Real Python

Structural Pattern Matching in Python

2024-10-16T14:00:00+00:00

Structural pattern matching is a powerful control flow construct invented decades ago that’s traditionally used by compiled languages, especially within the functional programming paradigm.

Most mainstream programming languages have since adopted some form of pattern matching, which offers concise and readable syntax while promoting a declarative code style. Although Python was late to join the party, it introduced structural pattern matching in the 3.10 release.

In this tutorial, you’ll:

Master the syntax of the match statement and case clauses
Explore various types of patterns supported by Python
Learn about guards, unions, aliases, and name binding
Extract values from deeply nested hierarchical data structures
Customize pattern matching for user-defined classes
Identify and avoid common pitfalls in Python’s pattern matching

To get the most out of this tutorial, you should have a basic understanding of conditional statements, loops, functions, and classes in Python. Additionally, familiarity with Python’s built-in data structures, such as tuples, lists, and dictionaries, will be beneficial.

Get Your Free Code: Click here to download the free sample code that shows you how to use structural pattern matching in Python.

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

Interactive Quiz

Structural Pattern Matching

In this quiz, you'll test your understanding of structural pattern matching in Python. This powerful control flow construct, introduced in Python 3.10, offers concise and readable syntax while promoting a declarative code style.

Getting to Know Structural Pattern Matching

Before taking advantage of structural pattern matching in your code, make sure that you’re running Python 3.10 or later, as you won’t be able to use it in earlier Python versions. Note that although the name structural pattern matching is often shortened to just pattern matching, the qualifier structural is crucial to understanding the use cases for this feature. In this section, you’ll get a high-level overview of structural pattern matching.

What Is Pattern Matching?

You can think of pattern matching as a form of syntactic sugar built on top of existing language constructs, including conditional statements and tuple unpacking. While you can absolutely live without pattern matching, it gives you new superpowers, making this feature more convenient than the conventional syntax in some situations.

Pattern matching often leads to more elegant, concise, and readable code written in a declarative style. To get a taste of it, take a quick look at the following example without trying to fully understand how it works just yet:

Python
      
    
import json

def log(event):
    match json.loads(event):
        case {"keyboard": {"key": {"code": code}}}:
            print(f"Key pressed: {code}")
        case {"mouse": {"cursor": {"screen": [x, y]}}}:
            print(f"Mouse cursor: {x=}, {y=}")
        case _:
            print("Unknown event type")

The match statement takes a subject, which can be any valid Python expression, such as a string literal or a function call, and compares the resulting value to one or more patterns listed in the case clauses. The first pattern that matches the given subject will trigger the corresponding case block to run. You’ll learn more about the match statement and case clauses later in this tutorial.

At first glance, the syntax of structural pattern matching in Python looks a bit like the switch statement found in the C-family programming languages if you squint your eyes:

C
      
    
void log_event(enum Event event) {
    switch (event) {
        case KEYBOARD:
            printf("Keyboard event\n");
            break;
        case MOUSE:
            printf("Mouse event\n");
            break;
        default:
            printf("Unknown event\n");
    }
}

This resemblance is deceptive, though. The classic switch statement controls the execution flow based on the exact value stored in a variable. It effectively works as a chained sequence of mutually exclusive if..elif... equality comparisons, but with a more succinct and readable syntax.

Although you can use pattern matching this way, you’d be missing out on its true power and flexibility. Structural pattern matching was designed to go beyond value comparisons. In particular, it combines conditional statements or branching based on a logical predicate with destructuring or object deconstruction, which is the inverse of object construction. You’ll see examples of destructuring in the next section.

Note: Because pattern matching does two things at once, the Python interpreter can take advantage of this to optimize the underlying bytecode with specialized opcodes, making the code run slightly faster.

The brief code snippet above merely scratches the surface of what you can achieve with pattern matching, but it already shows you its expressiveness, especially when you compare it with the traditional if...elif... statements and isinstance() checks. Here’s one of the many ways you can implement the equivalent logic using standard Python:

Python
      
    
import json

def log(event):
    parsed_event = json.loads(event)
    if (
        "keyboard" in parsed_event and
        "key" in parsed_event["keyboard"] and
        "code" in parsed_event["keyboard"]["key"]
    ):
        code = parsed_event["keyboard"]["key"]["code"]
        print(f"Key pressed: {code}")
    elif (
        "mouse" in parsed_event and
        "cursor" in parsed_event["mouse"] and
        "screen" in parsed_event["mouse"]["cursor"]
    ):
        screen = parsed_event["mouse"]["cursor"]["screen"]
        if isinstance(screen, list) and len(screen) == 2:
            x, y = screen
            print(f"Mouse cursor: x={x}, y={y}")
        else:
            print("Unknown event type")
    else:
        print("Unknown event type")

This code is functionally identical to the previous version but is longer and has more indentation levels than before. Additionally, it looks more verbose and imperative in style, describing not only what to do but also how to perform the individual steps. Granted, you could try making it slightly shorter by using the Walrus operator and following the EAFP principle without explicit checks, but it’d remain somewhat convoluted.

It’s worth noting that structural pattern matching first emerged in compiled functional languages with static typing. The attempt to implement it in Python, which is a dynamic language, presented completely new and unique challenges. You can read more about them in the paper entitled Dynamic Pattern Matching with Python, which was co-authored by Guido van Rossum and published in the proceedings of the Dynamic Languages Symposium in 2020.

Now that you’ve seen the most basic form of pattern matching in Python, it’s time to unravel the meaning of a structural pattern.

What Is a Structural Pattern?

Read the full article at https://realpython.com/structural-pattern-matching/ »

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Using Type Hints for Multiple Return Types in Python

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

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

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

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

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Syntactic Sugar: Why Python Is Sweet and Pythonic

2024-10-14T14:00:00+00:00

Python has several pieces of syntax that are syntactic sugar. This sugar is syntax that isn’t strictly necessary but gives Python some of its flavor as a readable, beginner-friendly, and powerful language. In this tutorial, you’ll explore some of Python’s most used pieces of syntactic sugar.

In practice, you already use most of these pieces of syntax, as they include many well-known Pythonic constructs. As you read on, you’ll see how Python works under the hood and learn how to use the language efficiently and securely.

In this tutorial, you’ll learn:

What syntactic sugar is
How syntactic sugar applies to operators
How assignment expressions are syntactic sugar
How for loops and comprehensions are syntactic sugar
How other Python constructs are also syntactic sugar

To get the most out of this tutorial, you should be familiar with the basics of Python, including operators, expressions, loops, decorators, classes, context managers, and more.

Get Your Code: Click here to download the free sample code that shows you how to use syntactic sugar in Python.

Take the Quiz: Test your knowledge with our interactive “Syntactic Sugar: Why Python Is Sweet and Pythonic” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Syntactic Sugar: Why Python Is Sweet and Pythonic

You can take this quiz to test your understanding of Python's most common pieces of syntactic sugar and how they make your code more Pythonic and readable.

Syntactic Sugar

In programming, syntactic sugar refers to pieces of syntax that simplify the code and make it more readable or concise. Syntactic sugar lets you express things in a clearer and more readable way.

It makes the language sweeter for human use: things can be expressed more clearly, more concisely, or in an alternative style that some may prefer. (Source)

However, syntactic sugar is something that you may not need in practice because you can get the same result using a different, and often more involved, construct.

Note: This tutorial is slightly inspired by Brett Cannon’s series of posts about unraveling syntactic sugar in Python. In that series, Brett goes deep into each piece of syntactic sugar. You can check out the series if you’d like a detailed discussion of the syntax constructs covered in this tutorial and others.

Python has many pieces of syntactic sugar that you’ll regularly use in your code. These syntax constructs make Python more readable, quicker to write, and user-friendly. Understanding these syntactic sugar pieces and their significance will help you better understand the inner workings of Python.

In rare situations, you’ll find that desugared versions of a given piece of syntactic sugar can better fulfill your needs. So, knowing about the alternative code to a given sugar can be a good skill to have.

Operators in Python

As with most programming languages, Python makes extensive use of operators. You’ll find several categories of operators, including arithmetic, assignment, augmented assignment, comparison, Boolean, and membership operators. All these operators are part of Python’s syntactic sugar constructs because they let you write expressions in a quick and readable way.

Note: To dive deeper into Python operators, check out the Operators and Expressions in Python tutorial.

For example, arithmetic operators allow you to create math expressions that are quick to write and read because they look pretty similar to what you learned in math class:

Python
      
>>> 5 + 7
12

>>> 10 - 4
6

>>> 2 * 4
8

>>> 20 / 2
10

In the first example, you use the plus operator (+) to add two numbers. In the second example, you use the subtraction operator (-) to subtract two numbers. The final two examples perform multiplication and division.

Python supports its arithmetic operators through special methods. Here’s a quick summary:

Operator	Operation	Method
`+`	Addition	`.__add__()`
`-`	Subtraction	`.__sub__()`
`*`	Multiplication	`.__mul__()`
`/`	Division	`.__truediv__()`
`//`	Integer division	`.__floordiv__()`
`**`	Exponentiation	`.__pow__()`

What does it mean to say Python supports its operators through special methods? It means that every time you use an operator, Python calls the corresponding special method under the hood.

Note: To learn more about special methods, also known as magic or dunder methods, check out Python’s Magic Methods: Leverage Their Power in Your Classes.

To illustrate, here’s how you can express the arithmetic operations you wrote earlier using the appropriate special methods:

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

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Quiz: Python Thread Safety: Using a Lock and Other Techniques

2024-10-13T12:00:00+00:00

In this quiz, you’ll test your understanding of Python threading and thread safety. You’ll revisit concepts such as race conditions, thread safety issues, and synchronization primitives in the threading module. This knowledge is crucial when working with multithreaded code using Python’s threading module and ThreadPoolExecutor.

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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.

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Quiz: Structural Pattern Matching

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

In this quiz, you’ll test your understanding of Structural Pattern Matching in Python.

You’ll revisit the syntax of the match statement and case clauses, explore various types of patterns supported by Python, and learn about guards, unions, aliases, and name binding.

[ 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 ]

Build a Contact Book App With Python, Textual, and SQLite

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

Building projects is a great way to learn programming and have fun at the same time. When you work on a project, you apply different coding skills simultaneously, which is good practice for what you’ll do in a real-life project. In this tutorial, you’ll create a contact book application with a text-based interface (TUI) based on Python and Textual. To store the contact data, your app will use an SQLite database.

In this tutorial, you’ll learn how to:

Create the contact book app’s TUI using Textual
Handle the database operations using SQLite
Connect the app’s TUI with the database code and make it functional

At the end of this project, you’ll have a functional contact book application that will allow you to store and manage your contact information.

To get the complete source code for the application and the code for every step in this tutorial, click the link below:

Get Your Code: Click here to download the free sample code you’ll use to build a contact book app with Python, Textual, and SQLite.

Demo: A Contact Book Built With Python and Textual

Contact or address books are a widely used type of application. They can be found on phones and computers, allowing users to store and manage contact information for family, friends, coworkers, and so on.

In this tutorial, you’ll code a contact book TUI app with Python, Textual, and SQLite. Here’s a demo of how your contact book will look once you’ve followed all the steps:

Your contact book will provide a basic set of features for this type of application, and you’ll be able to display, add, and remove the information in your contacts list.

Project Overview

To build your contact book app, you’ll organize the code in a few modules under a package. In this tutorial, you’ll use the following directory structure:

rpcontacts_project/
│
├── rpcontacts/
│   ├── __init__.py
│   ├── __main__.py
│   ├── database.py
│   ├── rpcontacts.tcss
│   └── tui.py
│
├── README.md
└── requirements.txt

The root directory of your project is rpcontacts_project/. Inside, there’s an rpcontacts/ subdirectory that holds the application’s main package.

You’ll cover the content of each file in this tutorial. The name of each file will give you an idea of its role in the application.

For example, __main__.py will host the application, and database.py will provide database-related code. Similarly, rpcontacts.tcss is a CSS file that will allow you to tweak the visual style of your Textual app. Finally, tui.py will contain the code to generate the app’s TUI, including the main screen and a couple of auxiliary screens or dialogs.

Prerequisites

To get the most out of this project, you should have some previous knowledge of how to lay out a Python project and work with SQLite databases. You should also know the basics of working with Python classes. Some knowledge about writing CSS code would also be a plus.

To satisfy these knowledge requirements, you can take a look at the following resources:

Don’t worry if you don’t have all of the prerequisite knowledge before starting this tutorial—that’s completely okay! You’ll learn through the process of getting your hands dirty as you build the project. If you get stuck, then take some time to review the resources linked above. Then, get back to the code.

The contact book application you’ll build in this tutorial has a single external dependency, which is Textual. This library provides a rapid application development framework that allows you to create apps you can run in your terminal and browser.

To follow best practices in your development process, you can start by creating a virtual environment and then install Textual using pip:

Read the full article at https://realpython.com/contact-book-python-textual/ »

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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.

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Quiz: Python Closures: Common Use Cases and Examples

2024-10-08T12:00:00+00:00

In this quiz, you’ll test your understanding of Python closures. Closures are a common feature in functional programming languages and are particularly popular in Python because they allow you to create function-based decorators.

Take this quiz after reading our Python Closures: Common Use Cases and Examples tutorial.

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Python News Roundup: October 2024

2024-10-07T14:00:00+00:00

October is always an important month for Python, as this is when a new major version is released. Python 3.13 is the new version this year, and it brings several new features that lay the groundwork for other changes in the future. As one version of Python comes to life, another is put to rest. Python 3.8 is already five years old, which means that this version won’t be supported any longer.

There are also exciting developments happening in the wider Python community. In this newsletter, you can read about Polars’ improved support for plotting, as well as how Django developers gathered for the annual DjangoCon US conference.

Time to jump in and read about what’s happening in the world of Python!

Python 3.13 Release Slightly Delayed

The release of Python 3.13, the newest version of Python, was originally scheduled for October 1, 2024. However, a few days before that date, release manager Thomas Wouters decided to postpone the release until October 7, 2024:

I’m a little concerned with the impact of the incremental GC change in 3.13, which recently showed up. It’s not clear that the incremental GC provides significant improvements (although the smaller pauses are probably desirable), it clearly has slightly more overhead in common cases, and we’re still discovering pathological cases.

I don’t think we should release 3.13.0 with the incremental GC. (Source)

The incremental garbage collector was a small improvement slated for Python 3.13. In many cases, the new garbage collection algorithm improves performance. Unfortunately, it was found to slow down Python significantly in some rare cases.

As a result, the core developers decided to revert the implementation and use the traditional garbage collector in Python 3.13. At the same time, the new implementation is being scrutinized and currently the goal is to include incremental garbage collection in Python 3.14.

Delaying a major Python release is never an easy choice. However, erring on the side of caution is a good approach, and it’s great to see that the Python 3.13 release is being handled responsibly.

Python 3.13 Highlights

As always, a new Python release brings many improvements and new features. You can explore these in-depth in Python 3.13: Cool New Features for You to Try. In particular, the new release includes:

A brand new interactive interpreter (REPL)
Colored tracebacks and improved error messages
A separate, free-threaded version of Python that runs without the global interpreter lock (GIL)
An experimental just-in-time (JIT) compiler
Several improvements to Python’s static type system

For free threading and the JIT compiler, you need to compile Python with special build flags. Read Python 3.13 Preview: Free Threading and a JIT Compiler to learn more about how to explore these two new features. Additionally, Python 3.13 Preview: A Modern REPL provides more detail on the new REPL.

In addition to these features, there are many smaller changes:

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

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Quiz: Iterators and Iterables in Python: Run Efficient Iterations

2024-10-04T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s Iterators and Iterables.

By working through this quiz, you’ll revisit how to create and work with iterators and iterables, understand the differences between them, and review how to use generator functions and the yield statement.

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Quiz: Python import: Advanced Techniques and Tips

2024-10-03T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s import statement and related topics.

By working through this quiz, you’ll revisit how to use modules in your scripts and import modules dynamically at runtime.

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A Guide to Modern Python String Formatting Tools

2024-10-02T14:00:00+00:00

When working with strings in Python, you may need to interpolate values into your string and format these values to create new strings dynamically. In modern Python, you have f-strings and the .format() method to approach the tasks of interpolating and formatting strings.

In this tutorial, you’ll learn how to:

Use f-strings and the .format() method for string interpolation
Format the interpolated values using replacement fields
Create custom format specifiers to format your strings

To get the most out of this tutorial, you should know the basics of Python programming and the string data type.

Get Your Code: Click here to download the free sample code that shows you how to use modern string formatting tools in Python.

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

Interactive Quiz

A Guide to Modern Python String Formatting Tools

You can take this quiz to test your understanding of modern tools for string formatting in Python. These tools include f-strings and the .format() method.

Getting to Know String Interpolation and Formatting in Python

Python has developed different string interpolation and formatting tools over the years. If you’re getting started with Python and looking for a quick way to format your strings, then you should use Python’s f-strings.

Note: To learn more about string interpolation, check out the String Interpolation in Python: Exploring Available Tools tutorial.

If you need to work with older versions of Python or legacy code, then it’s a good idea to learn about the other formatting tools, such as the .format() method.

In this tutorial, you’ll learn how to format your strings using f-strings and the .format() method. You’ll start with f-strings to kick things off, which are quite popular in modern Python code.

Using F-Strings for String Interpolation

Python has a string formatting tool called f-strings, which stands for formatted string literals. F-strings are string literals that you can create by prepending an f or F to the literal. They allow you to do string interpolation and formatting by inserting variables or expressions directly into the literal.

Creating F-String Literals

Here you’ll take a look at how you can create an f-string by prepending the string literal with an f or F:

Python
      
   👇
>>> f"Hello, Pythonista!"
'Hello, Pythonista!'

   👇
>>> F"Hello, Pythonista!"
'Hello, Pythonista!'

Using either f or F has the same effect. However, it’s a more common practice to use a lowercase f to create f-strings.

Just like with regular string literals, you can use single, double, or triple quotes to define an f-string:

Python
      
        
      
    
    👇
>>> f'Single-line f-string with single quotes'
'Single-line f-string with single quotes'

    👇
>>> f"Single-line f-string with double quotes"
'Single-line f-string with single quotes'
     👇
>>> f'''Multiline triple-quoted f-string
... with single quotes'''
'Multiline triple-quoted f-string\nwith single quotes'

     👇
>>> f"""Multiline triple-quoted f-string
... with double quotes"""
'Multiline triple-quoted f-string\nwith double quotes'

Up to this point, your f-strings look pretty much the same as regular strings. However, if you create f-strings like those in the examples above, you’ll get complaints from your code linter if you have one.

The remarkable feature of f-strings is that you can embed Python variables or expressions directly inside them. To insert the variable or expression, you must use a replacement field, which you create using a pair of curly braces.

Interpolating Variables Into F-Strings

The variable that you insert in a replacement field is evaluated and converted to its string representation. The result is interpolated into the original string at the replacement field’s location:

Python
      
>>> site = "Real Python"

                   👇
>>> f"Welcome to {site}!"
'Welcome to Real Python!'

In this example, you’ve interpolated the site variable into your string. Note that Python treats anything outside the curly braces as a regular string.

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

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Quiz: A Guide to Modern Python String Formatting Tools

2024-10-02T12:00:00+00:00

Test your understanding of Python’s tools for string formatting, including f-strings and the .format() method.

Take this quiz after reading our A Guide to Modern Python String Formatting Tools 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 ]

Differences Between Python's Mutable and Immutable Types

2024-10-01T14:00:00+00:00

As a Python developer, you’ll have to deal with mutable and immutable objects sooner or later. Mutable objects are those that allow you to change their value or data in place without affecting the object’s identity. In contrast, immutable objects don’t allow this kind of operation. You’ll just have the option of creating new objects of the same type with different values.

In Python, mutability is a characteristic that may profoundly influence your decision when choosing which data type to use in solving a given programming problem. Therefore, you need to know how mutable and immutable objects work in Python.

In this video course, you’ll:

Understand how mutability and immutability work under the hood in Python
Explore immutable and mutable built-in data types in Python
Identify and avoid some common mutability-related gotchas
Understand and control how mutability affects your custom classes

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Quiz: When to Use a List Comprehension in Python

2024-10-01T12:00:00+00:00

In this quiz, you’ll test your understanding of List Comprehension in Python.

By working through this quiz, you’ll revisit how to rewrite loops as list comprehensions, how to choose when to use list comprehensions, how you can use conditional logic in your comprehensions, and how to profile your code to resolve performance questions.

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Python 3.13: Cool New Features for You to Try

2024-09-30T14:00:00+00:00

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

In this tutorial, 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

If you want to try any of the examples in this tutorial, then you’ll need to use Python 3.13. The tutorials 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.

Note: Python 3.13 was originally planned to be released on October 1, 2024. The developers found some issues during testing and decided to push back the release to October 7.

In addition to learning more about the new features coming to the language, you’ll also get some advice about what to consider before upgrading to the new version. Click the link below to download code examples demonstrating the new capabilities of Python 3.13:

Get Your Code: Click here to download the free sample code that shows you how to use the new features in Python 3.13.

Take the Quiz: Test your knowledge with our interactive “Python 3.13: Cool New Features for You to Try” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python 3.13: Cool New Features for You to Try

In this quiz, you'll test your understanding of the new features introduced in Python 3.13. By working through this quiz, you'll review the key updates and improvements in this version of Python.

An Improved Interactive Interpreter (REPL)

If you run Python without specifying any script or code, you’ll find yourself inside Python’s interactive interpreter. This interpreter is informally called the REPL because it’s based on a read-evaluate-print loop. The REPL reads your input, evaluates it, and prints the result before looping back and doing the same thing again.

The Python REPL has been around for decades, and it supports an explorative workflow that makes Python a beginner-friendly language. Unfortunately, the interpreter has been missing several features you may have come to expect, including multiline editing and efficient pasting of code.

Note: Experienced Python developers often install a third-party interactive interpreter instead of relying on the built-in REPL. You can learn more about the alternatives in these tutorials:

You can also read more about alternative REPLs in the guide to the standard REPL.

Begin by starting the REPL. You can do this by typing python in your terminal. Depending on your setup, you may have to write py, python3, or even python3.13 instead. One way to recognize that you’re using the new interpreter shipping with Python 3.13 is that the prompt consisting of three chevrons (>>>) is subtly colored:

One improvement is that you can now use REPL-specific commands without calling them with parentheses as if they are Python functions. Here are some of the commands and keyboard shortcuts you can use:

exit or quit: Exit the interpreter
clear: Clear the screen
help or F1: Access the help system
F2: Open the history browser
F3: Enter paste mode

You can learn more about these options in Python 3.13 Preview: A Modern REPL.

Recalling code you’ve written earlier has been cumbersome in the REPL before Python 3.13, especially if you’re working with a block of code spanning several lines. Traditionally, you’ve had to bring back each line one by one by repeatedly pressing Up. Now in 3.13, you can bring back the whole block of code with a single Up keystroke.

To try this for yourself, enter the following code in your REPL:

Python
      
        
      
    
>>> numbers = range(3, 13)
>>> [
...     (number - 3)**3 for number in numbers
...     if number % 2 == 1
... ]
[0, 8, 64, 216, 512]

You’re creating a somewhat complex list comprehension that calculates an offset cube of a range of numbers, but only if the numbers are odd. The important part is that for readability, you split the list comprehension over several lines. Now try hitting that Up key! The interpreter recalls all four lines at once, and you can continue to use your arrow keys to move around inside of the expression.

You can make changes to your code and run it again. To execute the updated code, you need to move your cursor to the end of the last line in the code block. If you press Enter inside the expression, you’ll create a new empty line instead:

The ability to recall and edit multiline statements is a huge time-saver and will make you more efficient when working with the REPL.

Read the full article at https://realpython.com/python313-new-features/ »

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Quiz: Syntactic Sugar: Why Python Is Sweet and Pythonic

2024-09-28T12:00:00+00:00

Test your understanding of Python’s most common pieces of syntactic sugar and how they make your code more Pythonic and readable.

Take this quiz after reading our Syntactic Sugar: Why Python is Sweet and Pythonic 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 ]

Quiz: Python 3.13: Cool New Features for You to Try

2024-09-28T12:00:00+00:00

In this quiz, you’ll test your understanding of Python 3.13: Cool New Features for You to Try. By working through this quiz, you’ll review the key updates and improvements in this version of 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 ]

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 ]

Python 3.13 Preview: A Modern REPL

2024-09-25T14:00:00+00:00

One of Python’s strong points is its interactive capabilities. By running python you start the interactive interpreter, or REPL, which allows you to perform quick calculations or explore and experiment with your code. In Python 3.13, the interactive interpreter has been completely redesigned with new modern features.

Python’s REPL has remained largely unchanged for decades. Instead, alternative interpreters like IPython, bpython, and ptpython have addressed some of the built-in REPL’s shortcomings, providing more convenient interactive workflows for developers. As you’re about to learn, Python 3.13 brings many significant improvements to the interactive interpreter.

In this tutorial, you’ll:

Run Python 3.13 and explore the new REPL
Browse through the help system
Work with multiline statements
Paste code into your REPL session
Navigate through your interpreter history

The upgraded REPL is just one of the new features coming in Python 3.13. You can read about all the changes in the what’s new section of Python’s changelog. Additionally, you can dig deeper into the work done on free threading and a Just-In-Time compiler.

Get Your Code: Click here to download the free sample code that shows you how to use some of the new features in Python 3.13.

A New Interactive Interpreter (REPL) in Python 3.13

To try out the new REPL for yourself, you need to get your hands on a version of Python 3.13. Before the official release in October 2024, you can install a pre-release version. After October 2024, you should be able to install Python 3.13 through any of the regular channels.

A REPL, or a Read-Eval-Print Loop, is a program that allows you to work with code interactively. The REPL reads your input, evaluates it, and prints the result before looping back and doing the same thing again.

Note: To learn about the built-in REPL in Python and how it works in general, check out The Python Standard REPL: Try Out Code and Ideas Quickly.

In any version of Python, you can start this interactive shell by typing the name of your Python executable in your terminal. Typically, this will be python, but depending on your operating system and your setup, you may have to use something like py or python3 instead.

Once you start the REPL in Python 3.13, you’ll see a small but noticeable difference. The familiar Python interactive shell prompt, consisting of three right angle brackets (>>>), is now colored differently from the rest of the text:

The color difference indicates that the shell now supports color. In the new shell, color is mainly used to highlight output in tracebacks. If your terminal doesn’t display color, then the new REPL will automatically detect this and fall back to its plain, colorless display.

If you prefer to keep your interpreter free of color even when it’s supported, you can disable this new feature. One option is to set the new environment variable PYTHON_COLORS to 0:

Shell
      
$ PYTHON_COLORS=0 python
Python 3.13.0rc2 (main, Sep 13 2024, 17:09:27) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

Setting PYTHON_COLORS=0 disables color in the REPL. If you set the environment variable to 1, you’ll get the colored prompt and output. However, since it’s the default, this is rarely necessary.

Before going any further, you’ll exit the REPL. As you may know, the old REPL, that you’ve used on Python 3.12 and earlier, has been widely commented on for the following idiosyncrasy:

Python
      
>>> exit
Use exit() or Ctrl-D (i.e. EOF) to exit

The shell clearly understands your intention to end the session. Still, it makes you jump through hoops and add parentheses to your command. In Python 3.13, the REPL now understands special commands that you can write without any parentheses:

exit or quit: Exit the interpreter
help or F1: Access the help system
clear: Clear the screen

Having these commands more easily available is a small thing, but it removes some friction when using the interactive interpreter. You can still use parentheses and type something like exit() if you prefer. These commands are not reserved though. That means that you could shadow them with variable assignments:

Python
      
        
      
    
>>> exit = True
>>> exit
True
>>> exit()
Traceback (most recent call last):
  ...
TypeError: 'bool' object is not callable

>>> del exit
>>> exit

Here you create a variable named exit, effectively disabling the exit command. To end your REPL session, you can either delete the exit variable or use one of the alternative ways to exit the interpreter.

Note: Even with the new improvements, the built-in REPL is not as powerful as some of the third-party alternatives. If you’re already using any of these, there’s no immediate reason to revert back to the standard interactive interpreter.

The main advantage of the new REPL is that it’s installed together with Python. In other words, it’ll always be available for you. This is great if you find yourself in a situation where you can’t or don’t want to install a third-party library.

You can learn more about the alternative interactive interpreters in the following tutorials:

You can also read more about alternative REPLs in the guide to the standard REPL.

Read the full article at https://realpython.com/python313-repl/ »

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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

[ 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 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/ »

[ 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 #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!"

[ 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 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/ »

[ 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 ]

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.

[ 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 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.

[ 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 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/ »

[ 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 #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 ]

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__?

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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.

[ 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 ]

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 ]

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 ]

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 ]

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 ]

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 ]

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.

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Real Python

Structural Pattern Matching in Python

Getting to Know Structural Pattern Matching

What Is Pattern Matching?

What Is a Structural Pattern?

Using Type Hints for Multiple Return Types in Python

Syntactic Sugar: Why Python Is Sweet and Pythonic

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Operators in Python

Quiz: Python Thread Safety: Using a Lock and Other Techniques

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Build a Contact Book App With Python, Textual, and SQLite

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Prerequisites

What's New in Python 3.13

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Python News Roundup: October 2024

Python 3.13 Release Slightly Delayed

Python 3.13 Highlights

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A Guide to Modern Python String Formatting Tools

Getting to Know String Interpolation and Formatting in Python

Using F-Strings for String Interpolation

Creating F-String Literals

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Differences Between Python's Mutable and Immutable Types

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Advanced Python import Techniques

Python Virtual Environments: A Primer

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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

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

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

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

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

Using Pydantic to Simplify Python Data Validation

Using Astropy for Astronomy With Python

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

Exploring Astrophysics in Python With pandas and Matplotlib

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

Sorting Dictionaries in Python: Keys, Values, and More

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

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

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

Getting Started With `pip`

Finding `pip` on Your System

Running `pip` as a Module

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