Real Python

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 ]

Python News Roundup: December 2024

2024-12-09T14:00:00+00:00

The Python community has kept up its momentum this month, delivering a host of exciting updates. From the promising improvements to template strings in PEP 750 to the release of Python 3.14.0a2, innovation is front and center. Developers are exploring new tools like partial validation in Pydantic 2.10, while popular projects adapt to the end of life of Python 3.8.

December also welcomes the return of the beloved Advent of Code, challenging programmers and problem solvers with daily puzzles. And for those planning ahead, the PyCon 2025 call for proposals is closing soon, marking a final opportunity to contribute to the community’s largest annual gathering.

Whether you’re diving into cutting-edge features, revisiting Python fundamentals, or connecting with fellow developers, there’s something for everyone this month. Let’s round up the most important developments of last month and see how they can inspire your next Python project!

PEP 750 – Template Strings Updated

The authors of PEP 750, which you might have first encountered introducing tag strings, have revised and significantly updated their proposal.

The central point of the PEP is now renamed as template strings, or t-strings, and aligns its syntax with the beloved f-strings:

Python
      
    
planet = "World"
greeting = t"Hello, {planet}!"

Looks very familiar! The main practical difference to f-strings is that t-strings are lazily evaluated, which opens the door to common string interpolation scenarios that f-strings can’t handle, such as:

Performance-optimized logging
Internationalization
Template reuse
HTML templating

All of the above are examples of string interpolation that requires lazy evaluation.

Note: Template reuse can come in handy, for example, when you’re creating template prompts in LLM-powered applications and application frameworks.

If the PEP gets accepted and implemented, then you’ll be able to use t-strings where you currently have to resort to str.format(), using a Template object from the string module, or even the older modulo (%) string formatting approach.

As the Python community continues to innovate, PEP 750 is a testament to the ongoing efforts to enhance language features while keeping them intuitive for developers. If you want to dive deeper into this proposal and its potential impact, then you can read more in the detailed examples section of the PEP.

Python 3.14.0a2 Released

The Python 3.14 team rolled out the second alpha release of Python 3.14, version 3.14.0a2. This release is part of the ongoing development process for Python 3.14, with a total of seven alpha releases planned.

Read the full article at https://realpython.com/python-news-december-2024/ »

[ 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 ]

Using and Creating Global Variables in Your Python Functions

2024-12-08T14:00:00+00:00

In Python, global variables are accessible across your entire program, including within functions. Understanding how Python handles global variables is key to writing efficient code. This tutorial will guide you through accessing and modifying global variables in Python functions using the global keyword and the globals() function. You’ll also learn to manage scope and avoid potential conflicts between local and global variables.

You’ll explore how to create global variables inside functions and apply strategies to minimize their use, ensuring your code remains clean and maintainable. After reading this tutorial, you’ll be adept at managing global variables and understanding their impact on your Python code.

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

A global variable in Python is a variable defined at the module level, accessible throughout the program.
Accessing and modifying global variables inside Python functions can be achieved using the global keyword or the globals() function.
Python handles name conflicts by searching scopes from local to built-in, potentially causing name shadowing challenges.
Creating global variables inside a function is possible using the global keyword or globals(), but it’s generally not recommended.
Strategies to avoid global variables include using constants, passing arguments, and employing classes and methods to encapsulate state.

To follow along with this tutorial, you should have a solid understanding of Python programming, including fundamental concepts such as variables, data types, scope, mutability, functions, and classes.

Get Your Code: Click here to download the free sample code that you’ll use to understand when and how to work with global variables in your Python functions.

Take the Quiz: Test your knowledge with our interactive “Using and Creating Global Variables in Your Python Functions” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Using and Creating Global Variables in Your Python Functions

In this quiz, you'll test your understanding of how to use global variables in Python functions. With this knowledge, you'll be able to share data across an entire program, modify and create global variables within functions, and understand when to avoid using global variables.

Using Global Variables in Python Functions

Global variables are those that you can access and modify from anywhere in your code. In Python, you’ll typically define global variables at the module level. So, the containing module is their scope.

Note: You can also define global variables inside functions, as you’ll learn in the section Creating Global Variables Inside a Function.

Once you’ve defined a global variable, you can use it from within the module itself or from within other modules in your code. You can also use global variables in your functions. However, those cases can get a bit confusing because of differences between accessing and modifying global variables in functions.

To understand these differences, consider that Python can look for variables in four different scopes:

The local, or function-level, scope, which exists inside functions
The enclosing, or non-local, scope, which appears in nested functions
The global scope, which exists at the module level
The built-in scope, which is a special scope for Python’s built-in names

To illustrate, say that you’re inside an inner function. In that case, Python can look for names in all four scopes.

When you access a variable in that inner function, Python first looks inside that function. If the variable doesn’t exist there, then Python continues with the enclosing scope of the outer function. If the variable isn’t defined there either, then Python moves to the global and built-in scopes in that order. If Python finds the variable, then you get the value back. Otherwise, you get a NameError:

Python
      
        
      
    
>>> # Global scope

>>> def outer_func():
...     # Non-local scope
...     def inner_func():
...         # Local scope
...         print(some_variable)
...     inner_func()
...

>>> outer_func()
Traceback (most recent call last):
    ...
NameError: name 'some_variable' is not defined

>>> some_variable = "Hello from global scope!"
>>> outer_func()
Hello from global scope!

When you launch an interactive session, it starts off at the module level of global scope. In this example, you have outer_func(), which defines inner_func() as a nested function. From the perspective of this nested function, its own code block represents the local scope, while the outer_func() code block before the call to inner_func() represents the non-local scope.

If you call outer_func() without defining some_variable in either of your current scopes, then you get a NameError exception because the name isn’t defined.

If you define some_variable in the global scope and then call outer_func(), then you get Hello! on your screen. Internally, Python has searched the local, non-local, and global scopes to find some_variable and print its content. Note that you can define this variable in any of the three scopes, and Python will find it.

This search mechanism makes it possible to use global variables from inside functions. However, while taking advantage of this feature, you can face a few issues. For example, accessing a variable works, but directly modifying a variable doesn’t work:

Python
      
        
      
    
>>> number = 42

>>> def access_number():
...     return number
...

>>> access_number()
42

>>> def modify_number():
...     number = 7
...

>>> modify_number()
>>> number
42

The access_number() function works fine. It looks for number and finds it in the global scope. In contrast, modify_number() doesn’t work as expected. Why doesn’t this function update the value of your global variable, number? The problem is the scope of the variable. You can’t directly modify a variable from a high-level scope like global in a lower-level scope like local.

Internally, Python assumes that any name directly assigned within a function is local to that function. Therefore, the local name, number, shadows its global sibling.

In this sense, global variables behave as read-only names. You can access their values, but you can’t modify them.

Note: The discussion about modifying global variables inside functions revolves around assignment operations rather than in-place mutations of mutable objects. You’ll learn about the effects of mutability on global variables in the section Understanding How Mutability Affects Global Variables.

Read the full article at https://realpython.com/python-use-global-variable-in-function/ »

[ 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 ]

Effective Python Testing With pytest

2024-12-08T14:00:00+00:00

pytest is a popular testing framework for Python that simplifies the process of writing and executing tests. To start using pytest, install it with pip in a virtual environment. pytest offers several advantages over unittest that ships with Python, such as less boilerplate code, more readable output, and a rich plugin ecosystem.

pytest comes packed with features to boost your productivity. Its fixtures allow for explicit dependency declarations, making tests more understandable and reducing implicit dependencies. Parametrization in pytest helps prevent redundant test code by enabling multiple test cases from a single test function definition. This framework is highly customizable, so you can tailor it to your project’s needs.

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

Using pytest requires installing it with pip in a virtual environment to set up the pytest command.
pytest allows for less code, easier readability, and more features compared to unittest.
Managing test dependencies and state with pytest is made efficient through the use of fixtures, which provide explicit dependency declarations.
Parametrization in pytest helps avoid redundant test code by allowing multiple test scenarios from a single test function.
Assertion introspection in pytest provides detailed information about failures in the test report.

Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you’ll need to take your Python skills to the next level.

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

Interactive Quiz

Effective Testing with Pytest

In this quiz, you'll test your understanding of pytest, a Python testing tool. With this knowledge, you'll be able to write more efficient and effective tests, ensuring your code behaves as expected.

How to Install `pytest`

To follow along with some of the examples in this tutorial, you’ll need to install pytest. As most Python packages, pytest is available on PyPI. You can install it in a virtual environment using pip:

Windows PowerShell
      
PS> python -m venv venv
PS> .\venv\Scripts\activate
(venv) PS> python -m pip install pytest

Shell
      
$ python -m venv venv
$ source venv/bin/activate
(venv) $ python -m pip install pytest

The pytest command will now be available in your installation environment.

What Makes `pytest` So Useful?

If you’ve written unit tests for your Python code before, then you may have used Python’s built-in unittest module. unittest provides a solid base on which to build your test suite, but it has a few shortcomings.

A number of third-party testing frameworks attempt to address some of the issues with unittest, and pytest has proven to be one of the most popular. pytest is a feature-rich, plugin-based ecosystem for testing your Python code.

If you haven’t had the pleasure of using pytest yet, then you’re in for a treat! Its philosophy and features will make your testing experience more productive and enjoyable. With pytest, common tasks require less code and advanced tasks can be achieved through a variety of time-saving commands and plugins. It’ll even run your existing tests out of the box, including those written with unittest.

As with most frameworks, some development patterns that make sense when you first start using pytest can start causing pains as your test suite grows. This tutorial will help you understand some of the tools pytest provides to keep your testing efficient and effective even as it scales.

Less Boilerplate

Most functional tests follow the Arrange-Act-Assert model:

Arrange, or set up, the conditions for the test
Act by calling some function or method
Assert that some end condition is true

Testing frameworks typically hook into your test’s assertions so that they can provide information when an assertion fails. unittest, for example, provides a number of helpful assertion utilities out of the box. However, even a small set of tests requires a fair amount of boilerplate code.

Imagine you’d like to write a test suite just to make sure that unittest is working properly in your project. You might want to write one test that always passes and one that always fails:

Pythontest_with_unittest.py
      
    
from unittest import TestCase

class TryTesting(TestCase):
    def test_always_passes(self):
        self.assertTrue(True)

    def test_always_fails(self):
        self.assertTrue(False)

You can then run those tests from the command line using the discover option of unittest:

Shell
      
        
      
    
(venv) $ python -m unittest discover
F.
======================================================================
FAIL: test_always_fails (test_with_unittest.TryTesting)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "...\effective-python-testing-with-pytest\test_with_unittest.py",
  line 10, in test_always_fails
    self.assertTrue(False)
AssertionError: False is not true

----------------------------------------------------------------------

Ran 2 tests in 0.006s

FAILED (failures=1)

As expected, one test passed and one failed. You’ve proven that unittest is working, but look at what you had to do:

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

[ 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 ]

Python Timer Functions: Three Ways to Monitor Your Code

2024-12-08T14:00:00+00:00

A timer is a powerful tool for monitoring the performance of your Python code. By using the time.perf_counter() function, you can measure execution time with exceptional precision, making it ideal for benchmarking. Using a timer involves recording timestamps before and after a specific code block and calculating the time difference to determine how long your code took to run.

In this tutorial, you’ll explore three different approaches to implementing timers: classes, decorators, and context managers. Each method offers unique advantages, and you’ll learn when and how to use them to achieve optimal results. Plus, you’ll have a fully functional Python timer that can be applied to any program to measure execution time efficiently.

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

time.perf_counter() is the best choice for accurate timing in Python due to its high resolution.
You can create custom timer classes to encapsulate timing logic and reuse it across multiple parts of your program.
Using decorators lets you seamlessly add timing functionality to existing functions without altering their code.
You can leverage context managers to neatly measure execution time in specific code blocks, improving both resource management and code clarity.

Along the way, you’ll gain deeper insights into how classes, decorators, and context managers work in Python. As you explore real-world examples, you’ll discover how these concepts can not only help you measure code performance but also enhance your overall Python programming skills.

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.

Python Timers

First, you’ll take a look at some example code that you’ll use throughout the tutorial. Later, you’ll add a Python timer to this code to monitor its performance. You’ll also learn some of the simplest ways to measure the running time of this example.

Python Timer Functions

If you check out the built-in time module in Python, then you’ll notice several functions that can measure time:

Python 3.7 introduced several new functions, like thread_time(), as well as nanosecond versions of all the functions above, named with an _ns suffix. For example, perf_counter_ns() is the nanosecond version of perf_counter(). You’ll learn more about these functions later. For now, note what the documentation has to say about perf_counter():

Return the value (in fractional seconds) of a performance counter, i.e. a clock with the highest available resolution to measure a short duration. (Source)

First, you’ll use perf_counter() to create a Python timer. Later, you’ll compare this with other Python timer functions and learn why perf_counter() is usually the best choice.

Example: Download Tutorials

To better compare the different ways that you can add a Python timer to your code, you’ll apply different Python timer functions to the same code example throughout this tutorial. If you already have code that you’d like to measure, then feel free to follow the examples with that instead.

The example that you’ll use in this tutorial is a short function that uses the realpython-reader package to download the latest tutorials available here on Real Python. To learn more about the Real Python Reader and how it works, check out How to Publish an Open-Source Python Package to PyPI. You can install realpython-reader on your system with pip:

Shell
      
$ python -m pip install realpython-reader

Then, you can import the package as reader.

You’ll store the example in a file named latest_tutorial.py. The code consists of one function that downloads and prints the latest tutorial from Real Python:

Pythonlatest_tutorial.py
      
    
from reader import feed

def main():
    """Download and print the latest tutorial from Real Python"""
    tutorial = feed.get_article(0)
    print(tutorial)

if __name__ == "__main__":
    main()

realpython-reader handles most of the hard work:

Line 1 imports feed from realpython-reader. This module contains functionality for downloading tutorials from the Real Python feed.
Line 5 downloads the latest tutorial from Real Python. The number 0 is an offset, where 0 means the most recent tutorial, 1 is the previous tutorial, and so on.
Line 7 prints the tutorial to the console.
Line 9 calls main() when you run the script.

When you run this example, your output will typically look something like this:

Shell
      
$ python latest_tutorial.py
# Python Timer Functions: Three Ways to Monitor Your Code

A timer is a powerful tool for monitoring the performance of your Python
code. By using the `time.perf_counter()` function, you can measure execution
time with exceptional precision, making it ideal for benchmarking. Using a
timer involves recording timestamps before and after a specific code block and
calculating the time difference to determine how long your code took to run.

[ ... ]

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

* * *

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

[ 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 ]

Asynchronous Tasks With Django and Celery

2024-12-08T14:00:00+00:00

Integrating Celery with your Django application allows you to offload time-consuming tasks, ensuring smooth user experiences. Celery is a distributed task queue that processes tasks asynchronously, preventing delays in your web app’s response time. By using Celery with Django, you can efficiently manage tasks like sending emails, processing images, and analyzing data without slowing down your application.

Celery works by leveraging a message broker like Redis to communicate between your Django app and Celery workers. This setup enables you to handle tasks outside the main execution thread, improving your app’s performance.

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

Celery is a distributed task queue that handles tasks outside the main Django app flow.
Python’s Celery excels at offloading work and scheduling tasks independently.
Using Celery in Django helps maintain app responsiveness during time-intensive tasks.
Configuring Celery in Django involves setting up a message broker and defining tasks.
A Celery worker performs tasks asynchronously, freeing up the main app.
Running a task in Celery requires calling the task with .delay() or .apply_async().
Celery is not a message queue but uses a message broker like Redis for communication.

You’re in the right place if you’ve never used Celery in a Django app before, or if you’ve peeked into Celery’s documentation but couldn’t find your way around. You’ll learn how to configure Celery in Django to handle tasks asynchronously, ensuring your application remains responsive and efficient.

To focus this tutorial on the essentials, you’ll integrate Celery into an existing Django app. Go ahead and download the code for that app so that you can follow along:

Get Your Code: Click here to download free the sample code you’ll use to integrate Celery into your Django app.

Python Celery Basics

Celery is a distributed task queue that can collect, record, schedule, and perform tasks outside of your main program.

Note: Celery dropped support for Windows in version 4, so while you may still be able to get it to work on Windows, you’re better off using a different task queue, such as huey or Dramatiq, instead.

In this tutorial, you’ll focus on using Celery on UNIX systems, so if you’re trying to set up a distributed task queue on Windows, then this might not be the right tutorial for you.

To receive tasks from your program and send results to a back end, Celery requires a message broker for communication. Redis and RabbitMQ are two message brokers that developers often use together with Celery.

In this tutorial, you’ll use Redis as the message broker. To challenge yourself, you can stray from the instructions and use RabbitMQ as a message broker instead.

If you want to keep track of the results of your task runs, then you also need to set up a results back end database.

Note: Connecting Celery to a results back end is optional. Once you instruct Celery to run a task, it’ll do its duty whether you keep track of the task result or not.

However, keeping a record of all task results is often helpful, especially if you’re distributing tasks to multiple queues. To persist information about task results, you need a database back end.

You can use many different databases to keep track of Celery task results. In this tutorial, you’ll work with Redis both as a message broker and as a results back end. By using Redis, you limit the dependencies that you need to install because it can take on both roles.

You won’t do any work with the recorded task results in the scope of this tutorial. However, as a next step, you could inspect the results with the Redis command-line interface (CLI) or pull information into a dedicated page in your Django project.

Why Use Celery?

There are two main reasons why most developers want to start using Celery:

Offloading work from your app to distributed processes that can run independently of your app
Scheduling task execution at a specific time, sometimes as recurring events

Celery is an excellent choice for both of these use cases. It defines itself as “a task queue with focus on real-time processing, while also supporting task scheduling” (Source).

Even though both of these functionalities are part of Celery, they’re often addressed separately:

Celery workers are worker processes that run tasks independently from one another and outside the context of your main service.
Celery beat is a scheduler that orchestrates when to run tasks. You can use it to schedule periodic tasks as well.

Celery workers are the backbone of Celery. Even if you aim to schedule recurring tasks using Celery beat, a Celery worker will pick up your instructions and handle them at the scheduled time. What Celery beat adds to the mix is a time-based scheduler for Celery workers.

In this tutorial, you’ll learn how to integrate Celery with Django to perform operations asynchronously from the main execution thread of your app using Celery workers.

You won’t tackle task scheduling with Celery beat in this tutorial, but once you understand the basics of Celery tasks, you’ll be well equipped to set up periodic tasks with Celery beat.

Read the full article at https://realpython.com/asynchronous-tasks-with-django-and-celery/ »

[ 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 ]

How to Run Your Python Scripts and Code

2024-12-08T14:00:00+00:00

Running a Python script is a fundamental task for any Python developer. You can execute a Python .py file through various methods depending on your environment and platform. On Windows, Linux, and macOS, use the command line by typing python script_name.py to run your script. You can also use the python command with the -m option to execute modules. This tutorial covers these methods and more, ensuring you can run Python scripts efficiently.

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

Running a Python .py script involves using the python command followed by the script’s filename in the terminal or command prompt.
Running Python from the command prompt requires you to open the command prompt, navigate to the script’s directory, and execute it using python script_name.py.
Running a .py file in Windows can be done directly from the command prompt or by double-clicking the file if Python is associated with .py files.
Running a Python script without Python installed is possible by using online interpreters or converting scripts to executables, but it’s more flexible to install Python and run scripts natively.

To get the most out of this tutorial, you should know the basics of working with your operating system’s terminal and file manager. It’d also be beneficial for you to be familiar with a Python-friendly IDE or code editor and with the standard Python REPL (Read-Eval-Print Loop).

Free Download: Get a sample chapter from Python Tricks: The Book that shows you Python’s best practices with simple examples you can apply instantly to write more beautiful + Pythonic code.

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

Interactive Quiz

How to Run Your Python Scripts

One of the most important skills you need to build as a Python developer is to be able to run Python scripts and code. Test your understanding on how good you are with running your code.

What Scripts and Modules Are

In computing, the term script refers to a text file containing a logical sequence of orders that you can run to accomplish a specific task. These orders are typically expressed in a scripting language, which is a programming language that allows you to manipulate, customize, and automate tasks.

Scripting languages are usually interpreted at runtime rather than compiled. So, scripts are typically run by some kind of interpreter, which is responsible for executing each order in a sequence.

Python is an interpreted language. Because of that, Python programs are commonly called scripts. However, this terminology isn’t completely accurate because Python programs can be way more complex than a simple, sequential script.

In general, a file containing executable Python code is called a script—or an entry-point script in more complex applications—which is a common term for a top-level program. On the other hand, a file containing Python code that’s designed to be imported and used from another Python file is called a module.

So, the main difference between a module and a script is that modules store importable code while scripts hold executable code.

Note: Importable code is code that defines something but doesn’t perform a specific action. Some examples include function and class definitions. In contrast, executable code is code that performs specific actions. Some examples include function calls, loops, and conditionals.

In the following sections, you’ll learn how to run Python scripts, programs, and code in general. To kick things off, you’ll start by learning how to run them from your operating system’s command line or terminal.

How to Run Python Scripts From the Command Line

In Python programming, you’ll write programs in plain text files. By convention, files containing Python code use the .py extension, and there’s no distinction between scripts or executable programs and modules. All of them will use the same extension.

Note: On Windows systems, the extension can also be .pyw for those applications that should use the pythonw.exe launcher.

To create a Python script, you can use any Python-friendly code editor or IDE (integrated development environment). To keep moving forward in this tutorial, you’ll need to create a basic script, so fire up your favorite text editor and create a new hello.py file containing the following code:

Pythonhello.py
      
print("Hello, World!")

This is the classic "Hello, World!" program in Python. The executable code consists of a call to the built-in print() function that displays the "Hello, World!" message on your screen.

With this small program ready, you’re ready to learn different ways to run it. You’ll start by running the program from your command line, which is arguably the most commonly used approach to running scripts.

Using the `python` Command

To run Python scripts with the python command, you need to open a command-line window and type in the word python followed by the path to your target script:

Windows PowerShell
      
PS> python .\hello.py
Hello, World!

PS> py .\hello.py
Hello, World!

Shell
      
$ python ./hello.py
Hello, World!

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

[ 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 ]

When to Use a List Comprehension in Python

2024-12-07T14:00:00+00:00

List comprehensions in Python provide a concise way to create lists by embedding a loop and optional conditional logic in a single line. You use a list comprehension to transform and filter elements from an iterable efficiently. It allows you to replace complex loops and map() functions with more readable and often faster expressions. By understanding list comprehensions, you can optimize your code for better performance and clarity.

You can add conditional logic to a list comprehension in Python by appending an if statement to the end of the expression. This allows you to filter elements based on a condition, effectively replacing the need for filter() in many cases. Additionally, you can use conditional expressions at the beginning to choose between different outcomes, enhancing the versatility of your list comprehensions.

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

A list comprehension in Python is a tool for creating lists by iterating over an iterable and optionally applying a condition.
You should use list comprehensions instead of loops when you want concise, readable code that performs transformations or filtering.
You add conditional logic to a list comprehension by including an if statement within the comprehension.
A list comprehension can be faster than a for loop because it’s optimized for performance by Python’s internal mechanisms.
A Python list comprehension is not lazy—it generates and stores the entire list in memory eagerly.
The difference between list comprehensions and map() is that the former creates a list, while the latter returns a map object, which is iterable.
To optimize performance with list comprehensions, use them for small to medium-sized lists and profile different approaches to choose the fastest one.

In this tutorial, you’ll explore how to leverage list comprehensions to simplify your code. You’ll also gain an understanding of the trade-offs that come with using them so that you can determine when other approaches are preferable.

Get Your Code: Click here to download the free code that shows you how and when to use list comprehensions in Python.

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

Interactive Quiz

When to Use a List Comprehension in Python

In this quiz, you'll test your understanding of Python list comprehensions. You'll revisit how to rewrite loops as list comprehensions, how to choose between comprehensions and loops, and how to use conditional logic in your comprehensions.

Transforming Lists in Python

There are a few different ways to create and add items to a lists in Python. In this section, you’ll explore for loops and the map() function to perform these tasks. Then, you’ll move on to learn about how to use list comprehensions and when list comprehensions can benefit your Python program.

Use `for` Loops

The most common type of loop is the for loop. You can use a for loop to create a list of elements in three steps:

Instantiate an empty list.
Loop over an iterable or range of elements.
Append each element to the end of the list.

If you want to create a list containing the first ten perfect squares, then you can complete these steps in three lines of code:

Python
      
        
      
    
>>> squares = []
>>> for number in range(10):
...     squares.append(number * number)
...
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Here, you instantiate an empty list, squares. Then, you use a for loop to iterate over range(10). Finally, you multiply each number by itself and append the result to the end of the list.

Work With `map` Objects

For an alternative approach that’s based in functional programming, you can use map(). You pass in a function and an iterable, and map() will create an object. This object contains the result that you’d get from running each iterable element through the supplied function.

As an example, consider a situation in which you need to calculate the price after tax for a list of transactions:

Python
      
        
      
    
>>> prices = [1.09, 23.56, 57.84, 4.56, 6.78]
>>> TAX_RATE = .08
>>> def get_price_with_tax(price):
...     return price * (1 + TAX_RATE)
...

>>> final_prices = map(get_price_with_tax, prices)
>>> final_prices
<map object at 0x7f34da341f90>

>>> list(final_prices)
[1.1772000000000002, 25.4448, 62.467200000000005, 4.9248, 7.322400000000001]

Here, you have an iterable, prices, and a function, get_price_with_tax(). You pass both of these arguments to map() and store the resulting map object in final_prices. Finally, you convert final_prices into a list using list().

Leverage List Comprehensions

List comprehensions are a third way of making or transforming lists. With this elegant approach, you could rewrite the for loop from the first example in just a single line of code:

Python
      
>>> squares = [number * number for number in range(10)]
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Rather than creating an empty list and adding each element to the end, you simply define the list and its contents at the same time by following this format:

Python Syntax
      
new_list = [expression for member in iterable]

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

[ 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 ]

How to Round Numbers in Python

2024-12-07T14:00:00+00:00

Rounding numbers in Python is an essential task, especially when dealing with data precision. Python’s built-in round() function uses the rounding half to even strategy, which rounds numbers like 2.5 to 2 and 3.5 to 4. This method helps minimize rounding bias in datasets. To round numbers to specific decimal places, you can use the round() function with a second argument specifying the number of decimals.

For more advanced rounding strategies, you can explore Python’s decimal module or use NumPy and pandas for data science applications. NumPy arrays and pandas DataFrames offer methods for rounding numbers efficiently. In NumPy, you can use functions like np.round(), np.ceil(), np.floor(), and np.trunc() to apply different rounding strategies. For pandas, the df.round() method allows rounding of entire DataFrames or specific columns.

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

Python uses the rounding half to even strategy, where ties round to the nearest even number.
Python’s default rounding strategy minimizes rounding bias in large datasets.
You can round numbers to specific decimal places using Python’s round() function with a second argument.
Different rounding strategies can be applied using Python’s decimal module or custom functions for precision control.
NumPy and pandas provide methods for rounding numbers in arrays and DataFrames, offering flexibility in data manipulation.

You won’t get a treatise on numeric precision in computing, although you’ll touch briefly on the subject. Only a familiarity with the fundamentals of Python is necessary, and the math should feel familiar if you’ve had high school algebra.

You’ll start by looking at Python’s built-in rounding mechanism.

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

Interactive Quiz

Rounding Numbers in Python

Test your knowledge of rounding numbers in Python.

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

Python’s Built-in `round()` Function

Python has a built-in round() function that takes two numeric arguments, n and ndigits, and returns the number n rounded to ndigits. The ndigits argument defaults to zero, so leaving it out results in a number rounded to an integer. As you’ll see, round() may not work quite as you expect.

The way most people are taught to round a number goes something like this:

Round the number n to p decimal places by first shifting the decimal point in n by p places. To do that, multiply n by 10ᵖ (10 raised to the p power) to get a new number, m.
Then look at the digit d in the first decimal place of m. If d is less than 5, round m down to the nearest integer. Otherwise, round m up.
Finally, shift the decimal point back p places by dividing m by 10ᵖ.

It’s an algorithm! For example, the number 2.5 rounded to the nearest whole number is 3. The number 1.64 rounded to one decimal place is 1.6.

Now open up an interpreter session and round 2.5 to the nearest whole number using Python’s built-in round() function:

Python
      
>>> round(2.5)
2

Gasp!

Check out how round() handles the number 1.5:

Python
      
>>> round(1.5)
2

So, round() rounds 1.5 up to 2, and 2.5 down to 2!

Before you go raising an issue on the Python bug tracker, rest assured you that round(2.5) is supposed to return 2. There’s a good reason why round() behaves the way it does.

In this tutorial, you’ll learn that there are more ways to round a number than you might expect, each with unique advantages and disadvantages. round() behaves according to a particular rounding strategy—which may or may not be the one you need for a given situation.

You might be wondering, Can the way I round numbers really have that much of an impact? Next up, take a look at just how extreme the effects of rounding can be.

How Much Impact Can Rounding Have?

Suppose you have an incredibly lucky day and find $100 on the ground. Rather than spending all your money at once, you decide to play it smart and invest your money by buying some shares of different stocks.

The value of a stock depends on supply and demand. The more people there are who want to buy a stock, the more value that stock has, and vice versa. In high-volume stock markets, the value of a particular stock can fluctuate on a second-by-second basis.

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

[ 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 ]

Linear Regression in Python

2024-12-07T14:00:00+00:00

Linear regression is a foundational statistical tool for modeling the relationship between a dependent variable and one or more independent variables. It’s widely used in data science and machine learning to predict outcomes and understand relationships between variables. In Python, implementing linear regression can be straightforward with the help of third-party libraries such as scikit-learn and statsmodels.

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

Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation.
Implementing linear regression in Python involves using libraries like scikit-learn and statsmodels to fit models and make predictions.
The formula for linear regression is 𝑦 = 𝛽₀ + 𝛽₁𝑥₁ + ⋯ + 𝛽ᵣ𝑥ᵣ + 𝜀, representing the linear relationship between variables.
Simple linear regression involves one independent variable, whereas multiple linear regression involves two or more.
The scikit-learn library provides a convenient and efficient interface for performing linear regression in Python.

To implement linear regression in Python, you typically follow a five-step process: import necessary packages, provide and transform data, create and fit a regression model, evaluate the results, and make predictions. This approach allows you to perform both simple and multiple linear regressions, as well as polynomial regression, using Python’s robust ecosystem of scientific libraries.

Free Bonus: Click here to get access to a free NumPy Resources Guide that points you to the best tutorials, videos, and books for improving your NumPy skills.

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

Interactive Quiz

Linear Regression in Python

In this quiz, you'll test your knowledge of linear regression in Python. Linear regression is one of the fundamental statistical and machine learning techniques, and Python is a popular choice for machine learning.

Regression

Regression analysis is one of the most important fields in statistics and machine learning. There are many regression methods available. Linear regression is one of them.

What Is Regression?

Regression searches for relationships among variables. For example, you can observe several employees of some company and try to understand how their salaries depend on their features, such as experience, education level, role, city of employment, and so on.

This is a regression problem where data related to each employee represents one observation. The presumption is that the experience, education, role, and city are the independent features, while the salary depends on them.

Similarly, you can try to establish the mathematical dependence of housing prices on area, number of bedrooms, distance to the city center, and so on.

Generally, in regression analysis, you consider some phenomenon of interest and have a number of observations. Each observation has two or more features. Following the assumption that at least one of the features depends on the others, you try to establish a relation among them.

In other words, you need to find a function that maps some features or variables to others sufficiently well.

The dependent features are called the dependent variables, outputs, or responses. The independent features are called the independent variables, inputs, regressors, or predictors.

Regression problems usually have one continuous and unbounded dependent variable. The inputs, however, can be continuous, discrete, or even categorical data such as gender, nationality, or brand.

It’s a common practice to denote the outputs with 𝑦 and the inputs with 𝑥. If there are two or more independent variables, then they can be represented as the vector 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of inputs.

When Do You Need Regression?

Typically, you need regression to answer whether and how some phenomenon influences the other or how several variables are related. For example, you can use it to determine if and to what extent experience or gender impacts salaries.

Regression is also useful when you want to forecast a response using a new set of predictors. For example, you could try to predict electricity consumption of a household for the next hour given the outdoor temperature, time of day, and number of residents in that household.

Regression is used in many different fields, including economics, computer science, and the social sciences. Its importance rises every day with the availability of large amounts of data and increased awareness of the practical value of data.

Linear Regression

Linear regression is probably one of the most important and widely used regression techniques. It’s among the simplest regression methods. One of its main advantages is the ease of interpreting results.

Problem Formulation

When implementing linear regression of some dependent variable 𝑦 on the set of independent variables 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of predictors, you assume a linear relationship between 𝑦 and 𝐱: 𝑦 = 𝛽₀ + 𝛽₁𝑥₁ + ⋯ + 𝛽ᵣ𝑥ᵣ + 𝜀. This equation is the regression equation. 𝛽₀, 𝛽₁, …, 𝛽ᵣ are the regression coefficients, and 𝜀 is the random error.

Read the full article at https://realpython.com/linear-regression-in-python/ »

[ 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 ]

Python GUI Programming With Tkinter

2024-12-07T14:00:00+00:00

Tkinter is Python’s standard GUI framework, making it convenient for developing graphical user interfaces. As a cross-platform library, Tkinter ensures your applications appear native across Windows, macOS, and Linux. Despite criticisms of its outdated appearance, Tkinter remains a practical choice for quickly creating functional and cross-platform GUI applications.

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

GUI refers to graphical user interfaces, and Tkinter is Python’s built-in library for creating them.
Tkinter is included with most Python installations, so separate installation is often unnecessary.
Tkinter is still a relevant choice for building simple, cross-platform GUI applications.
Widgets in a Tkinter application can be arranged using geometry managers like .pack(), .place(), and .grid().
Interactive GUI applications with Tkinter are created by binding events, such as button clicks, to Python functions.

You’ll cover getting started with Tkinter, managing widgets, and creating interactive applications. Once you’ve mastered these skills by working through the exercises at the end of each section, you’ll tie everything together by building two applications. The first is a temperature converter, and the second is a text editor. It’s time to dive right in and learn how to build an application with Tkinter!

Note: This tutorial is adapted from the chapter “Graphical User Interfaces” of 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. In this tutorial, references to IDLE have been removed in favor of more general language.

The bulk of the material in this tutorial has been left unchanged, and you should have no problems running the example code from the editor and environment of your choice.

Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you’ll need to take your Python skills to the next level.

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

Interactive Quiz

Python GUI Programming With Tkinter

In this quiz, you'll test your understanding of Python GUI Programming With Tkinter, the de facto Python GUI framework. Check your knowledge of GUI programming concepts such as widgets, geometry managers, and event handlers.

Building Your First Python GUI Application With Tkinter

The foundational element of a Tkinter GUI is the window. Windows are the containers in which all other GUI elements live. These other GUI elements, such as text boxes, labels, and buttons, are known as widgets. Widgets are contained inside of windows.

First, create a window that contains a single widget. Start up a new Python shell session and follow along!

Note: The code examples in this tutorial have all been tested on Windows, macOS, and Ubuntu Linux 20.04 with Python version 3.10.

If you’ve installed Python with the official installers available for Windows and macOS from python.org, then you should have no problem running the sample code. You can safely skip the rest of this note and continue with the tutorial!

If you haven’t installed Python with the official installers, or there’s no official distribution for your system, then here are some tips for getting up and going.

Python on macOS with Homebrew:

The Python distribution for macOS available on Homebrew doesn’t come bundled with the Tcl/Tk dependency required by Tkinter. The default system version is used instead. This version may be outdated and prevent you from importing the Tkinter module. To avoid this problem, use the official macOS installer.

Ubuntu Linux 20.04:

To conserve memory space, the default version of the Python interpreter that comes pre-installed on Ubuntu Linux 20.04 has no support for Tkinter. However, if you want to continue using the Python interpreter bundled with your operating system, then install the following package:

Shell
      
$ sudo apt-get install python3-tk

This installs the Python GUI Tkinter module.

Other Linux Flavors:

If you’re unable to get a working Python installation on your flavor of Linux, then you can build Python with the correct version of Tcl/Tk from the source code. For a step-by-step walk-through of this process, check out the Python 3 Installation & Setup Guide. You may also try using pyenv to manage multiple Python versions.

With your Python shell open, the first thing you need to do is import the Python GUI Tkinter module:

Python
      
>>> import tkinter as tk

A window is an instance of Tkinter’s Tk class. Go ahead and create a new window and assign it to the variable window:

Python
      
>>> window = tk.Tk()

When you execute the above code, a new window pops up on your screen. How it looks depends on your operating system:

Throughout the rest of this tutorial, you’ll see Windows screenshots.

Now that you have a window, you can add a widget. Use the tk.Label class to add some text to a window. Create a Label widget with the text "Hello, Tkinter" and assign it to a variable called greeting:

Python
      
>>> greeting = tk.Label(text="Hello, Tkinter")

The window you created earlier doesn’t change. You just created a Label widget, but you haven’t added it to the window yet. There are several ways to add widgets to a window. Right now, you can use the Label widget’s .pack() method:

Python
      
>>> greeting.pack()

The window now looks like this:

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

[ 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.

[ 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 ]

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

[ 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 #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.

[ 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 ]

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.

[ 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: 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.

[ 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: 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.

[ 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: 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.

[ 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 ]

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.

[ 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: 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.

[ 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: 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.

[ 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 #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."

[ 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 ]

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.

[ 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: 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.

[ 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 ]

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 ]

What's New in Python 3.13

2024-10-08T14:00:00+00:00

Python 3.13 was published on October 7, 2024. This new version is a major step forward for the language, although several of the biggest changes are happening under the hood and won’t be immediately visible to you.

In a sense, Python 3.13 is laying the groundwork for some future improvements, especially to the language’s performance. As you watch the course, you’ll learn more about the background for this and dive into some new features that are fully available now.

In this video course, you’ll learn about some of the improvements in the new version, including:

Improvements made to the interactive interpreter (REPL)
Clearer error messages that can help you fix common mistakes
Advancements done in removing the global interpreter lock (GIL) and making Python free-threaded
The implementation of an experimental Just-In-Time (JIT) compiler
A host of minor upgrades to Python’s static type system

In this video course, you’ll explore these changes and see how this new version of Python can work for you.

If you want to try any of the examples in this video course, then you’ll need to use Python 3.13. The Python 3 Installation & Setup Guide and How Can You Install a Pre-Release Version of Python? walk you through several options for adding a new version of Python to your system.

[ 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

Documenting Python Projects With Sphinx and Read the Docs

Python News Roundup: December 2024

PEP 750 – Template Strings Updated

Python 3.14.0a2 Released

Using and Creating Global Variables in Your Python Functions

Using Global Variables in Python Functions

Effective Python Testing With pytest

How to Install pytest

What Makes pytest So Useful?

Less Boilerplate

Python Timer Functions: Three Ways to Monitor Your Code

Python Timers

Python Timer Functions

Example: Download Tutorials

Asynchronous Tasks With Django and Celery

Python Celery Basics

Why Use Celery?

How to Run Your Python Scripts and Code

What Scripts and Modules Are

How to Run Python Scripts From the Command Line

Using the python Command

When to Use a List Comprehension in Python

Transforming Lists in Python

Use for Loops

Work With map Objects

Leverage List Comprehensions

How to Round Numbers in Python

Python’s Built-in round() Function

How Much Impact Can Rounding Have?

Linear Regression in Python

Regression

What Is Regression?

When Do You Need Regression?

Linear Regression

Problem Formulation

Python GUI Programming With Tkinter

Building Your First Python GUI Application With Tkinter

Adding a Widget

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

What's New in Python 3.13

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

How to Install `pytest`

What Makes `pytest` So Useful?

Using the `python` Command

Use `for` Loops

Work With `map` Objects

Python’s Built-in `round()` Function

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