Pythonic Code PDF

Pythonic Code The goals of this chapter are as follows: To understand indices and slices, and correctly implement objects that can be indexed To implement sequences and other iterables To learn about good use cases for context managers, and how to write effective ones. To implement more id...

Pythonic Code The goals of this chapter are as follows: To understand indices and slices, and correctly implement objects that can be indexed To implement sequences and other iterables To learn about good use cases for context managers, and how to write effective ones. To implement more idiomatic code through magic methods To avoid common mistakes in Python that lead to undesired side effects Indexes and slices In Python, as in other languages, some data structures or types support accessing its elements by index Another thing it has in common with most programming languages is that the first element is placed in the index number In Python, this would work too, but we could also use a negative index number. Creating your own sequences The functionality we just discussed works, thanks to a magic method called __getitem__ A sequence is an object that implements the behavior of a collection of elements in a specific order, and strings are examples of sequence objects found in the standard library. In this section, we care more about getting particular elements from an object by a key than building sequences or iterable objects. Cleanup Code in Python Cleanup code is essential in programming to ensure that resources are properly released or reset after use. This is particularly important when working with external resources like files, network connections, or database connections, as failing to release these resources can lead to memory leaks or other issues. Creating your own sequences If you create classes that should act like standard types, it's a good idea to implement the interfaces from this module. This shows what your class is meant to do and ensures you include the necessary methods.  Context managers Context managers are a very useful feature in Python. They help run code that needs to happen before and after a main action. Context managers are especially helpful for managing resources. Since handling all possible situations in our code can make debugging difficult, A common solution is to place cleanup code in a finally block to ensure it always runs.  Context managers The open function acts as a context manager, automatically closing the file when the block is done, even if there's an error. Context managers have two special methods: __enter__ and __exit__. Implementing context managers Context managers can be implemented using a class with __enter__ and __exit__ methods. The with statement in Python is used to wrap the execution of a block of code within methods defined by a context manager. This approach ensures that resources are properly managed, such as files or network connections, without requiring explicit cleanup code. with statement example # Example: Reading a file using a context manager # Open the file using a context manager with open('example.txt', 'r') as file: # Perform operations on the file content = file.read() print(content) # Once the block inside the 'with' statement is finished, # the file will be automatically closed, regardless of any exceptions. # Example: Creating a custom context manager class MyContextManager: def __enter__(self): # Perform resource initialization print("Initializing resources") # You can optionally return an object that will be assigned to the variable after 'as' return self def __exit__(self, exc_type, exc_value, traceback): # Perform resource cleanup print("Cleaning up resources") # Handle any exceptions, if needed if exc_type: print(f"An exception of type {exc_type} occurred: {exc_value}") # Return False to propagate the exception, or True to suppress it return False Comprehensions and assignment expressions This is because they're usually a more concise way of writing code, and in general, code written this way tends to be easier to read The use of comprehensions is recommended to create data structures in a single instruction, instead of multiple operations  Properties, attributes, and different types of methods for objects In Python, all properties and functions of an object are public, unlike some other languages that have public, private, or protected attributes. Attributes starting with an underscore are intended to be private, and external access is discouraged.  Underscores in Python Python uses conventions and implementation details regarding underscores, which are important to understand. The attribute _timeout is intended to be accessed only within the connector class and not by external callers. Code should be organized to allow safe refactoring of _timeout without external dependency, preserving the object's interface.  Single Underscore Vs Double Underscore In Python Single underscore: A single underscore before an object name is a convention that indicates that the attribute or method is intended to be used inside that class. However, privacy is not enforced in any way. Using leading underscores for functions in a module indicates it should not be imported from somewhere else. Double underscore: A double underscore before an object name triggers name mangling, which means that the name of the variable is changed to include the class name as a prefix. This is done to avoid naming conflicts between different classes. The variable can still be accessed from outside the class by using the mangled name, but this is discouraged Properties The @property decorator in Python is a way to create properties in a class, which are attributes that can be accessed and modified like regular instance attributes, but with some additional functionality. For example, you can use properties to: Validate the input values before setting them Compute the attribute value dynamically based on other attributes Control the access level of the attribute (read-only, write-only, etc.) To use the @property decorator, you need to define a getter method for the property, which returns the attribute value, and optionally a setter method, which sets the attribute value. The getter method should have the same name as the property, and the setter method should have the same name with a.setter suffix.  Creating classes with a more compact syntax Boilerplate code in Python: Often, Python objects require initializing attributes within the __init__ method. Dataclasses simplify initialization: Python 3.7 introduced the dataclasses module to streamline this process. Data classes allow you to define classes with minimal boilerplate code, automatically generating methods like __init__, __repr__, and more.  Dataclass decorator 1.__init__: Initializes the object and assigns values to the attributes based on the provided arguments. 2.__repr__: Returns a string representation of the object. 3.__eq__: Compares two objects for equality based on their attribute values. 4.__ne__: Compares two objects for inequality based on their attribute values. 5.__lt__: Defines the less-than comparison between two objects. 6.__gt__: Defines the greater-than comparison between two objects. 7.__le__: Defines the less-than-or-equal-to comparison between two objects. 8.__ge__: Defines the greater-than-or-equal-to comparison between two objects. If you need to disable repr Iterable objects When you try to iterate an object in the form for e in myobject: what Python checks at a very high level are the following two things, in order: If the object contains one of the iterator methods— __next__ or __iter__ If the object is a sequence and has __len__ and __getitem__ Creating iterable objects Sequence vs. Iterator in Python Sequence and iterator are fundamental concepts in Python, often used to process collections of data. While they are related, they have distinct characteristics: Sequence A sequence is an ordered collection of items. It provides a way to access elements by their index. Python's built-in sequence types include: Lists: Mutable sequences of arbitrary objects. Tuples: Immutable sequences of arbitrary objects. Strings: Immutable sequences of characters. Ranges: Immutable sequences of numbers. Iterator An iterator is an object that implements the iterator protocol, which consists of two methods: __iter__: Returns the iterator object itself. __next__: Returns the next item in the sequence. If there are no more items, it raises a StopIteration exception. Iterators provide a way to access elements of a sequence one at a time, without loading the entire sequence into memory at once. This can be especially useful for large datasets. Relationship between Sequences and Iterators: Sequences are iterable: All sequence types in Python are iterable, meaning they can be used with for loops. Iterators can be created from sequences: Python provides built-in functions like iter() to create iterators from sequences. Thanks