Real Python

Programming Sockets in Python

2024-12-17T14:00:00+00:00

Sockets and the socket API are used to send messages across a network. They provide a form of inter-process communication (IPC). The network can be a logical, local network to the computer, or one that’s physically connected to an external network with its own connections to other networks. The obvious example is the Internet, which you connect to via your ISP.

In this video course, you’ll create:

A simple socket server and client
An improved version that handles multiple connections simultaneously
A server-client application that functions like a full-fledged socket application, complete with its own custom header and content

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Dictionaries in Python

2024-12-16T14:00:00+00:00

Python dictionaries are a powerful built-in data type that allows you to store key-value pairs for efficient data retrieval and manipulation. Learning about them is essential for developers who want to process data efficiently. In this tutorial, you’ll explore how to create dictionaries using literals and the dict() constructor, as well as how to use Python’s operators and built-in functions to manipulate them.

By learning about Python dictionaries, you’ll be able to access values through key lookups and modify dictionary content using various methods. This knowledge will help you in data processing, configuration management, and dealing with JSON and CSV data.

By the end of this tutorial, you’ll understand that:

A dictionary in Python is a mutable collection of key-value pairs that allows for efficient data retrieval using unique keys.
Both dict() and {} can create dictionaries in Python. Use {} for concise syntax and dict() for dynamic creation from iterable objects.
dict() is a class used to create dictionaries. However, it’s commonly called a built-in function in Python.
.__dict__ is a special attribute in Python that holds an object’s writable attributes in a dictionary.
Python dict is implemented as a hashmap, which allows for fast key lookups.

To get the most out of this tutorial, you should be familiar with basic Python syntax and concepts such as variables, loops, and built-in functions. Some experience with basic Python data types will also be helpful.

Get Your Code: Click here to download the free sample code that you’ll use to learn about dictionaries in Python.

Take the Quiz: Test your knowledge with our interactive “Python Dictionaries” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python Dictionaries

Test your understanding of Python dictionaries

Getting Started With Python Dictionaries

Dictionaries are one of Python’s most important and useful built-in data types. They provide a mutable collection of key-value pairs that lets you efficiently access and mutate values through their corresponding keys:

Python
      
        
      
    
>>> config = {
...     "color": "green",
...     "width": 42,
...     "height": 100,
...     "font": "Courier",
... }

>>> # Access a value through its key
>>> config["color"]
'green'

>>> # Update a value
>>> config["font"] = "Helvetica"
>>> config
{
    'color': 'green',
    'width': 42,
    'height': 100,
    'font': 'Helvetica'
}

A Python dictionary consists of a collection of key-value pairs, where each key corresponds to its associated value. In this example, "color" is a key, and "green" is the associated value.

Dictionaries are a fundamental part of Python. You’ll find them behind core concepts like scopes and namespaces as seen with the built-in functions globals() and locals():

Python
      
        
      
    
>>> globals()
{
    '__name__': '__main__',
    '__doc__': None,
    '__package__': None,
    ...
}

The globals() function returns a dictionary containing key-value pairs that map names to objects that live in your current global scope.

Python also uses dictionaries to support the internal implementation of classes. Consider the following demo class:

Python
      
>>> class Number:
...     def __init__(self, value):
...         self.value = value
...

>>> Number(42).__dict__
{'value': 42}

The .__dict__ special attribute is a dictionary that maps attribute names to their corresponding values in Python classes and objects. This implementation makes attribute and method lookup fast and efficient in object-oriented code.

You can use dictionaries to approach many programming tasks in your Python code. They come in handy when processing CSV and JSON files, working with databases, loading configuration files, and more.

Python’s dictionaries have the following characteristics:

Mutable: The dictionary values can be updated in place.
Dynamic: Dictionaries can grow and shrink as needed.
Efficient: They’re implemented as hash tables, which allows for fast key lookup.
Ordered: Starting with Python 3.7, dictionaries keep their items in the same order they were inserted.

The keys of a dictionary have a couple of restrictions. They need to be:

Hashable: This means that you can’t use unhashable objects like lists as dictionary keys.
Unique: This means that your dictionaries won’t have duplicate keys.

In contrast, the values in a dictionary aren’t restricted. They can be of any Python type, including other dictionaries, which makes it possible to have nested dictionaries.

It’s important to note that dictionaries are collections of pairs. So, you can’t insert a key without its corresponding value or vice versa. Since they come as a pair, you always have to insert a key with its corresponding value.

Note: In some situations, you may want to add keys to a dictionary without deciding what the associated value should be. In those cases, you can use the .setdefault() method to create keys with a default or placeholder value.

Read the full article at https://realpython.com/python-dicts/ »

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Object-Oriented Programming (OOP) in Python

2024-12-15T14:00:00+00:00

Object-oriented programming (OOP) in Python lets you structure your code by grouping related properties and behaviors into individual objects. You create classes as blueprints and instantiate them to form objects. With OOP, you can model real-world entities and their interactions, and create complex systems with reusable components.

OOP in Python revolves around four main concepts: encapsulation, inheritance, abstraction, and polymorphism. Encapsulation bundles data with methods, inheritance lets you create subclasses, abstraction hides complex details, and polymorphism allows for different implementations.

By the end of this tutorial, you’ll understand that:

Object-oriented programming in Python involves structuring code into classes to model real-world entities.
The four key concepts of OOP in Python are encapsulation, inheritance, abstraction, and polymorphism.
OOP in Python is considered straightforward to learn due to its clear syntax and readability.
The main focus of OOP in Python involves creating classes as blueprints for objects. These objects contain data and the methods needed to manipulate that data.

You’ll explore how to define classes, instantiate classes to create objects, and leverage inheritance to build robust systems in Python.

Note: This tutorial is adapted from the chapter “Object-Oriented Programming (OOP)” in Python Basics: A Practical Introduction to Python 3.

The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell, so you’ll see occasional references to IDLE throughout this tutorial. If you don’t use IDLE, you can run the example code from the editor and environment of your choice.

Get Your Code: Click here to download the free sample code that shows you how to do object-oriented programming with classes in Python 3.

Take the Quiz: Test your knowledge with our interactive “Object-Oriented Programming (OOP) in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Object-Oriented Programming (OOP) in Python

Object-oriented programming (OOP) is a method of structuring a program by bundling related properties and behaviors into individual objects.

What Is Object-Oriented Programming in Python?

Object-oriented programming is a programming paradigm that provides a means of structuring programs so that properties and behaviors are bundled into individual objects.

For example, an object could represent a person with properties like a name, age, and address and behaviors such as walking, talking, breathing, and running. Or it could represent an email with properties like a recipient list, subject, and body and behaviors like adding attachments and sending.

Put another way, object-oriented programming is an approach for modeling concrete, real-world things, like cars, as well as relations between things, like companies and employees or students and teachers. OOP models real-world entities as software objects that have some data associated with them and can perform certain operations.

OOP also exists in other programming languages and is often described to center around the four pillars, or four tenants of OOP:

Encapsulation allows you to bundle data (attributes) and behaviors (methods) within a class to create a cohesive unit. By defining methods to control access to attributes and its modification, encapsulation helps maintain data integrity and promotes modular, secure code.
Inheritance enables the creation of hierarchical relationships between classes, allowing a subclass to inherit attributes and methods from a parent class. This promotes code reuse and reduces duplication.
Abstraction focuses on hiding implementation details and exposing only the essential functionality of an object. By enforcing a consistent interface, abstraction simplifies interactions with objects, allowing developers to focus on what an object does rather than how it achieves its functionality.
Polymorphism allows you to treat objects of different types as instances of the same base type, as long as they implement a common interface or behavior. Python’s duck typing make it especially suited for polymorphism, as it allows you to access attributes and methods on objects without needing to worry about their actual class.

In this tutorial you’ll take a practical approach to understanding OOP in Python. But keeping these four concepts of object-oriented programming in mind may help you to remember the information that you gather.

The key takeaway is that objects are at the center of object-oriented programming in Python. In other programming paradigms, objects only represent the data. In OOP, they additionally inform the overall structure of the program.

How Do You Define a Class in Python?

In Python, you define a class by using the class keyword followed by a name and a colon. Then you use .__init__() to declare which attributes each instance of the class should have:

Python
      
    
class Employee:
    def __init__(self, name, age):
        self.name =  name
        self.age = age

But what does all of that mean? And why do you even need classes in the first place? Take a step back and consider using built-in, primitive data structures as an alternative.

Primitive data structures—like numbers, strings, and lists—are designed to represent straightforward pieces of information, such as the cost of an apple, the name of a poem, or your favorite colors, respectively. What if you want to represent something more complex?

For example, you might want to track employees in an organization. You need to store some basic information about each employee, such as their name, age, position, and the year they started working.

One way to do this is to represent each employee as a list:

Python
      
    
kirk = ["James Kirk", 34, "Captain", 2265]
spock = ["Spock", 35, "Science Officer", 2254]
mccoy = ["Leonard McCoy", "Chief Medical Officer", 2266]

There are a number of issues with this approach.

Read the full article at https://realpython.com/python3-object-oriented-programming/ »

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Build Enumerations of Constants With Python's Enum

2024-12-15T14:00:00+00:00

Python’s enum module offers a way to create enumerations, a data type allowing you to group related constants. You can define an enumeration using the Enum class, either by subclassing it or using its functional API. This tutorial will guide you through the process of creating and using Python enums, comparing them to simple constants, and exploring specialized types like IntEnum, IntFlag, and Flag.

Enumerations provide benefits over simple constants by offering a structured, readable, and maintainable way to manage sets of constant values. They ensure type safety, prevent value reassignment, and facilitate iteration and member access. By learning to utilize Python’s enum types, you enhance your ability to write organized and efficient code.

By the end of this tutorial, you’ll understand that:

An enum in Python groups related constants in a single data type using the Enum class.
You create enumerations by subclassing Enum or using the module’s functional API.
Using Enum over simple constants provides structure, prevents reassignment, and enhances code readability.
Enum, IntEnum, IntFlag, and Flag differ in their support for integer operations and bitwise flags.
Enums can work with data types like integers or strings, boosting their flexibility.
You access enumeration members using dot notation, call notation, or subscript notation.
You can iterate over enum members using loops or the .__members__ attribute.

To follow along with this tutorial, you should be familiar with object-oriented programming and inheritance in Python.

Get Your Code: Click here to download the free sample code that you’ll use to build enumerations in Python.

Getting to Know Enumerations in Python

Several programming languages, including Java and C++, have a native enumeration or enum data type as part of their syntax. This data type allows you to create sets of named constants, which are considered members of the containing enum. You can access the members through the enumeration itself.

Enumerations come in handy when you need to define an immutable and discrete set of similar or related constant values that may or may not have semantic meaning in your code.

Days of the week, months and seasons of the year, Earth’s cardinal directions, a program’s status codes, HTTP status codes, colors in a traffic light, and pricing plans of a web service are all great examples of enumerations in programming. In general, you can use an enum whenever you have a variable that can take one of a limited set of possible values.

Python doesn’t have an enum data type as part of its syntax. Fortunately, Python 3.4 added the enum module to the standard library. This module provides the Enum class for supporting general-purpose enumerations in Python.

Enumerations were introduced by PEP 435, which defines them as follows:

An enumeration is a set of symbolic names bound to unique, constant values. Within an enumeration, the values can be compared by identity, and the enumeration itself can be iterated over. (Source)

Before this addition to the standard library, you could create something similar to an enumeration by defining a sequence of similar or related constants. To this end, Python developers often used the following idiom:

Python
      
>>> RED, GREEN, YELLOW = range(3)

>>> RED
0

>>> GREEN
1

Even though this idiom works, it doesn’t scale well when you’re trying to group a large number of related constants. Another inconvenience is that the first constant will have a value of 0, which is falsy in Python. This can be an issue in certain situations, especially those involving Boolean tests.

Note: If you’re using a Python version before 3.4, then you can create enumerations by installing the enum34 library, which is a backport of the standard-library enum. The aenum third-party library could be an option for you as well.

In most cases, enumerations can help you avoid the drawbacks of the above idiom. They’ll also help you produce more organized, readable, and robust code. Enumerations have several benefits, some of which relate to ease of coding:

Allowing for conveniently grouping related constants in a sort of namespace
Allowing for additional behavior with custom methods that operate on either enum members or the enum itself
Providing quick and flexible access to enum members
Enabling direct iteration over members, including their names and values
Facilitating code completion within IDEs and editors
Enabling type and error checking with static checkers
Providing a hub of searchable names
Mitigating spelling mistakes when using the members of an enumeration

They also make your code robust by providing the following benefits:

Ensuring constant values that can’t be changed during the code’s execution
Guaranteeing type safety by differentiating the same value shared across several enums
Improving readability and maintainability by using descriptive names instead of mysterious values or magic numbers
Facilitating debugging by taking advantage of readable names instead of values with no explicit meaning
Providing a single source of truth and consistency throughout the code

Now that you know the basics of enumerations in programming and in Python, you can start creating your own enum types by using Python’s Enum class.

Creating Enumerations With Python’s `Enum`

Python’s enum module provides the Enum class, which allows you to create enumeration types. To create your own enumerations, you can either subclass Enum or use its functional API. Both options will let you define a set of related constants as enum members.

In the following sections, you’ll learn how to create enumerations in your code using the Enum class. You’ll also learn how to set automatically generated values for your enums and how to create enumerations containing alias and unique values. To kick things off, you’ll start by learning how to create an enumeration by subclassing Enum.

Read the full article at https://realpython.com/python-enum/ »

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Dependency Management With Python Poetry

2024-12-15T14:00:00+00:00

Poetry is a tool for managing Python projects that simplifies dependency management. You can use Poetry to specify, install, and resolve dependencies, ensuring that you work with the correct package versions. Poetry takes advantage of a pyproject.toml file for configuration and maintains a poetry.lock file to lock dependencies, providing a consistent environment across different machines.

You should generally install Poetry system-wide using tools like pipx. You can then create a new project with poetry new or add Poetry to an existing project with poetry init. Later, use poetry add to specify a new dependency in your project and poetry install to install the listed dependencies into your environment. By understanding how to manage dependencies effectively, you ensure that your projects are reproducible and maintainable.

By the end of this tutorial, you’ll understand that:

Poetry is a dependency manager that ensures consistent package versions and simplifies Python project setup.
You can install Poetry system-wide using pipx or the official installer for better environment management.
Installing Poetry with pip in a project’s environment is not recommended due to potential dependency conflicts.
Basic Poetry CLI commands include poetry new, poetry add, poetry install, and poetry update.
The poetry.lock file locks dependency versions, ensuring reproducible environments.
You resolve dependency conflicts by updating the poetry.lock file with poetry update or poetry lock.
Best practices include committing the poetry.lock file to version control and avoiding system-wide package installations.

To complete this tutorial and get the most out of it, you should have a basic understanding of virtual environments, modules and packages, and pip.

While you’ll focus on dependency management in this tutorial, Poetry can also help you build a distribution package for your project. If you want to share your work, then you can use Poetry to publish your project on the Python Packaging Index (PyPI).

Free Bonus: Click here to get access to a free 5-day class that shows you how to avoid common dependency management issues with tools like Pip, PyPI, Virtualenv, and requirements files.

Take Care of Prerequisites

Before diving into the nitty-gritty of Python Poetry, you’ll take care of some prerequisites. First, you’ll read a short overview of the terminology that you’ll encounter in this tutorial. Next, you’ll install Poetry itself.

Learn the Relevant Terminology

If you’ve ever used an import statement in one of your Python scripts, then you’ve worked with modules and packages. Some of them might have been Python files you wrote on your own. Others could’ve been standard library modules that ship with Python, like datetime. However, sometimes, what Python provides isn’t enough. That’s when you might turn to external modules and packages maintained by third parties.

When your Python code relies on such external modules and packages, they become the requirements or dependencies of your project.

To find packages contributed by the Python community that aren’t part of the Python standard library, you can browse PyPI. Once you’ve found a package you’re interested in, you can use Poetry to manage and install that package in your project. Before seeing how this works, you need to install Poetry on your system.

Install Poetry on Your Computer

Poetry is distributed as a Python package itself, which means that you can install it into a virtual environment using pip, just like any other external package:

Windows PowerShell
      
(venv) PS> python -m pip install poetry

Shell
      
(venv) $ python3 -m pip install poetry

This is fine if you just want to quickly try it out. However, the official documentation strongly advises against installing Poetry into your project’s virtual environment, which the tool must manage. Because Poetry depends on several external packages itself, you’d run the risk of a dependency conflict between one of your project’s dependencies and those required by Poetry. In turn, this could cause Poetry or your code to malfunction.

In practice, you always want to keep Poetry separate from any virtual environment that you create for your Python projects. You also want to install Poetry system-wide to access it as a stand-alone application regardless of the specific virtual environment or Python version that you’re currently working in.

There are several ways to get Poetry running on your computer, including:

A tool called pipx
The official installer
Manual installation
Pre-built system packages

In most cases, the recommended way to install Poetry is with the help of pipx, which takes care of creating and maintaining isolated virtual environments for command-line Python applications. After installing pipx, you can install Poetry by issuing the following command in your terminal window:

Windows PowerShell
      
PS> pipx install poetry

Shell
      
$ pipx install poetry

While this command looks very similar to the one you saw previously, it’ll install Poetry into a dedicated virtual environment that won’t be shared with other Python packages.

Read the full article at https://realpython.com/dependency-management-python-poetry/ »

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Python's property(): Add Managed Attributes to Your Classes

2024-12-15T14:00:00+00:00

The @property decorator simplifies the management of attributes in your Python classes. It allows you to control attribute access, enabling features such as data validation, lazy evaluation, and the creation of backward-compatible APIs without modifying the class’s public interface. By using @property, you can avoid the clutter of getter and setter methods, keeping your code clean and Pythonic.

Using @property effectively transforms your attributes into dynamic, computed, or read-only properties. You can leverage this feature to validate input data, compute attributes dynamically, or manage attribute deletion. It’s particularly useful when you need to modify attribute behavior without disrupting existing code. Understanding when to use @property is key to optimal class design, as it ensure both clarity and performance.

By the end of this tutorial, you’ll understand that:

A property in Python is a tool for creating managed attributes in classes.
The @property decorator allows you to define getter, setter, and deleter methods for attributes.
You use properties when you need controlled access or want to encapsulate logic without changing the API.
Properties are useful for validating data, computing attributes, and creating read-only or read-write attributes.
You should avoid @property when direct access is sufficient or performance is critical.
You create read-only attributes by defining a getter method only, while read-write attributes require both a getter and setter method.

First, you’ll learn how to use property() as a function through practical examples. These examples will demonstrate how to validate input data, compute attribute values dynamically, log your code, and more. Then you’ll explore the @property decorator, the most common syntax for working with properties. To get the most out of this tutorial, you should know the basics of object-oriented programming, classes, and decorators in Python.

Get Your Code: Click here to download the free sample code that shows you how to use Python’s property() to add managed attributes to your classes.

Take the Quiz: Test your knowledge with our interactive “Python's property(): Add Managed Attributes to Your Classes” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python's property(): Add Managed Attributes to Your Classes

In this quiz, you'll test your understanding of Python's property(). With this knowledge, you'll be able to create managed attributes in your classes, perform lazy attribute evaluation, provide computed attributes, and more.

Managing Attributes in Your Classes

When you define a class in an object-oriented programming language, you’ll probably end up with some instance and class attributes. In other words, you’ll end up with variables that are accessible through the instance, class, or even both, depending on the language. Attributes represent and hold the internal state of a given object, which you’ll often need to access and mutate.

Typically, you have at least two ways to access and mutate an attribute. Either you can access and mutate the attribute directly or you can use methods. Methods are functions attached to a given class. They provide the behaviors and actions that an object can perform with its internal data and attributes.

If you expose attributes to the user, then they become part of the class’s public API. This means that your users will access and mutate them directly in their code. The problem comes when you need to change the internal implementation of a given attribute.

Say you’re working on a Circle class and add an attribute called .radius, making it public. You finish coding the class and ship it to your end users. They start using Circle in their code to create a lot of awesome projects and applications. Good job!

Now suppose that you have an important user that comes to you with a new requirement. They don’t want Circle to store the radius any longer. Instead, they want a public .diameter attribute.

At this point, removing .radius to start using .diameter could break the code of some of your other users. You need to manage this situation in a way other than removing .radius.

Programming languages such as Java and C++ encourage you to never expose your attributes to avoid this kind of problem. Instead, you should provide getter and setter methods, also known as accessors and mutators, respectively. These methods offer a way to change the internal implementation of your attributes without changing your public API.

Note: Getter and setter methods are often considered an anti-pattern and a signal of poor object-oriented design. The main argument behind this proposition is that these methods break encapsulation. They allow you to access and mutate the components of your objects from the outside.

These programming languages need getter and setter methods because they don’t have a suitable way to change an attribute’s internal implementation when a given requirement changes. Changing the internal implementation would require an API modification, which can break your end users’ code.

The Getter and Setter Approach in Python

Technically, there’s nothing that stops you from using getter and setter methods in Python. Here’s a quick example that shows how this approach would look:

Pythonpoint_v1.py
      
    
class Point:
    def __init__(self, x, y):
        self._x = x
        self._y = y

    def get_x(self):
        return self._x

    def set_x(self, value):
        self._x = value

    def get_y(self):
        return self._y

    def set_y(self, value):
        self._y = value

In this example, you create a Point class with two non-public attributes ._x and ._y to hold the Cartesian coordinates of the point at hand.

Note: Python doesn’t have the notion of access modifiers, such as private, protected, and public, to restrict access to attributes and methods. In Python, the distinction is between public and non-public class members.

If you want to signal that a given attribute or method is non-public, then you have to use the well-known Python convention of prefixing the name with an underscore (_). That’s the reason behind the naming of the attributes ._x and ._y.

Note that this is just a convention. It doesn’t stop you and other programmers from accessing the attributes using dot notation, as in obj._attr. However, it’s bad practice to violate this convention.

To access and mutate the value of either ._x or ._y, you can use the corresponding getter and setter methods. Go ahead and save the above definition of Point in a Python module and import the class into an interactive session. Then run the following code:

Python
      
        
      
    
>>> from point_v1 import Point

>>> point = Point(12, 5)
>>> point.get_x()
12
>>> point.get_y()
5

>>> point.set_x(42)
>>> point.get_x()
42

>>> # Non-public attributes are still accessible
>>> point._x
42
>>> point._y
5

With .get_x() and .get_y(), you can access the current values of ._x and ._y. You can use the setter method to store a new value in the corresponding managed attribute. From the two final examples, you can confirm that Python doesn’t restrict access to non-public attributes. Whether or not you access them directly is up to you.

Read the full article at https://realpython.com/python-property/ »

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Python Classes: The Power of Object-Oriented Programming

2024-12-15T14:00:00+00:00

Python classes form the backbone of object-oriented programming, enabling you to encapsulate data and behavior into a single entity. When you work with a Python class, you define attributes to store data and methods to perform actions. This structure allows you to model real-world objects and create organized, reusable code.

A class in Python serves as a blueprint for creating objects, which are instances of the class. You use classes when you need to encapsulate related data and functions, making your code modular and easier to manage. By defining classes, you can create multiple objects that share the same attributes and methods, while maintaining their own unique state.

In this tutorial, you’ll learn how to define and use Python classes, understand the distinction between classes and objects, and explore methods and attributes. You’ll also learn about instance and class attributes, methods, inheritance, and common pitfalls to avoid when working with classes.

By the end of this tutorial, you’ll understand that:

A class in Python is like a recipe for creating objects that encapsulate both data and behavior.
You use classes to model complex data structures and behaviors in a modular way.
You define classes in Python using the class keyword, and instantiate them to create objects.
A class is a blueprint, while an object is an instance of a class.
Methods define behaviors, while attributes store data within class instances.
Instance attributes are unique to each object, while class attributes are shared across all instances of the class.

To get the most out of this tutorial, you should know about Python variables, data types, and functions. Some experience with object-oriented programming (OOP) is also a plus. Don’t worry if you’re not an OOP expert yet. In this tutorial, you’ll learn the key concepts that you need to get started and more. You’ll also write several practical examples to help reinforce your knowledge of Python classes.

Get Your Code: Click here to download your free sample code that shows you how to build powerful object blueprints with classes in Python.

Take the Quiz: Test your knowledge with our interactive “Python Classes - The Power of Object-Oriented Programming” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python Classes - The Power of Object-Oriented Programming

In this quiz, you'll test your understanding of Python classes. With this knowledge, you'll be able to define reusable pieces of code that encapsulate data and behavior in a single entity, model real-world objects, and solve complex problems.

Getting Started With Python Classes

Python is a multiparadigm programming language that supports object-oriented programming (OOP) through classes that you can define with the class keyword. You can think of a class as a piece of code that specifies the data and behavior that represent and model a particular type of object.

What is a class in Python? A common analogy is that a class is like the blueprint for a house. You can use the blueprint to create several houses and even a complete neighborhood. Each concrete house is an object or instance that’s derived from the blueprint.

Each instance can have its own properties, such as color, owner, and interior design. These properties carry what’s commonly known as the object’s state. Instances can also have different behaviors, such as locking the doors and windows, opening the garage door, turning the lights on and off, watering the garden, and more.

In OOP, you commonly use the term attributes to refer to the properties or data associated with a specific object of a given class. In Python, attributes are variables defined inside a class with the purpose of storing all the required data for the class to work.

Similarly, you’ll use the term methods to refer to the different behaviors that objects will show. Methods are functions that you define within a class. These functions typically operate on or with the attributes of the underlying instance or class. Attributes and methods are collectively referred to as members of a class or object.

You can write classes to model the real world. These classes will help you better organize your code and solve complex programming problems.

For example, you can use classes to create objects that emulate people, animals, vehicles, books, buildings, cars, or other objects. You can also model virtual objects, such as a web server, directory tree, chatbot, file manager, and more.

Finally, you can use classes to build class hierarchies. This way, you’ll promote code reuse and remove repetition throughout your codebase.

In this tutorial, you’ll learn a lot about classes and all the cool things that you can do with them. To kick things off, you’ll start by defining your first class in Python. Then you’ll dive into other topics related to instances, attributes, and methods.

Defining a Class in Python

To define a class, you need to use the class keyword followed by the class name and a colon, just like you’d do for other compound statements in Python. Then you must define the class body, which will start at the next indentation level:

Python Syntax
      
    
class ClassName:
    <body>

In a class’s body, you can define attributes and methods as needed. As you already learned, attributes are variables that hold the class data, while methods are functions that provide behavior and typically act on the class data.

Note: In Python, the body of a given class works as a namespace where attributes and methods live. You can only access those attributes and methods through the class or its objects.

As an example of how to define attributes and methods, say that you need a Circle class to model different circles in a drawing application. Initially, your class will have a single attribute to hold the radius. It’ll also have a method to calculate the circle’s area:

Pythoncircle.py
      
    
import math

class Circle:
    def __init__(self, radius):
        self.radius = radius

    def calculate_area(self):
        return math.pi * self.radius ** 2

Read the full article at https://realpython.com/python-classes/ »

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Primer on Python Decorators

2024-12-14T14:00:00+00:00

Python decorators allow you to modify or extend the behavior of functions and methods without changing their actual code. When you use a Python decorator, you wrap a function with another function, which takes the original function as an argument and returns its modified version. This technique provides a simple way to implement higher-order functions in Python, enhancing code reusability and readability.

You can use decorators in various practical scenarios, such as logging, enforcing access control, caching results, or measuring execution time. To create a custom decorator, define a function that takes another function as an argument, creates a nested wrapper function, and returns the wrapper. When applying a decorator, you place @decorator on the line before the function definition.

By the end of this tutorial, you’ll understand that:

Python decorators allow you to wrap a function with another function to extend or modify its behavior without altering the original function’s code.
Practical use cases for decorators include logging, enforcing access control, caching results, and measuring execution time.
Custom decorators are written by defining a function that takes another function as an argument, defines a nested wrapper function, and returns the wrapper.
Multiple decorators can be applied to a single function by stacking them, one per line, before the function definition.
The order of decorators impacts the final output, as each decorator wraps the next, influencing the behavior of the decorated function.

You can find all the examples from this tutorial by downloading the accompanying materials below:

Get Your Code: Click here to download the free sample code that shows you how to create and use Python decorators.

Free Bonus: Click here to get access to a free "The Power of Python Decorators" guide that shows you three advanced decorator patterns and techniques you can use to write cleaner and more Pythonic programs.

Decorators Cheat Sheet: Click here to get access to a free three-page Python decorators cheat sheet that summarizes the techniques explained in this tutorial.

Decorators Q&A Transcript: Click here to get access to a 25-page chat log from our Python decorators Q&A session in the Real Python Community Slack where we discussed common decorator questions.

Take the Quiz: Test your knowledge with our interactive “Decorators” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Decorators

In this quiz, you'll revisit the foundational concepts of what Python decorators are and how to create and use them.

Python Functions

In order to understand decorators, you must first understand some finer points of how functions work. There are many aspects to functions, but in the context of decorators, a function returns a value based on the given arguments. Here’s a basic example:

Python
      
>>> def add_one(number):
...     return number + 1
...

>>> add_one(2)
3

In general, functions in Python may also have side effects rather than just turning an input into an output. The print() function is an example of this: it returns None while having the side effect of outputting something to the console. However, to understand decorators, it’s enough to think about functions as tools that turn given arguments into values.

First-Class Objects

In functional programming, you work almost entirely with pure functions that don’t have side effects. While not a purely functional language, Python supports many functional programming concepts, including treating functions as first-class objects.

This means that functions can be passed around and used as arguments, just like any other object like str, int, float, list, and so on. Consider the following three functions:

Pythongreeters.py
      
    
def say_hello(name):
    return f"Hello {name}"

def be_awesome(name):
    return f"Yo {name}, together we're the awesomest!"

def greet_bob(greeter_func):
    return greeter_func("Bob")

Here, say_hello() and be_awesome() are regular functions that expect a name given as a string. The greet_bob() function, however, expects a function as its argument. You can, for example, pass it the say_hello() or the be_awesome() function.

To test your functions, you can run your code in interactive mode. You do this with the -i flag. For example, if your code is in a file named greeters.py, then you run python -i greeters.py:

Python
      
>>> greet_bob(say_hello)
'Hello Bob'

>>> greet_bob(be_awesome)
'Yo Bob, together we're the awesomest!'

Note that greet_bob(say_hello) refers to two functions, greet_bob() and say_hello, but in different ways. The say_hello function is named without parentheses. This means that only a reference to the function is passed. The function isn’t executed. The greet_bob() function, on the other hand, is written with parentheses, so it will be called as usual.

This is an important distinction that’s crucial for how functions work as first-class objects. A function name without parentheses is a reference to a function, while a function name with trailing parentheses calls the function and refers to its return value.

Inner Functions

It’s possible to define functions inside other functions. Such functions are called inner functions. Here’s an example of a function with two inner functions:

Read the full article at https://realpython.com/primer-on-python-decorators/ »

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YAML: The Missing Battery in Python

2024-12-14T14:00:00+00:00

YAML is a portable and widely used data serialization format. Unlike the more compact JSON or verbose XML formats, YAML emphasizes human readability with block indentation, which should be familiar to most Python programmers. While Python comes with batteries included, it lacks built-in support for YAML. Still, you can read and write YAML documents in Python by installing a third-party library, such as PyYAML.

PyYAML allows you to serialize and deserialize Python data types, as well as custom objects, to and from YAML. It also provides ways to safely handle YAML data from untrusted sources. PyYAML is a popular choice for relatively simple use cases. However, you might want to explore alternatives like ruamel.yaml for YAML 1.2 compliance or StrictYAML for schema validation.

By the end of this tutorial, you’ll understand that:

YAML stands for YAML Ain’t Markup Language, emphasizing its focus on data representation rather than document markup.
YAML is often used for configuration and serialization due to its human-readable syntax.
Python doesn’t support YAML natively, unlike the JSON and XML formats. You need to install a third-party library to work with YAML in Python programs.
PyYAML is arguably the most popular YAML library for Python due to its simplicity.
Alternatives to PyYAML include ruamel.yaml and StrictYAML, which offer more features.

To get the most out of this tutorial, you should be familiar with object-oriented programming in Python and know how to create a class. If you’re ready to jump in, then you can follow the link below to get the source code for the examples that you’ll code in this tutorial:

Get Your Code: Click here to download the free sample code you’ll use to work with YAML in Python.

Taking a Crash Course in YAML

In this section, you’re going to learn the basic facts about YAML, including its uses, syntax, and some of its unique and powerful features. If you’ve worked with YAML before, then you can skip ahead and continue reading from the next section, which covers using YAML in Python.

Historical Context

YAML, which rhymes with camel, is a recursive acronym that stands for YAML Ain’t Markup Language because it’s not a markup language! Interestingly enough, the original draft for the YAML specification defined the language as Yet Another Markup Language, but later the current backronym was adopted to more accurately describe the language’s purpose.

An actual markup language, such as Markdown or HTML, lets you annotate text with formatting or processing instructions intermixed with your content. Markup languages are, therefore, primarily concerned with text documents, whereas YAML is a data serialization format that integrates well with common data types native to many programming languages. There’s no inherent text in YAML, only data to represent.

YAML was originally meant to simplify Extensible Markup Language (XML), but in reality, it has a lot more in common with JavaScript Object Notation (JSON). In fact, it’s a superset of JSON.

Even though XML was initially designed to be a metalanguage for creating markup languages for documents, people quickly adopted it as the standard data serialization format. The HTML-like syntax of angle brackets made XML look familiar. Suddenly, everyone wanted to use XML as their configuration, persistence, or messaging format.

As the first kid on the block, XML dominated the scene for many years. It became a mature and trusted data interchange format and helped shape new concepts like building interactive web applications. After all, the letter X in AJAX, a technique for getting data from the server without reloading the page, stands for none other than XML.

Ironically, it was AJAX that ultimately led to XML’s decline in popularity. The verbose, complex, and redundant XML syntax wasted a lot of bandwidth when data was sent over the network. Parsing XML documents in JavaScript was slow and tedious because of XML’s fixed document object model (DOM), which wouldn’t match the application’s data model. The community finally acknowledged that they’d been using the wrong tool for the job.

That’s when JSON entered the picture. It was built from the ground up with data serialization in mind. Web browsers could parse it effortlessly because JSON is a subset of JavaScript, which they already supported. Not only was JSON’s minimalistic syntax appealing to developers, but it also made porting to other platforms easier than XML did. To this day, JSON remains the slimmest, fastest, and most versatile textual data interchange format on the Internet.

YAML came into existence the same year as JSON, and by pure coincidence, it was almost a complete superset of JSON on the syntactical and semantic levels. Starting from YAML 1.2, the format officially became a strict superset of JSON, meaning that every valid JSON document also happens to be a YAML document.

In practice, however, the two formats look different, as the YAML specification puts more emphasis on human readability by adding a lot more syntactic sugar and features on top of JSON. As a result, YAML is more applicable to configuration files edited by hand rather than as a transport layer.

Comparison With XML and JSON

If you’re familiar with XML or JSON, then you might be wondering what YAML brings to the table. All three are major data interchange formats, which share some overlapping features. For example, they’re all text based and more or less human readable. At the same time, they differ in many respects, which you’ll find out next.

Note: There are other, popular textual data formats like TOML, which the new build system in Python is based on. Currently, only external packaging and dependency management tools like Poetry can read TOML, but since Python 3.11 there’s a TOML parser in the standard library.

Common binary data serialization formats you’d find in the wild include Google’s Protocol Buffers and Apache’s Avro.

Now have a look at a sample document expressed in all three data formats but representing the same person. You can click to expand the collapsible sections and reveal data serialized in those formats:

XML
      
    
<?xml version="1.0" encoding="UTF-8" ?>
<person firstName="John" lastName="Doe">
    <dateOfBirth>1969-12-31</dateOfBirth>
    <married>true</married>
    <spouse>
        <person firstName="Jane" lastName="Doe">
            <dateOfBirth/> <!- This is a comment -->
        </person>
    </spouse>
</person>

JSON
      
    
{
    "person": {
        "dateOfBirth": "1969-12-31",
        "firstName": "John",
        "lastName": "Doe",
        "married": true,
        "spouse": {
            "dateOfBirth": null,
            "firstName": "Jane",
            "lastName": "Doe"
        }
    }
}

YAML
      
    
%YAML 1.2
---
person:
  dateOfBirth: 1969-12-31
  firstName: John
  lastName: Doe
  married: true
  spouse:
    dateOfBirth: null  # This is a comment
    firstName: Jane
    lastName: Doe

Read the full article at https://realpython.com/python-yaml/ »

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Build Command-Line Interfaces With Python's argparse

2024-12-14T14:00:00+00:00

When building Python command-line interfaces (CLI), Python’s argparse module offers a comprehensive solution. You can use argparse to create user-friendly command-line interfaces that parse arguments and options directly from the command line. This tutorial guides you through organizing CLI projects, adding arguments and options, and customizing your CLI’s behavior with argparse.

You’ll also learn about setting up command-line argument parsers, parsing arguments, and implementing advanced features like subcommands and mutually exclusive argument groups. By understanding how argparse handles errors and messages, you can create robust and intuitive CLIs for your Python applications.

By the end of this tutorial, you’ll understand that:

Command-line interfaces enable interaction with applications through terminals.
You can create CLIs in Python using the standard library argparse module.
argparse parses command-line arguments and generates help messages.
You can customize CLIs with argparse by defining argument types and actions.
Subcommands and mutually exclusive groups enhance CLI functionality.
Error handling and exit statuses are crucial for robust CLI applications.

To get the most out of this tutorial, you should be familiar with Python programming, including concepts such as object-oriented programming, script development and execution, and Python packages and modules. It’ll also be helpful if you’re familiar with general concepts and topics related to using a command line or terminal.

Get Your Code: Click here to download the free sample code that you’ll use to build command-line interfaces with argparse.

Take the Quiz: Test your knowledge with our interactive “Build Command-Line Interfaces With Python's argparse” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Build Command-Line Interfaces With Python's argparse

In this quiz, you'll test your understanding of creating command-line interfaces (CLIs) in Python using the argparse module. This knowledge is essential for creating user-friendly command-line apps, which are common in development, data science, and systems administration.

Getting to Know Command-Line Interfaces

Since the invention of computers, humans have always needed and found ways to interact and share information with these machines. The information exchange has flowed among humans, computer software, and hardware components. The shared boundary between any two of these elements is generically known as an interface.

In software development, an interface is a special part of a given piece of software that allows interaction between components of a computer system. When it comes to human and software interaction, this vital component is known as the user interface.

You’ll find different types of user interfaces in programming. Probably, graphical user interfaces (GUIs) are the most common today. However, you’ll also find apps and programs that provide command-line interfaces (CLIs) for their users. In this tutorial, you’ll learn about CLIs and how to create them in Python.

Command-Line Interfaces (CLIs)

Command-line interfaces allow you to interact with an application or program through your operating system command line, terminal, or console.

To understand command-line interfaces and how they work, consider this practical example. Say that you have a directory called sample containing three sample files. If you’re on a Unix-like operating system, such as Linux or macOS, go ahead and open a command-line window or terminal in the parent directory and then execute the following command:

Shell
      
$ ls sample/
hello.txt     lorem.md      realpython.md

The ls Unix command lists the files and subdirectories contained in a target directory, which defaults to the current working directory. The above command call doesn’t display much information about the content of sample. It only displays the filenames on the screen.

Note: If you’re on Windows, then you’ll have an ls command that works similarly to the Unix ls command. However, in its plain form, the command displays a different output:

Windows PowerShell
      
PS> ls .\sample\

    Directory: C:\sample

Mode                 LastWriteTime         Length Name
----                 -------------         ------ ----
-a---          11/10/2022 10:06 AM             88 hello.txt
-a---          11/10/2022 10:06 AM           2629 lorem.md
-a---          11/10/2022 10:06 AM            429 realpython.md

The PowerShell ls command issues a table containing detailed information on every file and subdirectory under your target directory. So, the upcoming examples won’t work as expected on Windows systems.

Suppose you want richer information about your directory and its content. In that case, you don’t need to look around for a program other than ls because this command has a full-featured command-line interface with a useful set of options that you can use to customize the command’s behavior.

For example, go ahead and execute ls with the -l option:

Shell
      
        
      
    
$ ls -l sample/
total 24
-rw-r--r--@ 1 user  staff    83 Aug 17 22:15 hello.txt
-rw-r--r--@ 1 user  staff  2609 Aug 17 22:15 lorem.md
-rw-r--r--@ 1 user  staff   428 Aug 17 22:15 realpython.md

The output of ls is quite different now. The command displays much more information about the files in sample, including permissions, owner, group, date, and size. It also shows the total space that these files use on your computer’s disk.

Note: To get a detailed list of all the options that ls provides as part of its CLI, go ahead and run the man ls command in your command line or terminal.

This richer output results from using the -l option, which is part of the Unix ls command-line interface and enables the detailed output format.

Commands, Arguments, Options, Parameters, and Subcommands

Throughout this tutorial, you’ll learn about commands and subcommands. You’ll also learn about command-line arguments, options, and parameters, so you should incorporate these terms into your tech vocabulary:

Read the full article at https://realpython.com/command-line-interfaces-python-argparse/ »

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Sorting a Python Dictionary: Values, Keys, and More

2024-12-14T14:00:00+00:00

Sorting a Python dictionary involves organizing its key-value pairs in a specific order. To sort a Python dictionary by its keys, use the sorted() function combined with .items(). This approach returns a list of tuples sorted by keys, which you can convert back to a dictionary using the dict() constructor. Sorting by values requires specifying a sort key using a lambda function or itemgetter().

You can sort a Python dictionary in descending order by setting the reverse argument of the sorted() function to True. Sorting dictionaries with non-comparable keys or values can be challenging, but you can manage it by providing default values or using a custom sort key.

By the end of this tutorial, you’ll understand that:

You can sort a dictionary by its keys using sorted() with .items() and dict().
To sort by values, you use sorted() with a key function like lambda or itemgetter().
Sorting in descending order is possible by setting reverse=True in sorted().
For non-comparable keys or values, you use default values or custom sort keys.
Python dictionaries can’t be sorted in-place, so you need to create a new sorted dictionary.

Read on to learn how to effectively sort dictionaries using these techniques and the strategic implications of choosing the right data structure for your key-value data. But first, you’ll learn some foundational knowledge that will help you understand how to sort a dictionary in Python.

Free Download: Click here to download the code that you’ll use to sort key-value pairs in this tutorial.

Rediscovering Dictionary Order in Python

Before Python 3.6, dictionaries were inherently unordered. A Python dictionary is an implementation of the hash table, which is traditionally an unordered data structure.

As a side effect of the compact dictionary implementation in Python 3.6, dictionaries started to conserve insertion order. From 3.7, that insertion order has been guaranteed.

If you wanted to keep an ordered dictionary as a data structure before compact dictionaries, then you could use OrderedDict from the collections module. Similar to the modern compact dictionary, it also keeps insertion order, but neither type of dictionary sorts itself.

Another alternative for storing an ordered key-value pair data is to store the pairs as a list of tuples. As you’ll see later in the tutorial, using a list of tuples could be the best choice for your data.

An essential point to understand when sorting dictionaries is that even though they conserve insertion order, they’re not considered a sequence. A dictionary is like a set of key-value pairs, and sets are unordered.

Dictionaries also don’t have much reordering functionality. They’re not like lists, where you can insert elements at any position. In the next section, you’ll explore the consequences of this limitation further.

Understanding What Sorting a Dictionary Really Means

Because dictionaries don’t have much reordering functionality, when sorting a dictionary, it’s rarely done in-place. In fact, there are no methods for explicitly moving items in a dictionary.

If you wanted to sort a dictionary in-place, then you’d have to use the del keyword to delete an item from the dictionary and then add it again. Deleting and then adding again effectively moves the key-value pair to the end.

The OrderedDict class has a specific method to move an item to the end or the start, which may make OrderedDict preferable for keeping a sorted dictionary. However, it’s still not very common and isn’t very performant, to say the least.

The typical method for sorting dictionaries is to get a dictionary view, sort it, and then cast the resulting list back into a dictionary. So you effectively go from a dictionary to a list and back into a dictionary. Depending on your use case, you may not need to convert the list back into a dictionary.

Note: Sorted dictionaries aren’t a very common pattern. You’ll explore more about that topic later in the tutorial.

With those preliminaries out of the way, you’ll get to sorting dictionaries in the next section.

Sorting Dictionaries in Python

In this section, you’ll be putting together the components of sorting a dictionary so that, in the end, you can master the most common way of sorting a dictionary:

Python
      
>>> people = {3: "Jim", 2: "Jack", 4: "Jane", 1: "Jill"}

>>> # Sort by key
>>> dict(sorted(people.items()))
{1: 'Jill', 2: 'Jack', 3: 'Jim', 4: 'Jane'}

>>> # Sort by value
>>> dict(sorted(people.items(), key=lambda item: item[1]))
{2: 'Jack', 4: 'Jane', 1: 'Jill', 3: 'Jim'}

Don’t worry if you don’t understand the snippets above—you’ll review it all step-by-step in the following sections. Along the way, you’ll learn how to use the sorted() function with sort keys, lambda functions, and dictionary constructors.

Using the `sorted()` Function

Read the full article at https://realpython.com/sort-python-dictionary/ »

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Documenting Python Projects With Sphinx and Read the Docs

2024-12-10T14:00:00+00:00

Sphinx is a document generation tool that’s become the de facto standard for Python projects. It uses the reStructuredText (RST) markup language to define document structure and styling, and it can output in a wide variety of formats, including HTML, ePub, man pages, and much more. Sphinx is extendable and has plugins for incorporating pydoc comments from your code into your docs and for using MyST Markdown instead of RST.

Read the Docs is a free document hosting site where many Python projects host their documentation. It integrates with GitHub, GitLab, and Bitbucket to automatically pull new documentation sources from your repositories and build their Sphinx sources.

In this video course, you’ll learn how to:

Write your documentation with Sphinx
Structure and style your document with RST syntax
Incorporate your pydoc comments into your documentation
Host your documentation on Read the Docs

With these skills, you’ll be able to write clear, reliable documentation that’ll help your users get the most out of your project.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #231: Good Python Programming Practices When New to the Language

2024-12-06T12:00:00+00:00

What advice would you give to someone moving from another language to Python? What good programming practices are inherent to the language? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Handling or Preventing Errors in Python: LBYL vs EAFP

2024-12-03T14:00:00+00:00

Dealing with errors and exceptional situations is a common requirement in programming. You can either prevent errors before they happen or handle errors after they’ve happened. In general, you’ll have two coding styles matching these strategies: look before you leap (LBYL), and easier to ask forgiveness than permission (EAFP). In this video course, you’ll dive into the questions and considerations surrounding LBYL vs EAFP in Python.

By learning about Python’s LBYL and EAFP coding styles, you’ll be able to decide which strategy and coding style to use when you’re dealing with errors in your code.

In this video course, you’ll learn how to:

Use the LBYL and EAFP styles in your Python code
Understand the pros and cons of LBYL vs EAFP
Decide when to use either LBYL or EAFP

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The Real Python Podcast – Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python

2024-11-29T12:00:00+00:00

What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python.

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Managing Dependencies With Python Poetry

2024-11-26T14:00:00+00:00

When your Python project relies on external packages, you need to make sure you’re using the right version of each package. After an update, a package might not work as it did before. A dependency manager like Python Poetry helps you specify, install, and resolve external packages in your projects. This way, you can be sure that you always work with the correct dependency version on every machine.

In this video course, you’ll learn how to:

Create a new project using Poetry
Add Poetry to an existing project
Configure your project through pyproject.toml
Pin your project’s dependency versions
Install dependencies from a poetry.lock file
Run basic Poetry commands using the Poetry CLI

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #229: The Joy of Tinkering & Python Free-Threading Performance

2024-11-22T12:00:00+00:00

What keeps your spark alive for developing software and learning Python? Do you like to try new frameworks, build toy projects, or collaborate with other developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Quiz: Expression vs Statement in Python: What's the Difference?

2024-11-21T12:00:00+00:00

In this quiz, you’ll test your understanding of Expression vs Statement in Python: What’s the Difference?

By working through this quiz, you’ll revisit the key differences between expressions and statements in Python, and how to use them effectively in your code.

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Quiz: Interacting With Python

2024-11-21T12:00:00+00:00

In this quiz, you’ll test your understanding of the different ways you can interact with Python.

By working through this quiz, you’ll revisit key concepts related to Python interaction in interactive mode using the Read-Eval-Print Loop (REPL), through Python script files, and within Integrated Development Environments (IDEs) and code editors.

You’ll also test your knowledge of some other options that may be useful, such as Jupyter Notebooks.

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Quiz: NumPy Practical Examples: Useful Techniques

2024-11-20T12:00:00+00:00

In this quiz, you’ll test your understanding of the techniques covered in the tutorial NumPy Practical Examples: Useful Techniques.

By working through the questions, you’ll review your understanding of NumPy arrays and also expand on what you learned in the tutorial.

You’ll need to do some research outside of the tutorial to answer all the questions. Embrace this challenge and let it take you on a learning journey.

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Working With TOML and Python

2024-11-19T14:00:00+00:00

TOML—Tom’s Obvious Minimal Language—is a reasonably new configuration file format that the Python community has embraced over the last couple of years. TOML plays an essential part in the Python ecosystem. Many of your favorite tools rely on TOML for configuration, and you’ll use pyproject.toml when you build and distribute your own packages.

In this video course, you’ll learn more about TOML and how you can use it. In particular, you’ll:

Learn and understand the syntax of TOML
Use tomli and tomllib to parse TOML documents
Use tomli_w to write data structures as TOML
Use tomlkit when you need more control over your TOML files

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #228: Maintaining the Foundations of Python & Cautionary Tales

2024-11-15T12:00:00+00:00

How do you build a sustainable open-source project and community? What lessons can be learned from Python's history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation.

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Quiz: Namespaces and Scope in Python

2024-11-14T12:00:00+00:00

In this quiz, you’ll test your understanding of Python Namespaces and Scope.

You’ll revisit how Python organizes symbolic names and objects in namespaces, when Python creates a new namespace, how namespaces are implemented, and how variable scope determines symbolic name visibility.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Python Dictionary Comprehensions: How and When to Use Them

2024-11-13T12:00:00+00:00

In this quiz, you’ll test your understanding of Python Dictionary Comprehensions: How and When to Use Them.

Understanding dictionary comprehensions is crucial for you as a Python developer because they provide a Pythonic tool for dictionary manipulation and can be a valuable addition to your programming toolkit.

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Quiz: Basic Input and Output in Python

2024-11-13T12:00:00+00:00

In this quiz, you’ll test your understanding of how to use Python’s built-in functions input() and print() for basic input and output operations.

You’ll also revisit how to use readline to improve the user experience when collecting input, and how to format output using the sep and end keyword arguments of print().

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Formatting Floats Inside Python F-Strings

2024-11-12T14:00:00+00:00

You’ll often need to format and round a Python float to display the results of your calculations neatly within strings. In earlier versions of Python, this was a messy thing to do because you needed to round your numbers first and then use either string concatenation or the old string formatting technique to do this for you.

Since Python 3.6, the literal string interpolation, more commonly known as a formatted string literal or f-string, allows you to customize the content of your strings in a more readable way.

An f-string is a literal string prefixed with a lowercase or uppercase letter f and contains zero or more replacement fields enclosed within a pair of curly braces {...}. Each field contains an expression that produces a value. You can calculate the field’s content, but you can also use function calls or even variables.

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The Real Python Podcast – Episode #227: New PEPs: Template Strings & External Wheel Hosting

2024-11-08T12:00:00+00:00

Have you wanted the flexibility of f-strings but need safety checks in place? What if you could have deferred evaluation for logging or avoiding injection attacks? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Introduction to Web Scraping With Python

2024-11-05T14:00:00+00:00

Web scraping is the process of collecting and parsing raw data from the Web, and the Python community has come up with some pretty powerful web scraping tools.

The Internet hosts perhaps the greatest source of information on the planet. Many disciplines, such as data science, business intelligence, and investigative reporting, can benefit enormously from collecting and analyzing data from websites.

In this video course, you’ll learn how to:

Parse website data using string methods and regular expressions
Parse website data using an HTML parser
Interact with forms and other website components

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Variables in Python: Usage and Best Practices

2024-11-05T12:00:00+00:00

In this quiz, you’ll test your understanding of Variables in Python: Usage and Best Practices.

By working through this quiz, you’ll revisit how to create and assign values to variables, change a variable’s data type dynamically, use variables to create expressions, counters, accumulators, and Boolean flags, follow best practices for naming variables, and create, access, and use variables in their scopes.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #226: PySheets: Spreadsheets in the Browser Using PyScript

2024-11-01T12:00:00+00:00

What goes into building a spreadsheet application in Python that runs in the browser? How do you make it launch quickly, and where do you store the cells of data? This week on the show, we speak with Chris Laffra about his project, PySheets, and his book "Communication for Engineers."

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Python's Magic Methods in Classes

2024-10-29T14:00:00+00:00

As a Python developer who wants to harness the power of object-oriented programming, you’ll love to learn how to customize your classes using special methods, also known as magic methods or dunder methods. A special method is a method whose name starts and ends with a double underscore. These methods have special meanings in Python.

Python automatically calls magic methods as a response to certain operations, such as instantiation, sequence indexing, attribute managing, and much more. Magic methods support core object-oriented features in Python, so learning about them is fundamental for you as a Python programmer.

In this video course, you’ll:

Learn what Python’s special or magic methods are
Understand the magic behind magic methods in Python
Customize different behaviors of your custom classes with special methods

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Using .repr() vs .str() in Python

2024-10-29T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s .__repr__() and .__str__() special methods. These methods allow you to control how a program displays an object, making your classes more readable and easier to debug and maintain.

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Quiz: Beautiful Soup: Build a Web Scraper With Python

2024-10-28T12:00:00+00:00

In this quiz, you’ll test your understanding of web scraping with Python, Requests, and Beautiful Soup.

By working through this quiz, you’ll revisit how to inspect the HTML structure of your target site with your browser’s developer tools, decipher data encoded in URLs, use Requests and Beautiful Soup for scraping and parsing internet data, and gain an understanding of what a web scraping pipeline looks like.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #225: Python Getting Faster and Leaner & Ideas for Django Projects

2024-10-25T12:00:00+00:00

What changes are happening under the hood in the latest versions of Python? How are these updates laying the groundwork for a faster Python in the coming years? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: How to Reset a pandas DataFrame Index

2024-10-25T12:00:00+00:00

In this quiz, you’ll test your understanding of how to reset a pandas DataFrame index.

By working through the questions, you’ll review your knowledge of indexing and also expand on what you learned in the tutorial.

You’ll need to do some research outside of the tutorial to answer all the questions. Embrace this challenge and let it take you on a learning journey.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Understanding Python's Global Interpreter Lock (GIL)

2024-10-22T14:00:00+00:00

The Python Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter.

This means that only one thread can be in a state of execution at any point in time. The impact of the GIL isn’t visible to developers who execute single-threaded programs, but it can be a performance bottleneck in CPU-bound and multi-threaded code.

Since the GIL allows only one thread to execute at a time even in a multi-threaded architecture with more than one CPU core, the GIL has gained a reputation as an “infamous” feature of Python.

In this video course you’ll learn how the GIL affects the performance of your Python programs, and how you can mitigate the impact it might have on your code.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries

2024-10-18T12:00:00+00:00

How does a Python tool support all types of DataFrames and their various features? Could a lightweight library be used to add compatibility for newer formats like Polars or PyArrow? This week on the show, we speak with Marco Gorelli about his project, Narwhals.

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Using Type Hints for Multiple Return Types in Python

2024-10-15T14:00:00+00:00

In Python, type hinting is an optional yet useful feature for making your code easier to read, reason about, and debug. With type hints, you let other developers know the expected data types for variables, function arguments, and return values. As you write code for applications that require greater flexibility, you may need to specify multiple return types to make your code more robust and adaptable to different situations.

You’ll encounter different use cases where you may want to annotate multiple return types within a single function in Python. In other words, the data returned can vary in type. In this video course, you’ll walk through examples of how to specify multiple return types for a function that parses a string from an email address to grab the domain name.

In addition, you’ll see examples of how to specify type hints for callback functions or functions that take another function as input. With these examples, you’ll be ready to express type hints in functional programming.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #223: Exploring the New Features of Python 3.13

2024-10-11T12:00:00+00:00

Python 3.13 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. This year, Geir Arne coordinated a series of preview articles with members of the Real Python team and a showcase tutorial, "Python 3.13: Cool New Features for You to Try." Christopher's video course "What's New in Python 3.13" covers the topics from the article and shows the new features in action.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #222: Using Virtual Environments in Docker & Comparing Python Dev Tools

2024-09-27T12:00:00+00:00

Should you use a Python virtual environment in a Docker container? What are the advantages of using the same development practices locally and inside a container? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Real Python

Programming Sockets in Python

Dictionaries in Python

Getting Started With Python Dictionaries

Object-Oriented Programming (OOP) in Python

What Is Object-Oriented Programming in Python?

How Do You Define a Class in Python?

Build Enumerations of Constants With Python's Enum

Getting to Know Enumerations in Python

Creating Enumerations With Python’s Enum

Dependency Management With Python Poetry

Take Care of Prerequisites

Learn the Relevant Terminology

Install Poetry on Your Computer

Python's property(): Add Managed Attributes to Your Classes

Managing Attributes in Your Classes

The Getter and Setter Approach in Python

Python Classes: The Power of Object-Oriented Programming

Getting Started With Python Classes

Defining a Class in Python

Primer on Python Decorators

Python Functions

First-Class Objects

Inner Functions

YAML: The Missing Battery in Python

Taking a Crash Course in YAML

Historical Context

Comparison With XML and JSON

Build Command-Line Interfaces With Python's argparse

Getting to Know Command-Line Interfaces

Command-Line Interfaces (CLIs)

Commands, Arguments, Options, Parameters, and Subcommands

Sorting a Python Dictionary: Values, Keys, and More

Rediscovering Dictionary Order in Python

Understanding What Sorting a Dictionary Really Means

Sorting Dictionaries in Python

Using the sorted() Function

Documenting Python Projects With Sphinx and Read the Docs

The Real Python Podcast – Episode #231: Good Python Programming Practices When New to the Language

Handling or Preventing Errors in Python: LBYL vs EAFP

The Real Python Podcast – Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python

Managing Dependencies With Python Poetry

The Real Python Podcast – Episode #229: The Joy of Tinkering & Python Free-Threading Performance

Quiz: Expression vs Statement in Python: What's the Difference?

Quiz: Interacting With Python

Quiz: NumPy Practical Examples: Useful Techniques

Working With TOML and Python

The Real Python Podcast – Episode #228: Maintaining the Foundations of Python & Cautionary Tales

Quiz: Namespaces and Scope in Python

Quiz: Python Dictionary Comprehensions: How and When to Use Them

Quiz: Basic Input and Output in Python

Formatting Floats Inside Python F-Strings

The Real Python Podcast – Episode #227: New PEPs: Template Strings & External Wheel Hosting

Introduction to Web Scraping With Python

Quiz: Variables in Python: Usage and Best Practices

The Real Python Podcast – Episode #226: PySheets: Spreadsheets in the Browser Using PyScript

Python's Magic Methods in Classes

Quiz: Using .__repr__() vs .__str__() in Python

Quiz: Beautiful Soup: Build a Web Scraper With Python

The Real Python Podcast – Episode #225: Python Getting Faster and Leaner & Ideas for Django Projects

Quiz: How to Reset a pandas DataFrame Index

Understanding Python's Global Interpreter Lock (GIL)

The Real Python Podcast – Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries

Using Type Hints for Multiple Return Types in Python

The Real Python Podcast – Episode #223: Exploring the New Features of Python 3.13

The Real Python Podcast – Episode #222: Using Virtual Environments in Docker & Comparing Python Dev Tools

Creating Enumerations With Python’s `Enum`

Using the `sorted()` Function

Quiz: Using .repr() vs .str() in Python