example:

class Point:

def __init__(self, x, y):

self.x = x

self.y = y

p = Point(2, 3)

p.z = 5 # New attribute created dynamically

print(p.z)

# Outputs 5

This dynamic behavior can be useful in certain cases like:

  • Implementing caching as attributes
  • Lazily creating attributes only when accessed
  • Proxy or delegate classes that reroute attribute access
  • Dynamic mixins that add capabilities to classes

However, it can also make code harder to understand and trace since attributes aren’t explicitly defined beforehand.

Python supports the __slots__ magic method to restrict this behavior. It tells Python only to allow attributes listed there and raise AttributeError for anything else:

class Point:

__slots__ = ['x', 'y']

p = Point(2, 3)

p.z = 5 # AttributeError!

So __slots__ prevents surprise behaviors from dynamic attributes.

Descriptor Protocol

This advanced protocol provides the underlying mechanics of how attributes like properties and methods work in Python.

Descriptors essentially control attribute access on objects. They are implemented as classes containing __get__, __set__ and __delete__ methods.

For example, the @property decorator works through descriptors:

class Property:

def __init__(self, fget):

self.fget = fget

def __get__(self, obj, owner):

return self.fget(obj)

class Point:

def __init__(self, x):

self.x = x

@Property

def y(self):

return self.x * 2

p = Point(10)

print(p.y) # Calls getter internally

Here, the Property descriptor class implements __get__ to call the underlying y method when accessed as an attribute.

Some other examples of descriptors:

  • @classmethod and @staticmethod to define different method types
  • @memoized to cache method return values
  • __slots__ to restrict attributes
  • ORM frameworks mapping DB rows to Python objects

The descriptor protocol is a key ingredient that enables much of Python’s magic like @property, class methods, static methods, etc. Mastering descriptors unlocks deeper capabilities.

Context Managers

While the with statement provides easy file cleanup logic, Python enables implementing the same pattern for any object through context managers.

For example, acquiring and releasing a lock or database connection:

class Resource:

def __enter__(self):

print("Acquiring resource")

def __exit__(self, exc_type, exc_val, exc_tb):

print("Releasing resource")

with Resource() as resource:

# Use resource

This ensures reliable cleanup through the __exit__ method. Context managers can also suppress exceptions during cleanup:

class Resource:

# ...

def __exit__(self, exc_type, exc_val, exc_tb):

print("Handling exception")

# suppress exception by returning True

return True

The contextlib module provides utilities like @contextmanager to simplify creating context managers.

Some real-world examples include:

  • File opening
  • Lock acquiring/release
  • Database connections
  • Temporary directory handling
  • Logging redirected to buffer

Context managers provide a robust way to handle resources in Python.

Unit Testing

Writing tests is vital for validating code quality and ensuring proper behavior as the codebase grows. Python comes with a built-in unittest framework for authoring and running unit tests.

The key components are test case classes, individual test methods, assertions, and test runners:

import unittest

class UserTestCase(unittest.TestCase):

# Setup run before each test method

def setUp(self):

self.user = User("John", "Doe")

def test_full_name(self):

self.assertEqual(self.user.full_name(), "John Doe")

def test_initials(self):

self.assertEqual(self.user.initials(), "J.D")

@unittest.expectedFailure

def test_send_email(self):

self.user.send_email("test@example.com")

if __name__ == "__main__":

unittest.main() # Run all tests

This allows for organizing related tests into reusable test cases. Functionality like fixtures, assertions, mocking, and test runners handle the testing workflow.

Some other Python testing tools include pytest for a streamlined experience and mocks for isolating code dependencies.

Thorough testing improves code quality and reduces bugs in the long run. Tests empower developers to refactor and iterate rapidly.

Generators and Iterators

Generators allow pausing and resuming execution to produce a sequence of values individually through iteration lazily. This is useful for:

  • Dealing with large datasets without loading everything into memory
  • Implementing streams and efficient pipelines
  • Avoiding eager allocation of resources until needed

In Python, generators are defined using yield instead of return:

def num_sequence(n):

for i in range(n):

yield i

seq = num_sequence(3)

print(next(seq)) # 0

print(next(seq)) # 1

When called, generators return a generator object that supports the iteration protocol. Lazy iteration enables efficient streaming:

def read_log_file(file):

for line in open(file):

yield process(line)

for event in read_log_file("logs.txt"):

print(event)

Generators allow implementation of Python iterators elegantly. Popular libraries like Django ORM use them extensively for lazy querying.

Asynchronous Programming

Python 3.5 introduced async/await syntax to natively support asynchronous code using asyncio:

import asyncio


async def fetch(url):
  async with aiohttp.ClientSession() as session:
    async with session.get(url) as response:
      return await response.text()


async def main():
  urls = ["url1", "url2", "url3"]
  tasks = []
  for url in urls:
    tasks.append(fetch(url))

  

  results = await asyncio.gather(*tasks)
  print(results)
asyncio.run(main())

Asyncio provides an event loop to orchestrate concurrent tasks and asynchronous I/O efficiently. It is well suited for highly parallel network programs.

Python Typing

Type hints allow adding static types to function arguments and return values for static analysis:

from typing import List
def sum_numbers(nums: List[int]) -> int:
  return sum(nums)

This metadata enables better error-catching, IDE autocompletion, and documentation. The code still runs dynamically, as usual.

Popular third-party tools like MyPy leverage these type hints to provide optional static type checking for Python. Typing brings some of the benefits of static languages to Python.

Python Packaging

Python code is typically organized into modules or packages. The Python Packaging Index (PyPI) contains thousands of open-source packages with functionality beyond the standard library.

Some best practices for structuring Python code for others to use:

  • Setup project structure with src and tests folders
  • Write setup.py script for pip installation
  • Include requirements.txt with dependencies
  • Follow Semantic Versioning for releases
  • Upload package to PyPI for public sharing

Tools like Poetry, Flit, and pipenv streamline handling dependencies and packaging. Sharing reusable packages enables collective code reuse in Python.

Conclusion

This concludes our advanced guide to modern Python. We covered important techniques like:

  • Object-oriented programming principles
  • Metaprogramming with metaclasses and decorators
  • Improving performance through concurrency
  • Robust resource handling with context managers
  • Unit testing and maintaining code quality
  • Lazy generation of sequences for efficiency
  • Asynchronous I/O handling
  • Static type hints
  • Python packaging ecosystem

These capabilities enable large, robust applications, libraries, and frameworks to be developed in Python.

The key is always to keep learning through documentation, books, open-source code, conferences, and trying out ideas. Python has an amazing community.

I hope you enjoyed reading this guide and feel motivated to start your Python programming journey.

If you find this post exciting, find more exciting posts on Learnhub Blog; we write everything tech from Cloud computing to Frontend DevCybersecurityAI, and Blockchain.

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