Real Python

Advanced Python import Techniques

2024-09-24T14:00:00+00:00

In Python, you use the import keyword to make code in one module available in another. Imports in Python are important for structuring your code effectively. Using imports properly will make you more productive, allowing you to reuse code while keeping your projects maintainable.

This video course provides a comprehensive overview of Python’s import statement and how it works. The import system is powerful, and this course will teach you how to harness this power. While you’ll cover many of the concepts behind Python’s import system, this video course is mostly example driven, so you’ll learn from the numerous code examples shared throughout.

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

Use modules, packages, and namespace packages
Manage namespaces and avoid shadowing
Avoid circular imports
Import modules dynamically at runtime
Customize Python’s import system

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Python Virtual Environments: A Primer

2024-09-23T14:00:00+00:00

In this tutorial, you’ll learn how to work with Python’s venv module to create and manage separate virtual environments for your Python projects. Each environment can use different versions of package dependencies and different versions of Python.

Once you’ve learned to work with virtual environments, you’ll be able to help other programmers reproduce your development setup and make sure that your projects never create dependency conflicts.

By the end of this tutorial, you’ll know how to:

Create and activate a Python virtual environment
Explain why you want to isolate external dependencies
Visualize what Python does when you create a virtual environment
Customize your virtual environments using optional arguments to venv
Deactivate and remove virtual environments
Choose additional tools for managing your Python versions and virtual environments

Working with virtual environments is a common and effective technique used in Python development. Gaining a better understanding of how they work, why you need them, and what you can do with them will help you master your Python programming workflow.

Throughout the tutorial, you can select code examples for either Windows, Ubuntu Linux, or macOS. Pick your platform at the top right of the relevant code blocks to get the commands that you need, and feel free to switch between them if you want to learn how to work with virtual environments on other operating systems.

Free Bonus: Click here to download a free cheat sheet that summarizes the main venv commands you’ll learn about in this tutorial.

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

Interactive Quiz

Python Virtual Environments: A Primer

In this quiz, you'll test your understanding of Python virtual environments. With this knowledge, you'll be able to avoid dependency conflicts and help other developers reproduce your development environment.

How Can You Work With a Python Virtual Environment?

If you just need to get a virtual environment up and running to continue working on your favorite project, then this section is for you.

This tutorial uses Python’s venv module to create virtual environments. This module is part of Python’s standard library, and it’s been the officially recommended way to create virtual environments since Python 3.5.

Note: There are other great third-party tools for creating virtual environments, such as conda and virtualenv, that you’ll learn more about later in this tutorial. Either of these tools can help you set up a virtual environment and also go beyond just that.

For basic usage, venv is an excellent choice because it already comes packaged with your Python installation. With that in mind, you’re ready to create your first virtual environment.

Create It

Any time you’re working on a Python project that uses external dependencies you’re installing with pip, it’s best to first create a virtual environment:

Windows PowerShell
      
PS> py -m venv venv\

This command allows the Python launcher for Windows to select an appropriate version of Python to execute. It comes bundled with the official installation and is the most convenient way to execute Python on Windows.

You can bypass the launcher and run the Python executable directly using the python command, but if you haven’t configured the PATH and PATHEXT variables, then you might need to provide the full path:

Windows PowerShell
      
PS> C:\Users\Name\AppData\Local\Programs\Python\Python312\python -m venv venv\

The system path shown above assumes that you installed Python 3.12 using the Windows installer provided by the Python downloads page. The path to the Python executable on your system might be different. Working with PowerShell, you can find the path using the where.exe python command.

Note: You don’t need to include the backslash (\) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

Shell
      
$ python3 -m venv venv/

Many Linux operating systems ship with a version of Python 3. If python3 doesn’t work, then you’ll have to first install Python and you may need to use the specific name of the executable version that you installed, for example, python3.12 for Python 3.12.x. If that’s the case for you, remember to replace mentions of python3 in the code blocks with your specific version number.

Note: You don’t need to include the slash (/) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

Shell
      
$ python3 -m venv venv/

Older versions of macOS come with a system installation of Python 2.7.x that you should never use to run your scripts. If you’re working on macOS < 12.3 and invoke the Python interpreter with python instead of python3, then you might accidentally start up the outdated system Python interpreter.

If running python3 doesn’t work, then you’ll have to first install a modern version of Python.

Note: You don’t need to include the slash (/) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

This command creates a new virtual environment named venv using Python’s built-in venv module. The first venv that you use in the command specifies the module, and the second venv/ sets the name for your virtual environment. You could name it differently, but calling it venv is a good practice for consistency.

Activate It

Great! Your project now has its own virtual environment. Generally, before you start to use it, you’ll activate the environment by executing a script that comes with the installation:

Windows PowerShell
      
PS> venv\Scripts\activate
(venv) PS>

If your attempt to run this command produces an error, then you’ll first have to loosen the execution policy.

Shell
      
$ source venv/bin/activate
(venv) $

Before you run this command, make sure that you’re in the folder containing the virtual environment you just created. If you’ve named your virtual environment something other than venv, then you’ll have to use that name in the path instead of venv when you source the activation script.

Note: You can also work with your virtual environment without activating it. To do this, you provide the full path to its Python interpreter when executing a command. However, you’ll likely want to activate the virtual environment after you create it to save yourself the effort of having to repeatedly type long pathnames.

Once you can see the name of your virtual environment in your command prompt—in this case (venv)—then you’ll know that your virtual environment is active. Now you’re all set and ready to install your external packages!

Read the full article at https://realpython.com/python-virtual-environments-a-primer/ »

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The Real Python Podcast – Episode #221: Thriving as a Developer With ADHD

2024-09-20T12:00:00+00:00

What are strategies for being a productive developer with ADHD? How can you help your team members with ADHD to succeed and complete projects? This week on the show, we speak with Chris Ferdinandi about his website and podcast "ADHD For the Win!"

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Python 3.13 Preview: Free Threading and a JIT Compiler

2024-09-18T14:00:00+00:00

Although the final release of Python 3.13 is scheduled for October 2024, you can download and install a preview version today to explore the new features. Notably, the introduction of free threading and a just-in-time (JIT) compiler are among the most exciting enhancements, both designed to give your code a significant performance boost.

In this tutorial, you’ll:

Compile a custom Python build from source using Docker
Disable the Global Interpreter Lock (GIL) in Python
Enable the Just-In-Time (JIT) compiler for Python code
Determine the availability of new features at runtime
Assess the performance improvements in Python 3.13
Make a C extension module targeting Python’s new ABI

Check out what’s new in the Python changelog for a complete list of the upcoming features and improvements. This document contains a quick summary of the release highlights as well as a detailed breakdown of the planned changes.

To download the sample code and other resources accompanying this tutorial, click the link below:

Get Your Code: Click here to download the free sample code that shows you how to work with the experimental free threading and JIT compiler in Python 3.13.

Take the Quiz: Test your knowledge with our interactive “Python 3.13: Free-Threading and a JIT Compiler” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python 3.13: Free-Threading and a JIT Compiler

In this quiz, you'll test your understanding of the new features in Python 3.13. You'll revisit how to compile a custom Python build, disable the Global Interpreter Lock (GIL), enable the Just-In-Time (JIT) compiler, and more.

Free Threading and JIT in Python 3.13: What’s the Fuss?

Before going any further, it’s important to note that the majority of improvements in Python 3.13 will remain invisible to the average Joe. This includes free threading (PEP 703) and the JIT compiler (PEP 744), which have already sparked a lot of excitement in the Python community.

Keep in mind that they’re both experimental features aimed at power users, who must take extra steps to enable them at Python’s build time. None of the official channels will distribute Python 3.13 with these additional features enabled by default. This is to maintain backward compatibility and to prevent potential glitches, which should be expected.

Note: Don’t try to use Python 3.13 with the experimental features in a production environment! It may cause unexpected problems, and the Python Steering Council reserves the right to remove these features entirely from future Python releases if they prove to be unstable. Treat them as an experiment to gather real-world data.

In this section, you’ll get a birds-eye view of these experimental features so you can set the right expectations. You’ll find detailed explanations on how to enable them and evaluate their impact on Python’s performance in the remainder of this tutorial.

Free Threading Makes the GIL Optional

Free threading is an attempt to remove the Global Interpreter Lock (GIL) from CPython, which has traditionally been the biggest obstacle to achieving thread-based parallelism when performing CPU-bound tasks. In short, the GIL allows only one thread of execution to run at any given time, regardless of how many cores your CPU is equipped with. This prevents Python from leveraging the available computing power effectively.

There have been many attempts in the past to bypass the GIL in Python, each with varying levels of success. You can read about these attempts in the tutorial on bypassing the GIL. While previous attempts were made by third parties, this is the first time that the core Python development team has taken similar steps with the permission of the steering council, even if some reservations remain.

Note: Python 3.12 approached the GIL obstacle from a different angle by allowing the individual subinterpreters to have their independent GILs. This can improve Python’s concurrency by letting you run different tasks in parallel, but without the ability to share data cheaply between them due to isolated memory spaces. In Python 3.13, you’ll be able to combine subinterpreters with free threading.

The removal of the GIL would have significant implications for the Python interpreter itself and especially for the large body of third-party code that relies on it. Because free threading essentially breaks backward compatibility, the long-term plan for its implementation is as follows:

Experimental: Free threading is introduced as an experimental feature and isn’t a part of the official Python distribution. You must make a custom Python build to disable the GIL.
Enabled: The GIL becomes optional in the official Python distribution but remains enabled by default to allow for a transition period.
Disabled: The GIL is disabled by default, but you can still enable it if needed for compatibility reasons.

There are no plans to completely remove the GIL from the official Python distribution at the moment, as that would cause significant disruption to legacy codebases and libraries. Note that the steps outlined above are just a proposal subject to change. Also, free threading may not pan out at all if it makes single-threaded Python run slower than without it.

Until the GIL becomes optional in the official Python distribution, which may take a few more years, the Python development team will maintain two incompatible interpreter versions. The vanilla Python build won’t support free threading, while the special free-threaded flavor will have a slightly different Application Binary Interface (ABI) tagged with the letter “t” for threading.

This means that C extension modules built for stock Python won’t be compatible with the free-threaded version and the other way around. Maintainers of those external modules will be expected to distribute two packages with each release. If you’re one of them, and you use the Python/C API, then you’ll learn how to target CPython’s new ABI in the final section of this tutorial.

JIT Compiles Python to Machine Code

As an interpreted language, Python takes your high-level code and executes it on the fly without the need for prior compilation. This has both pros and cons. Some of the biggest advantages of interpreted languages include better portability across different hardware architectures and a quick development time due to the lack of a compilation step. At the same time, interpretation is much slower than directly executing code native to your machine.

Note: To be more precise, Python interprets bytecode instructions, an intermediate binary representation between pure Python and machine code. The Python interpreter compiles your code to bytecode when you import a module and stores the resulting bytecode in the __pycache__ folder. This doesn’t inherently make your Python scripts run faster, but loading a pre-processed bytecode can indeed speed up their startup time.

Languages like C and C++ leverage Ahead-of-Time (AOT) compilation to translate your high-level code into machine code before you ship your software. The benefit of this is faster execution since the code is already in the computer’s mother tongue. While you no longer need a separate program to interpret the code, you must compile it separately for all target platforms that you want supported. You should also handle platform-specific differences yourself.

Read the full article at https://realpython.com/python313-free-threading-jit/ »

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Customizing VS Code Through Color Themes

2024-09-17T14:00:00+00:00

A well-designed coding environment not only enhances your focus and productivity but also makes coding sessions more enjoyable. In this Code Conversation, your instructor Philipp Acsany will guide you step-by-step through the process of finding, installing, and adjusting color themes in VS Code. You’ll explore the various options available in VS Code and learn how to make fine adjustments to create a setup that suits your personal preferences.

In this video course, you’ll:

Learn about Themes in VS Code
Find a VS Code Color Theme
Select a Theme
Install Your Theme
Make Additional Adjustments

By the end of the course, you’ll have a coding environment that not only looks great but also enhances your overall coding experience.

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Quiz: Using Python's pip to Manage Your Projects' Dependencies

2024-09-17T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s standard package manager, pip. You’ll revisit the concepts behind pip, important commands, and how to install packages.

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Using Python's pip to Manage Your Projects' Dependencies

2024-09-16T14:00:00+00:00

The standard package manager for Python is pip. It allows you to install and manage packages that aren’t part of the Python standard library. If you’re looking for an introduction to pip, then you’ve come to the right place!

In this tutorial, you’ll learn how to:

Set up pip in your working environment
Fix common errors related to working with pip
Install and uninstall packages with pip
Manage projects’ dependencies using requirements files

You can do a lot with pip, but the Python community is very active and has created some neat alternatives to pip. You’ll learn about those later in this tutorial.

Get Your Cheat Sheet: Click here to download a free pip cheat sheet that summarizes the most important pip commands.

Take the Quiz: Test your knowledge with our interactive “Using Python's pip to Manage Your Projects' Dependencies” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Using Python's pip to Manage Your Projects' Dependencies

In this quiz, you'll test your understanding of Python's standard package manager, pip. You'll revisit the ideas behind pip, important commands, and how to install packages.

Getting Started With `pip`

So, what exactly does pip do? pip is a package manager for Python. That means it’s a tool that allows you to install and manage libraries and dependencies that aren’t distributed as part of the standard library. The name pip was introduced by Ian Bicking in 2008:

I’ve finished renaming pyinstall to its new name: pip. The name pip is [an] acronym and declaration: pip installs packages. (Source)

Package management is so important that Python’s installers have included pip since versions 3.4 and 2.7.9, for Python 3 and Python 2, respectively. Many Python projects use pip, which makes it an essential tool for every Pythonista.

The concept of a package manager might be familiar to you if you’re coming from another programming language. JavaScript uses npm for package management, Ruby uses gem, and the .NET platform uses NuGet. In Python, pip has become the standard package manager.

Finding `pip` on Your System

The Python installer gives you the option to install pip when installing Python on your system. In fact, the option to install pip with Python is checked by default, so pip should be ready for you to use after installing Python.

Note: On some Linux (Unix) systems like Ubuntu, pip comes in a separate package called python3-pip, which you need to install with sudo apt install python3-pip. It’s not installed by default with the interpreter.

You can verify that pip is available by looking for the pip3 executable on your system. Select your operating system below and use your platform-specific command accordingly:

Windows PowerShell
      
PS> where pip3

The where command on Windows will show you where you can find the executable of pip3. If Windows can’t find an executable named pip3, then you can also try looking for pip without the three (3) at the end.

Shell

$ which pip3

The which command on Linux systems and macOS shows you where the pip3 binary file is located.

On Windows and Unix systems, pip3 may be found in more than one location. This can happen when you have multiple Python versions installed. If you can’t find pip in any location on your system, then you may consider reinstalling pip.

Instead of running your system pip directly, you can also run it as a Python module. In the next section, you’ll learn how.

Running `pip` as a Module

When you run your system pip directly, the command itself doesn’t reveal which Python version pip belongs to. This unfortunately means that you could use pip to install a package into the site-packages of an old Python version without noticing. To prevent this from happening, you should run pip as a Python module:

Shell
      
$ python -m pip

Notice that you use python -m to run pip. The -m switch tells Python to run a module as an executable of the python interpreter. This way, you can ensure that your system default Python version runs the pip command. If you want to learn more about this way of running pip, then you can read Brett Cannon’s insightful article about the advantages of using python -m pip.

Note: Depending on how you installed Python, your Python executable may have a different name than python. You’ll see python used in this tutorial, but you may have to adapt the commands to use something like py or python3 instead.

Sometimes you may want to be more explicit and limit packages to a specific project. In situations like this, you should run pip inside a virtual environment.

Read the full article at https://realpython.com/what-is-pip/ »

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The Real Python Podcast – Episode #220: Configuring Git Pre-Commit Hooks & Estimating Software Projects

2024-09-13T12:00:00+00:00

How do you take advantage of Git pre-commit hooks? How do you build custom software checks and rules that run every time you commit your code? 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: Python 3.13: Free-Threading and a JIT Compiler

2024-09-12T12:00:00+00:00

In this quiz, you’ll test your understanding of the new features in Python 3.13.

By working through this quiz, you’ll revisit how to compile a custom Python build, disable the Global Interpreter Lock (GIL), enable the Just-In-Time (JIT) compiler, determine the availability of new features at runtime, assess the performance improvements in Python 3.13, and make a C extension module targeting Python’s new ABI.

[ 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 Use Conditional Expressions With NumPy where()

2024-09-11T14:00:00+00:00

The NumPy where() function is a powerful tool for filtering array elements in lists, tuples, and NumPy arrays. It works by using a conditional predicate, similar to the logic used in the WHERE or HAVING clauses in SQL queries. It’s okay if you’re not familiar with SQL—you don’t need to know it to follow along with this tutorial.

You would typically use np.where() when you have an array and need to analyze its elements differently depending on their values. For example, you might need to replace negative numbers with zeros or replace missing values such as None or np.nan with something more meaningful. When you run where(), you’ll produce a new array containing the results of your analysis.

You generally supply three parameters when using where(). First, you provide a condition against which each element of your original array is matched. Then, you provide two additional parameters: the first defines what you want to do if an element matches your condition, while the second defines what you want to do if it doesn’t.

If you think this all sounds similar to Python’s ternary operator, you’re correct. The logic is the same.

Note: In this tutorial, you’ll work with two-dimensional arrays. However, the same principles can be applied to arrays of any dimension.

Before you start, you should familiarize yourself with NumPy arrays and how to use them. It will also be helpful if you understand the subject of broadcasting, particularly for the latter part of this tutorial.

In addition, you may want to use the data analysis tool Jupyter Notebook as you work through the examples in this tutorial. Alternatively, JupyterLab will give you an enhanced notebook experience, but feel free to use any Python environment.

The NumPy library is not part of core Python, so you’ll need to install it. If you’re using a Jupyter Notebook, create a new code cell and type !python -m pip install numpy into it. When you run the cell, the library will install. If you’re working at the command line, use the same command, only without the exclamation point (!).

With these preliminaries out of the way, you’re now good to go.

Get Your Code: Click here to download the free sample code that shows you how to use conditional expressions with NumPy where().

Take the Quiz: Test your knowledge with our interactive “How to Use Conditional Expressions With NumPy where()” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Use Conditional Expressions With NumPy where()

This quiz aims to test your understanding of the np.where() function. You won't find all the answers in the tutorial, so you'll need to do additional research. It's recommended that you make sure you can do all the exercises in the tutorial before tackling this quiz. Enjoy!

How to Write Conditional Expressions With NumPy `where()`

One of the most common scenarios for using where() is when you need to replace certain elements in a NumPy array with other values depending on some condition.

Consider the following array:

Python
      
        
      
    
>>> import numpy as np

>>> test_array = np.array(
...     [
...         [3.1688358, 3.9091694, 1.66405549, -3.61976783],
...         [7.33400434, -3.25797286, -9.65148913, -0.76115911],
...         [2.71053173, -6.02410179, 7.46355805, 1.30949485],
...     ]
... )

To begin with, you need to import the NumPy library into your program. It’s standard practice to do so using the alias np, which allows you to refer to the library using this abbreviated form.

The resulting array has a shape of three rows and four columns, each containing a floating-point number.

Now suppose you wanted to replace all the negative numbers with their positive equivalents:

Python
      
        
      
    
>>> np.where(
...     test_array < 0,
...     test_array * -1,
...     test_array,
... )
array([[3.1688358 , 3.9091694 , 1.66405549, 3.61976783],
       [7.33400434, 3.25797286, 9.65148913, 0.76115911],
       [2.71053173, 6.02410179, 7.46355805, 1.30949485]])

The result is a new NumPy array with the negative numbers replaced by positives. Look carefully at the original test_array and then at the corresponding elements of the new all_positives array, and you’ll see that the result is exactly what you wanted.

Note: The above example gives you an idea of how the where() function works. If you were doing this in practice, you’d most likely use either the np.abs() or np.absolute() functions instead. Both do the same thing because the former is shorthand for the latter:

Python
      
>>> np.abs(test_array)
array([[3.1688358 , 3.9091694 , 1.66405549, 3.61976783],
       [7.33400434, 3.25797286, 9.65148913, 0.76115911],
       [2.71053173, 6.02410179, 7.46355805, 1.30949485]])

Once more, all negative values have been removed.

Before moving on to other use cases of where(), you’ll take a closer look at how this all works. To achieve your aim in the previous example, you passed in test_array < 0 as the condition. In NumPy, this creates a Boolean array that where() uses:

Python
      
>>> test_array < 0
array([[False, False, False,  True],
       [False,  True,  True,  True],
       [False,  True, False, False]])

The Boolean array, often called the mask, consists only of elements that are either True or False. If an element matches the condition, the corresponding element in the Boolean array will be True. Otherwise, it’ll be False.

Read the full article at https://realpython.com/numpy-where-conditional-expressions/ »

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Quiz: Python Virtual Environments: A Primer

2024-09-11T12:00:00+00:00

So you’ve been primed on Python virtual environments! Test your understanding of the tutorial here.

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When to Use .repr() vs .str() in Python

2024-09-10T14:00:00+00:00

One of the most common tasks that a computer program performs is to display data. The program often displays this information to the program’s user. However, a program also needs to show information to the programmer developing and maintaining it. The information a programmer needs about an object differs from how the program should display the same object for the user, and that’s where .__repr__() vs .__str__() comes in.

A Python object has several special methods that provide specific behavior. There are two similar special methods that describe the object using a string representation. These methods are .__repr__() and .__str__(). The .__repr__() method returns a detailed description for a programmer who needs to maintain and debug the code. The .__str__() method returns a simpler description with information for the user of the program.

The .__repr__() and .__str__() methods are two of the special methods that you can define for any class. They allow you to control how a program displays an object in several common forms of output, such as what you get from the print() function, formatted strings, and interactive environments.

In this video course, you’ll learn how to differentiate .__repr__() vs .__str__() and how to use these special methods in the classes you define. Defining these methods effectively makes the classes that you write more readable and easier to debug and maintain. So, when should you choose Python’s .__repr__() vs .__str__?

[ 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: September 2024

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

As the autumn leaves start to fall and signal the transition to cooler weather, the Python community has warmed up to a series of noteworthy developments. Last month, a new maintenance release of Python 3.12.5 was introduced, reinforcing the language’s ongoing commitment to stability and security.

On a parallel note, Python continues its reign as the top programming language according to IEEE Spectrum’s annual rankings. This sentiment is echoed by the Python Developers Survey 2023 results, which reveal intriguing trends and preferences within the community.

Looking ahead, PEP 750 has proposed the addition of tag strings in Python 3.14, inspired by JavaScript’s tagged template literals. This feature aims to enhance string processing, offering developers more control and expressiveness.

Also, EuroSciPy 2024 recently concluded in Poland after successfully fostering cross-disciplinary collaboration and learning. The event featured insightful talks and hands-on tutorials, spotlighting innovative tools and libraries that are advancing scientific computing with Python.

Let’s dive into the most significant Python news from the past month!

Python 3.12.5 Released

Early last month, Python 3.12.5 was released as the fifth maintenance update for the 3.12 series. Since the previous patch update in June, this release packs over 250 bug fixes, performance improvements, and documentation enhancements.

Here are the most important highlights:

Standard Library: Many modules in the standard library received crucial updates, such as fixes for crashes in ssl when the main interpreter restarts, and various corrections for error-handling mechanisms.
Core Python: The core Python runtime has several enhancements, including improvements to dictionary watchers, error messages, and fixes for edge-case crashes involving f-strings and multithreading.
Security: Key security improvements include the addition of missing audit events for interactive Python use and socket connection authentication within a fallback implementation on platforms such as Windows, where Unix inter-process communication is unavailable.
Tests: New test cases have been added and bug fixes have been applied to prevent random memory leaks during testing.
Documentation: Python documentation has been updated to remove discrepancies and clarify edge cases in multithreaded queues.

Additionally, Python 3.12.5 comes equipped with pip 24.2 by default, bringing a slew of significant improvements to enhance security, efficiency, and functionality. One of the most notable upgrades is that pip now defaults to using system certificates, bolstering security measures when managing and installing third-party packages.

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

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The Real Python Podcast – Episode #219: Astrophysics and Astronomy With Python & PyCon Africa 2024

2024-09-06T12:00:00+00:00

Are you interested in practicing your Python skills while learning how to solve astrophysics and astronomy problems? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Lists vs Tuples in Python

2024-09-04T14:00:00+00:00

In Python, lists and tuples are versatile and useful data types that allow you to store data in a sequence. You’ll find them in virtually every nontrivial Python program. Learning about them is a core skill for you as a Python developer.

In this tutorial, you’ll:

Get to know lists and tuples
Explore the core characteristics of lists and tuples
Learn how to define and manipulate lists and tuples
Decide when to use lists or tuples in your code

To get the most out of this tutorial, you should know the basics of Python programming, including how to define variables.

Get Your Code: Click here to download the free sample code that shows you how to work with lists and tuples in Python.

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

Interactive Quiz

Lists vs Tuples in Python

Challenge yourself with this quiz to evaluate and deepen your understanding of Python lists and tuples. You'll explore key concepts, such as how to create, access, and manipulate these data types, while also learning best practices for using them efficiently in your code.

Getting Started With Python Lists and Tuples

In Python, a list is a collection of arbitrary objects, somewhat akin to an array in many other programming languages but more flexible. To define a list, you typically enclose a comma-separated sequence of objects in square brackets ([]), as shown below:

Python
      
>>> colors = ["red", "green", "blue", "yellow"]

>>> colors
['red', 'green', 'blue', 'yellow']

In this code snippet, you define a list of colors using string objects separated by commas and enclose them in square brackets.

Similarly, tuples are also collections of arbitrary objects. To define a tuple, you’ll enclose a comma-separated sequence of objects in parentheses (()), as shown below:

Python
      
>>> person = ("Jane Doe", 25, "Python Developer", "Canada")

>>> person
('Jane Doe', 25, 'Python Developer', 'Canada')

In this example, you define a tuple with data for a given person, including their name, age, job, and base country.

Up to this point, it may seem that lists and tuples are mostly the same. However, there’s an important difference:

Feature	List	Tuple
Is an ordered sequence	✅	✅
Can contain arbitrary objects	✅	✅
Can be indexed and sliced	✅	✅
Can be nested	✅	✅
Is mutable	✅	❌

Both lists and tuples are sequence data types, which means they can contain objects arranged in order. You can access those objects using an integer index that represents their position in the sequence.

Even though both data types can contain arbitrary and heterogeneous objects, you’ll commonly use lists to store homogeneous objects and tuples to store heterogeneous objects.

Note: In this tutorial, you’ll see the terms homogeneous and heterogeneous used to express the following ideas:

Homogeneous: Objects of the same data type or the same semantic meaning, like a series of animals, fruits, colors, and so on.
Heterogeneous: Objects of different data types or different semantic meanings, like the attributes of a car: model, color, make, year, fuel type, and so on.

You can perform indexing and slicing operations on both lists and tuples. You can also have nested lists and nested tuples or a combination of them, like a list of tuples.

The most notable difference between lists and tuples is that lists are mutable, while tuples are immutable. This feature distinguishes them and drives their specific use cases.

Essentially, a list doesn’t have a fixed length since it’s mutable. Therefore, it’s natural to use homogeneous elements to have some structure in the list. A tuple, on the other hand, has a fixed length so the position of elements can have meaning, supporting heterogeneous data.

Creating Lists in Python

In many situations, you’ll define a list object using a literal. A list literal is a comma-separated sequence of objects enclosed in square brackets:

Python
      
>>> countries = ["United States", "Canada", "Poland", "Germany", "Austria"]

>>> countries
['United States', 'Canada', 'Poland', 'Germany', 'Austria']

In this example, you create a list of countries represented by string objects. Because lists are ordered sequences, the values retain the insertion order.

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

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Quiz: Lists vs Tuples in Python

2024-09-04T12:00:00+00:00

Challenge yourself with this quiz to evaluate and deepen your understanding of Python lists and tuples. You’ll explore key concepts, such as how to create and manipulate these data types, while also learning best practices for using them efficiently in your code.

You can take this quiz after reading the Lists vs Tuples in Python tutorial.

[ 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 Pydantic to Simplify Python Data Validation

2024-09-03T14:00:00+00:00

Pydantic is a powerful data validation and settings management library for Python, engineered to enhance the robustness and reliability of your codebase. From basic tasks, such as checking whether a variable is an integer, to more complex tasks, like ensuring highly-nested dictionary keys and values have the correct data types, Pydantic can handle just about any data validation scenario with minimal boilerplate code.

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

Work with data schemas with Pydantic’s BaseModel
Write custom validators for complex use cases
Validate function arguments with Pydantic’s @validate_call
Manage settings and configure applications with pydantic-settings

[ 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 ]

Generate Images With DALL·E and the OpenAI API

2024-09-02T14:00:00+00:00

Describe any image, then let a computer create it for you. What sounded futuristic only a few years ago has become reality with advances in neural networks and latent diffusion models (LDM). DALL·E by OpenAI has made a splash through the amazing generative art and realistic images that people create with it.

OpenAI allows access to DALL·E through their API, which means that you can incorporate its functionality into your Python applications.

In this tutorial, you’ll:

Get started using the OpenAI Python library
Explore API calls related to image generation
Create images from text prompts
Create variations of your generated image
Convert Base64 JSON responses to PNG image files

You’ll need some experience with Python, JSON, and file operations to breeze through this tutorial. You can also study up on these topics while you go along, as you’ll find relevant links throughout the text.

Get Your Code: Click here to download the free sample code that you’ll use to generate stunning images with DALL·E and the OpenAI API.

Take the Quiz: Test your knowledge with our interactive “Generate Images With DALL·E and the OpenAI API” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Generate Images With DALL·E and the OpenAI API

In this quiz, you'll test your understanding of generating images with DALL·E by OpenAI using Python. You'll revisit concepts such as using the OpenAI Python library, making API calls for image generation, creating images from text prompts, and converting Base64 strings to PNG image files.

Complete the Setup Requirements

If you’ve seen what DALL·E can do and you’re eager to make its functionality part of your Python applications, then you’re in the right spot! In this first section, you’ll quickly walk through what you need to do to get started using DALL·E’s image creation capabilities in your own code.

Install the OpenAI Python Library

Confirm that you’re running Python version 3.7.1 or higher, create and activate a virtual environment, and install the OpenAI Python library:

Windows PowerShell
      
        
      
    
PS> python --version
Python 3.12.5
PS> python -m venv venv
PS> .\venv\Scripts\activate
(venv) PS> python -m pip install openai

Shell
      
        
      
    
$ python --version
Python 3.12.5
$ python -m venv venv
$ source venv/bin/activate
(venv) $ python -m pip install openai

The openai package gives you access to the full OpenAI API. In this tutorial, you’ll focus on image generation, which lets you interact with DALL·E models to create and edit images from text prompts.

Get Your OpenAI API Key

You need an API key to make successful API calls. Sign up with OpenAI and create a new project API key by clicking on the Dashboard menu and then API keys on the bottom left of the navigation menu:

On this page, you can create and manage your API keys, which allow you to access the service that OpenAI offers through their API. You can create and delete secret keys.

Click on Create new secret key to create a new API key, and copy the value shown in the pop-up window.

Note: OpenAI assigns your API usage through unique key values, so make sure to keep your API key private. The company calculates the pricing of requests to generate images on a per-image basis that depends on the model you use and the resolution of the output image.

Keep in mind that OpenAI’s API services and pricing policies may change. Be sure to check their website for up-to-date information about pricing and offers.

Always keep this key secret! Copy the value of this key so you can later use it in your project. You’ll only see the key value once.

Save Your API Key as an Environment Variable

A quick way to save your API key and make it available to your Python scripts is to save it as an environment variable. Select your operating system to learn how:

Windows PowerShell
      
(venv) PS> $ENV:OPENAI_API_KEY = "<your-key-value-here>"

Shell
      
(venv) $ export OPENAI_API_KEY="<your-key-value-here>"

Read the full article at https://realpython.com/generate-images-with-dalle-openai-api/ »

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Quiz: How to Use Conditional Expressions With NumPy where()

2024-09-02T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Use Conditional Expressions With NumPy where().

By working through the questions, you’ll consolidate the knowledge you gained from the tutorial and take yourself beyond what you learned.

To answer some of the questions, you’ll need to do some research outside of the tutorial itself. Embrace this challenge because exploration can take you on a valuable 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 ]

Quiz: Generate Images With DALL·E and the OpenAI API

2024-09-02T12:00:00+00:00

In this quiz, you’ll test your understanding of generating images with DALL·E by OpenAI API using Python.

By working through this quiz, you’ll revisit how to use the OpenAI Python library, make API calls related to image generation, create images from text prompts, create variations of an image, and convert Base64 strings to PNG image 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 ]

Web Scraping With Scrapy and MongoDB

2024-08-28T14:00:00+00:00

Scrapy is a robust Python web scraping framework that can manage requests asynchronously, follow links, and parse site content. To store scraped data, you can use MongoDB, a scalable NoSQL database, that stores data in a JSON-like format. Combining Scrapy with MongoDB offers a powerful solution for web scraping projects, leveraging Scrapy’s efficiency and MongoDB’s flexible data storage.

In this tutorial, you’ll learn how to:

Set up and configure a Scrapy project
Build a functional web scraper with Scrapy
Extract data from websites using selectors
Store scraped data in a MongoDB database
Test and debug your Scrapy web scraper

If you’re new to web scraping and you’re looking for flexible and scalable tooling, then this is the right tutorial for you. You’ll also benefit from learning this tool kit if you’ve scraped sites before, but the complexity of your project has outgrown using Beautiful Soup and Requests.

To get the most out of this tutorial, you should have basic Python programming knowledge, understand object-oriented programming, comfortably work with third-party packages, and be familiar with HTML and CSS.

By the end, you’ll know how to get, parse, and store static data from the Internet, and you’ll be familiar with several useful tools that allow you to go much deeper.

Get Your Code: Click here to download the free code that shows you how to gather Web data with Scrapy and MongoDB.

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

Interactive Quiz

Web Scraping With Scrapy and MongoDB

In this quiz, you'll test your understanding of web scraping with Scrapy and MongoDB. You'll revisit how to set up a Scrapy project, build a functional web scraper, extract data from websites, store scraped data in MongoDB, and test and debug your Scrapy web scraper.

Prepare the Scraper Scaffolding

You’ll start by setting up the necessary tools and creating a basic project structure that will serve as the backbone for your scraping tasks.

While working through the tutorial, you’ll build a complete web scraping project, approaching it as an ETL (Extract, Transform, Load) process:

Extract data from the website using a Scrapy spider as your web crawler.
Transform this data, for example by cleaning or validating it, using an item pipeline.
Load the transformed data into a storage system like MongoDB with an item pipeline.

Scrapy provides scaffolding for all of these processes, and you’ll tap into that scaffolding to learn web scraping following the robust structure that Scrapy provides and that numerous enterprise-scale web scraping projects rely on.

Note: In a Scrapy web scraping project, a spider is a Python class that defines how to crawl a specific website or a group of websites. It contains the logic for making requests, parsing responses, and extracting the desired data.

First, you’ll install Scrapy and create a new Scrapy project, then explore the auto-generated project structure to ensure that you’re well-equipped to proceed with building a performant web scraper.

Install the Scrapy Package

To get started with Scrapy, you first need to install it using pip. Create and activate a virtual environment to keep the installation separate from your global Python installation. Then, you can install Scrapy:

Shell
      
(venv) $ python -m pip install scrapy

After the installation is complete, you can verify it by running the scrapy command and viewing the output:

Shell
      
        
      
    
(venv) $ scrapy
Scrapy 2.11.2 - no active project

Usage:
  scrapy <command> [options] [args]

Available commands:
  bench         Run quick benchmark test
  fetch         Fetch a URL using the Scrapy downloader
  genspider     Generate new spider using pre-defined templates
  runspider     Run a self-contained spider (without creating a project)
  settings      Get settings values
  shell         Interactive scraping console
  startproject  Create new project
  version       Print Scrapy version
  view          Open URL in browser, as seen by Scrapy

  [ more ]      More commands available when run from project directory

Use "scrapy <command> -h" to see more info about a command

The command-line (CLI) program should display the help text of Scrapy. This confirms that you installed the package correctly. You’ll next run the highlighted startproject command to create a project.

Create a Scrapy Project

Scrapy is built around projects. Generally, you’ll create a new project for each web scraping project that you’re working on. In this tutorial, you’ll work on scraping a website called Books to Scrape, so you can call your project books.

As you may have already identified in the help text, the framework provides a command to create a new project:

Shell
      
(venv) $ scrapy startproject books

Read the full article at https://realpython.com/web-scraping-with-scrapy-and-mongodb/ »

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Using Astropy for Astronomy With Python

2024-08-27T14:00:00+00:00

This course covers two problems from introductory astronomy to help you play with some Python libraries. You’ll use Astropy, NumPy, Matplotlib, and pandas to find planet conjunctions, and graph the best viewing times for a star.

In this course you’ll learn about:

Astronomy concepts of conjunction and optimal viewing
The Python package Astropy
Using pandas to process data
Building graphs with Matplotlib
Python’s warning module

[ 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 Install Python on Your System: A Guide

2024-08-26T14:00:00+00:00

Installing the latest version of Python on your computer could be a common requirement for you as a Python programmer. Fortunately, you’ll have a multitude of installation options. For example, you can download the official Python installer from Python.org, use your operating system’s package manager or app store, and more.

In this tutorial, you’ll focus on official CPython distributions, which are generally the best option for learning to program with the language. However, you’ll also learn about a few other distributions, like the one available on Homebrew for macOS users.

In this tutorial, you’ll learn how to:

Check whether a version of Python is installed on your system
Install or update to the latest Python on Windows, macOS, and Linux
Install Python on mobile devices like phones or tablets
Use Python on your browser with online interpreters

This tutorial covers installing the latest Python on the most important platforms or operating systems, such as Windows, macOS, Linux, iOS, and Android. However, it doesn’t cover all the existing Linux distributions, which would be a huge task. Anyway, you’ll find instructions for the most popular distros nowadays.

To get the most out of this tutorial, you should be comfortable using your operating system’s terminal or command line.

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

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

Interactive Quiz

Python Installation and Setup

In this quiz, you'll test your understanding of how to install or update Python on your computer. With this knowledge, you'll be able to set up Python on various operating systems, including Windows, macOS, and Linux.

Windows: How to Check or Get Python

In this section, you’ll learn to check whether Python is installed on your Windows operating system (OS) and which version you have. You’ll also explore three installation options that you can use on Windows.

Note: In this tutorial, you’ll focus on installing the latest version of Python in your current operating system (OS) rather than on installing multiple versions of Python. If you want to install several versions of Python in your OS, then check out the Managing Multiple Python Versions With pyenv tutorial. Note that on Windows machines, you’d have to use pyenv-win instead of pyenv.

For a more comprehensive guide on setting up a Windows machine for Python programming, check out Your Python Coding Environment on Windows: Setup Guide.

Checking the Python Version on Windows

To check whether you already have Python on your Windows machine, open a command-line application like PowerShell or the Windows Terminal.

Follow the steps below to open PowerShell on Windows:

Press the Win key.
Type PowerShell.
Press Enter.

Alternatively, you can right-click the Start button and select Windows PowerShell or Windows PowerShell (Admin). In some versions of Windows, you’ll find Terminal or Terminal (admin).

Note: To learn more about your options for the Windows terminal, check out Using the Terminal on Windows.

With the command line open, type in the following command and press the Enter key:

Windows PowerShell
      
PS> python --version
Python 3.x.z

Using the --version switch will show you the installed version. Note that the 3.x.z part is a placeholder here. In your machine, x and z will be numbers corresponding to the specific version you have installed.

Alternatively, you can use the -V switch:

Windows PowerShell
      
PS> python -V
Python 3.x.z

Using the python -V or python—-version command, you can check whether Python is installed on your system and learn what version you have. If Python isn’t installed on your OS, you’ll get an error message.

Knowing the Python Installation Options on Windows

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

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The Real Python Podcast – Episode #218: Exploring Robotics and Python Through Electronic Projects

2024-08-23T12:00:00+00:00

Are you interested in learning robotics with Python? Can physical electronics-based projects grow a child's interest in coding? This week on the show, we speak with author Marwan Alsabbagh about his book "Build Your Own Robot - Using Python, CRICKIT, and Raspberry Pi."

[ 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 ]

Primer on Jinja Templating

2024-08-21T14:00:00+00:00

Templates are an essential ingredient in full-stack web development. With Jinja, you can build rich templates that power the front end of your Python web applications.

But you don’t need to use a web framework to experience the capabilities of Jinja. When you want to create text files with programmatic content, Jinja can help you out.

In this tutorial, you’ll learn how to:

Install the Jinja template engine
Create your first Jinja template
Render a Jinja template in Flask
Use for loops and conditional statements with Jinja
Nest Jinja templates
Modify variables in Jinja with filters
Use macros to add functionality to your front end

You’ll start by using Jinja on its own to cover the basics of Jinja templating. Later you’ll build a basic Flask web project with two pages and a navigation bar to leverage the full potential of Jinja.

Throughout the tutorial, you’ll build an example app that showcases some of Jinja’s wide range of features. To see what it’ll do, skip ahead to the final section.

You can also find the full source code of the web project by clicking on the link below:

Source Code: Click here to download the source code that you’ll use to explore Jinja’s capabilities.

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

Interactive Quiz

Primer on Jinja Templating

In this quiz, you'll test your understanding of Jinja templating. Jinja is a powerful tool for building rich templates in Python web applications, and it can also be used to create text files with programmatic content.

This tutorial is for you if you want to learn more about the Jinja template language or if you’re getting started with Flask.

Get Started With Jinja

Jinja is not only a city in the Eastern Region of Uganda and a Japanese temple, but also a template engine. You commonly use template engines for web templates that receive dynamic content from the back end and render it as a static page in the front end.

But you can use Jinja without a web framework running in the background. That’s exactly what you’ll do in this section. Specifically, you’ll install Jinja and build your first templates.

Install Jinja

Before exploring any new package, it’s a good idea to create and activate a virtual environment. That way, you’re installing any project dependencies in your project’s virtual environment instead of system-wide.

Select your operating system below and use your platform-specific command to set up a virtual environment:

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

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

With the above commands, you create and activate a virtual environment named venv by using Python’s built-in venv module. The parentheses (()) surrounding venv in front of the prompt indicate that you’ve successfully activated the virtual environment.

After you’ve created and activated your virtual environment, it’s time to install Jinja with pip:

Shell
      
(venv) $ python -m pip install Jinja2

Don’t forget the 2 at the end of the package name. Otherwise, you’ll install an old version that isn’t compatible with Python 3.

It’s worth noting that although the current major version is actually greater than 2, the package that you’ll install is nevertheless called Jinja2. You can verify that you’ve installed a modern version of Jinja by running pip list:

Shell
      
        
      
    
(venv) $ python -m pip list
Package    Version
---------- -------
Jinja2     3.x
...

To make things even more confusing, after installing Jinja with an uppercase J, you have to import it with a lowercase j in Python. Try it out by opening the interactive Python interpreter and running the following commands:

Read the full article at https://realpython.com/primer-on-jinja-templating/ »

[ 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: Primer on Jinja Templating

2024-08-21T12:00:00+00:00

In this quiz, you’ll test your understanding of Jinja templating. Jinja is a powerful tool for building rich templates in Python web applications, and it can also be used to create text files with programmatic content.

[ 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 ]

Exploring Astrophysics in Python With pandas and Matplotlib

2024-08-20T14:00:00+00:00

This course uses three problems often covered in introductory astro-physics courses to play in Python. Along the way you’ll learn some astronomy, and how to use a variety of datascience libraries like NumPy, Matplotlib, pandas, and pint.

In this video course you’ll learn about:

Introductory astrophysics topics
Working with dataframes in pandas
Writing code that uses scientific units
Visualizing information with Matplotlib

[ 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 #217: Packaging Data Analyses & Using pandas GroupBy

2024-08-16T12:00:00+00:00

What are the best practices for organizing data analysis projects in Python? What are the advantages of a more package-centric approach to data science? 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: Web Scraping With Scrapy and MongoDB

2024-08-16T12:00:00+00:00

In this quiz, you’ll test your understanding of Web Scraping with Scrapy and MongoDB.

By working through this quiz, you’ll revisit how to set up a Scrapy project, build a functional web scraper, extract data from websites using selectors, store scraped data in a MongoDB database, and test and debug your Scrapy web scraper.

[ 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: The Walrus Operator: Python's Assignment Expressions

2024-08-14T12:00:00+00:00

In this quiz, you’ll test your understanding of the Python Walrus Operator. This operator, used for assignment expressions, was introduced in Python 3.8 and can be used to assign values to variables as part of an expression.

[ 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 ]

Sorting Dictionaries in Python: Keys, Values, and More

2024-08-13T14:00:00+00:00

You’ve got a dictionary, but you’d like to sort the key-value pairs. Perhaps you’ve tried passing a dictionary to the sorted() function but didn’t receive the results you expected. In this video course, you’ll go over everything you need to know to sort dictionaries in Python.

In this video course, you’ll:

Review how to use the sorted() function
Learn how to get dictionary views to iterate over
Understand how dictionaries are cast to lists during sorting
Learn how to specify a sort key to sort a dictionary by value, key, or nested attribute
Review dictionary comprehensions and the dict() constructor to rebuild your dictionaries
Consider alternative data structures for your key-value data

[ 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 #216: Learning Through Building the Black Python Devs Community

2024-08-09T12:00:00+00:00

What hurdles must be cleared when starting an international organization? How do you empower others in a community by sharing responsibilities? This week on the show, we speak with Jay Miller about Black Python Devs.

[ 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: Asynchronous Iterators and Iterables in Python

2024-08-07T12:00:00+00:00

Test your understanding of how to create and use Python async iterators and iterables in the context of asynchronous code.

You can take this quiz after reading the Asynchronous Iterators and Iterables in Python tutorial.

[ 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 ]

Interacting With REST APIs and Python

2024-08-06T14:00:00+00:00

There’s an amazing amount of data available on the Web. Many web services, like YouTube and GitHub, make their data accessible to third-party applications through an application programming interface (API). One of the most popular ways to build APIs is the REST architecture style. Python provides some great tools not only to get data from REST APIs but also to build your own Python REST APIs.

In this video course, you’ll learn:

What REST architecture is
How REST APIs provide access to web data
How to consume data from REST APIs using the requests library
What steps to take to build a REST API
What some popular Python tools are for building REST APIs

[ 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 #215: Using GraphQL in Django With Strawberry & Prototype Purgatory

2024-08-02T12:00:00+00:00

How do you integrate GraphQL into your Python web development? How about quickly building graph-based APIs inside Django's battery-included framework? 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 ]

Simulate a Text File in Python

2024-07-30T14:00:00+00:00

Testing applications that read files from a disk can be challenging. Issues such as machine dependencies, special access requirements, and slow performance often arise when you need to read text from a file.

In this Code Conversation with instructor Martin Breuss, you’ll discover how to simplify this process by simulating text files with StringIO from the io module in Python’s standard library.

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

Use io.StringIO to simulate a text file on disk
Perform file operations on a io.StringIO object
Decide when to use io.StringIO and when to avoid it
Understand possible alternatives
Mock a file object using unittest.mock

Understanding how to simulate text file objects and mock file objects can help streamline your testing strategy and development process.

[ 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 #214: Build Captivating Display Tables in Python With Great Tables

2024-07-26T12:00:00+00:00

Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library.

[ 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 ]

pandas GroupBy: Grouping Real World Data in Python

2024-07-23T14:00:00+00:00

Whether you’ve just started working with pandas and want to master one of its core capabilities, or you’re looking to fill in some gaps in your understanding about .groupby(), this course will help you to break down and visualize a pandas GroupBy operation from start to finish.

This course is meant to complement the official pandas documentation and the pandas Cookbook, where there are self-contained, bite-sized examples. Here, however, you’ll focus on three more involved walkthroughs that use real-world datasets.

In this course, you’ll cover:

How to use pandas GroupBy operations on real-world data
How the split-apply-combine chain of operations works
How to decompose the split-apply-combine chain into steps
How to categorize methods of a pandas GroupBy object based on their intent and result

[ 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 #213: Constraint Programming & Exploring Python's Built-in Functions

2024-07-19T12:00:00+00:00

What are discrete optimization problems? How do you solve them with constraint programming in Python? 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 ]

The Real Python Podcast – Episode #212: Digging Into Graph Theory in Python With David Amos

2024-07-12T12:00:00+00:00

Have you wondered about graph theory and how to start exploring it in Python? What resources and Python libraries can you use to experiment and learn more? This week on the show, former co-host David Amos returns to talk about what he's been up to and share his knowledge about graph theory 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 ]

Real Python

Advanced Python import Techniques

Python Virtual Environments: A Primer

How Can You Work With a Python Virtual Environment?

Create It

Activate It

The Real Python Podcast – Episode #221: Thriving as a Developer With ADHD

Python 3.13 Preview: Free Threading and a JIT Compiler

Free Threading and JIT in Python 3.13: What’s the Fuss?

Free Threading Makes the GIL Optional

JIT Compiles Python to Machine Code

Customizing VS Code Through Color Themes

Quiz: Using Python's pip to Manage Your Projects' Dependencies

Using Python's pip to Manage Your Projects' Dependencies

Getting Started With pip

Finding pip on Your System

Running pip as a Module

The Real Python Podcast – Episode #220: Configuring Git Pre-Commit Hooks & Estimating Software Projects

Quiz: Python 3.13: Free-Threading and a JIT Compiler

How to Use Conditional Expressions With NumPy where()

How to Write Conditional Expressions With NumPy where()

Quiz: Python Virtual Environments: A Primer

When to Use .__repr__() vs .__str__() in Python

Python News Roundup: September 2024

Python 3.12.5 Released

The Real Python Podcast – Episode #219: Astrophysics and Astronomy With Python & PyCon Africa 2024

Lists vs Tuples in Python

Getting Started With Python Lists and Tuples

Creating Lists in Python

Quiz: Lists vs Tuples in Python

Using Pydantic to Simplify Python Data Validation

Generate Images With DALL·E and the OpenAI API

Complete the Setup Requirements

Install the OpenAI Python Library

Get Your OpenAI API Key

Save Your API Key as an Environment Variable

Quiz: How to Use Conditional Expressions With NumPy where()

Quiz: Generate Images With DALL·E and the OpenAI API

Web Scraping With Scrapy and MongoDB

Prepare the Scraper Scaffolding

Install the Scrapy Package

Create a Scrapy Project

Using Astropy for Astronomy With Python

How to Install Python on Your System: A Guide

Windows: How to Check or Get Python

Checking the Python Version on Windows

Knowing the Python Installation Options on Windows

The Real Python Podcast – Episode #218: Exploring Robotics and Python Through Electronic Projects

Primer on Jinja Templating

Get Started With Jinja

Install Jinja

Quiz: Primer on Jinja Templating

Exploring Astrophysics in Python With pandas and Matplotlib

The Real Python Podcast – Episode #217: Packaging Data Analyses & Using pandas GroupBy

Quiz: Web Scraping With Scrapy and MongoDB

Quiz: The Walrus Operator: Python's Assignment Expressions

Sorting Dictionaries in Python: Keys, Values, and More

The Real Python Podcast – Episode #216: Learning Through Building the Black Python Devs Community

Quiz: Asynchronous Iterators and Iterables in Python

Interacting With REST APIs and Python

The Real Python Podcast – Episode #215: Using GraphQL in Django With Strawberry & Prototype Purgatory

Simulate a Text File in Python

The Real Python Podcast – Episode #214: Build Captivating Display Tables in Python With Great Tables

pandas GroupBy: Grouping Real World Data in Python

The Real Python Podcast – Episode #213: Constraint Programming & Exploring Python's Built-in Functions

The Real Python Podcast – Episode #212: Digging Into Graph Theory in Python With David Amos

Getting Started With `pip`

Finding `pip` on Your System

Running `pip` as a Module

How to Write Conditional Expressions With NumPy `where()`

When to Use .repr() vs .str() in Python