This chapter covers advanced topics that go beyond the basics of Python usage, aiming to maximize productivity and write more efficient code. The code developed through this chapter will be more robust, scalable, and easier to maintain.<\/p>\n\n\n\n
Context managers are a Python feature that automates resource allocation and release in scenarios such as file opening, database connections, and using locks. They enhance code readability and reduce the likelihood of bugs.<\/p>\n\n\n\n
The most common example of a context manager in Python is opening a file using the To implement a context manager, you just need to define a class with This example manages resources by recognizing the start and end of a block.<\/p>\n\n\n\n Generators are a simplified version of iterators and can save memory when processing large datasets. Generators yield one value at a time and wait until the next value is needed. This property allows generators to efficiently handle large datasets.<\/p>\n\n\n\n Generator functions are defined like regular functions but use The above function creates a generator object that returns 1, 2, and 3 sequentially each time it is called.<\/p>\n\n\n\n Generators can easily create infinite loops, making them useful for processes that repeat periodically.<\/p>\n\n\n\n This function returns an increasing number starting from 0 indefinitely until stopped.<\/p>\n\n\n\n Decorators are powerful tools that dynamically alter or extend the behavior of functions or methods. They greatly enhance code reusability and are primarily used for logging, access control, and metrics.<\/p>\n\n\n\n Decorators can wrap another function to add specific logic or modify the input and output of an existing function.<\/p>\n\n\n\n In this example, the functionality added by the decorator runs when the Multiple decorators can be applied to a single function, and their behavior can vary based on the order of the decorators.<\/p>\n\n\n\n To improve program performance, you can use parallelism or multithreading. This allows the code to utilize multiple CPU cores to perform tasks simultaneously.<\/p>\n\n\n\n Multithreading is useful in cases where there are many I\/O bound tasks. You can create threads using Python’s This code creates three threads, each performing a task for 2 seconds.<\/p>\n\n\n\n For CPU bound tasks, the Exception handling is essential for enhancing the reliability of programs. In this section, we will explore advanced exception handling techniques.<\/p>\n\n\n\n You can create custom exceptions to explicitly express exceptions that may occur in specific situations.<\/p>\n\n\n\nwith<\/code> statement.<\/p>\n\n\n\nwith open('example.txt', 'r') as file:\n data = file.read()\n # The file is automatically closed when the block ends.<\/code><\/pre>\n\n\n\n1.2 Custom Context Managers<\/h3>\n\n\n\n
__enter__<\/code> and __exit__<\/code> methods.<\/p>\n\n\n\nclass CustomContext:\n def __enter__(self):\n # Resource allocation or setup\n print(\"Resource allocated\")\n return self\n \n def __exit__(self, exc_type, exc_val, exc_tb):\n # Resource release\n print(\"Resource released\")\n\nwith CustomContext() as context:\n print(\"Inside the block\")<\/code><\/pre>\n\n\n\n2. Generators<\/h2>\n\n\n\n
2.1 Generator Functions<\/h3>\n\n\n\n
yield<\/code> instead of return<\/code> to provide values.<\/p>\n\n\n\ndef simple_generator():\n yield 1\n yield 2\n yield 3<\/code><\/pre>\n\n\n\n2.2 Infinite Generators<\/h3>\n\n\n\n
def infinite_sequence():\n num = 0\n while True:\n yield num\n num += 1<\/code><\/pre>\n\n\n\n3. Decorators<\/h2>\n\n\n\n
3.1 Definition and Use of Decorators<\/h3>\n\n\n\n
def my_decorator(func):\n def wrapper():\n print(\"Before function call\")\n func()\n print(\"After function call\")\n return wrapper\n\n@my_decorator\ndef say_hello():\n print(\"Hello!\")\n\nsay_hello()<\/code><\/pre>\n\n\n\nsay_hello<\/code> function is called.<\/p>\n\n\n\n3.2 Combining Multiple Decorators<\/h3>\n\n\n\n
def decorator_one(func):\n def wrapper():\n print(\"Decorator 1 applied\")\n func()\n return wrapper\n\ndef decorator_two(func):\n def wrapper():\n print(\"Decorator 2 applied\")\n func()\n return wrapper\n\n@decorator_two\n@decorator_one\ndef display():\n print(\"Display function\")\n\ndisplay()<\/code><\/pre>\n\n\n\n4. Parallelism and Multithreading<\/h2>\n\n\n\n
4.1 Multithreading<\/h3>\n\n\n\n
threading<\/code> module.<\/p>\n\n\n\nimport threading\nimport time\n\ndef thread_function(name):\n print(f\"Thread {name} started\")\n time.sleep(2)\n print(f\"Thread {name} ended\")\n\nthreads = []\nfor i in range(3):\n thread = threading.Thread(target=thread_function, args=(i,))\n threads.append(thread)\n thread.start()\n\nfor thread in threads:\n thread.join()<\/code><\/pre>\n\n\n\n4.2 Multiprocessing<\/h3>\n\n\n\n
multiprocessing<\/code> module is more efficient. It creates processes to fully utilize CPU cores.<\/p>\n\n\n\nfrom multiprocessing import Process\n\ndef process_function(name):\n print(f\"Process {name} started\")\n time.sleep(2)\n print(f\"Process {name} ended\")\n\nprocesses = []\nfor i in range(3):\n process = Process(target=process_function, args=(i,))\n processes.append(process)\n process.start()\n\nfor process in processes:\n process.join()<\/code><\/pre>\n\n\n\n5. Advanced Exception Handling<\/h2>\n\n\n\n
5.1 Creating Custom Exceptions<\/h3>\n\n\n\n
class CustomError(Exception):\n pass\n\ntry:\n raise CustomError(\"This is a custom exception\")\nexcept CustomError as e:\n print(e)<\/code><\/pre>\n\n\n\n5.2 Exception Chaining<\/h3>\n\n\n\n