Real Python

The Real Python Podcast – Episode #187: Serializing Data With Python & Underscore Naming Conventions

2024-01-12T12:00:00+00:00

Do you need to transfer an extensive data collection for a science project? What's the best way to send executable code over the wire for distributed processing? What are the different ways to serialize data in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Python range(): Represent Numerical Ranges

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

A range is a Python object that represents an interval of integers. Usually, the numbers are consecutive, but you can also specify that you want to space them out. You can create ranges by calling range() with one, two, or three arguments, as the following examples show:

Python
      
>>> list(range(5))
[0, 1, 2, 3, 4]

>>> list(range(1, 7))
[1, 2, 3, 4, 5, 6]

>>> list(range(1, 20, 2))
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]

In each example, you use list() to explicitly list the individual elements of each range. You’ll study these examples in more detail soon.

In this tutorial, you’ll learn how you can:

Create range objects that represent ranges of consecutive integers
Represent ranges of spaced-out numbers with a fixed step
Decide when range is a good solution for your use case
Avoid range in most loops

A range can sometimes be a powerful tool. However, throughout this tutorial, you’ll also explore alternatives that may work better in some situations. You can click the link below to download the code that you’ll see in this tutorial:

Get Your Code: Click here to download the free sample code that shows you how to represent numerical ranges in Python.

Construct Numerical Ranges

In Python, range() is built in. This means that you can always call range() without doing any preparations first. Calling range() constructs a range object that you can put to use. Later, you’ll see practical examples of how to use range objects.

You can provide range() with one, two, or three integer arguments. This corresponds to three different use cases:

Ranges counting from zero
Ranges of consecutive numbers
Ranges stepping over numbers

You’ll learn how to use each of these next.

Count From Zero

When you call range() with one argument, you create a range that counts from zero and up to, but not including, the number you provided:

Python
      
>>> range(5)
range(0, 5)

Here, you’ve created a range from zero to five. To see the individual elements in the range, you can use list() to convert the range to a list:

Python
      
>>> list(range(5))
[0, 1, 2, 3, 4]

Inspecting range(5) shows that it contains the numbers zero, one, two, three, and four. Five itself is not a part of the range. One nice property of these ranges is that the argument, 5 in this case, is the same as the number of elements in the range.

Count From Start to Stop

You can call range() with two arguments. The first value will be the start of the range. As before, the range will count up to, but not include, the second value:

Python
      
>>> range(1, 7)
range(1, 7)

The representation of a range object just shows you the arguments that you provided, so it’s not super helpful in this case. You can use list() to inspect the individual elements:

Python
      
>>> list(range(1, 7))
[1, 2, 3, 4, 5, 6]

Observe that range(1, 7) starts at one and includes the consecutive numbers up to six. Seven is the limit of the range and isn’t included. You can calculate the number of elements in a range by subtracting the start value from the end value. In this example, there are 7 - 1 = 6 elements.

Count From Start to Stop While Stepping Over Numbers

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

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Python Basics Exercises: Functions and Loops

2024-01-09T14:00:00+00:00

As you learned in Python Basics: Functions and Loops, functions serve as the fundamental building blocks in almost every Python program. They’re where the real action happens!

You now know that functions are crucial for breaking down code into smaller, manageable chunks. They enable you to define actions that your program can execute repeatedly throughout your code. Instead of duplicating the same code whenever your program needs to accomplish a particular task, you can simply call the function.

However, there are instances when you need to repeat certain code multiple times in a row. This is where loops become invaluable.

In this Python Basics Exercises video course, you’ll practice:

Creating user-defined functions
Implementing for loops
Getting user input
Rounding numbers

This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out the other Python Basics courses.

Note that you’ll be using IDLE to interact with Python throughout this course.

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Learn From 2023's Most Popular Python Tutorials and Courses

2024-01-08T14:00:00+00:00

Python is always getting better, and 2023 brought a number of exciting developments. As part of the Faster CPython project, Python 3.12 is speedier than previous versions. The new release brings improved error messages and more powerful f-strings. You can also enjoy support for the Linux perf profiler, static typing improvements, and changes to subinterpreters.

The Python Software Foundation (PSF) focused on enhancing security in 2023, with the help of inaugural Security Developer-in-Residence Seth Michael Larson. As part of this comprehensive effort, PyPI completed its first security audit. Plus, the year brought new major versions of pandas, Django, and Pydantic. Another exciting development was Mojo, a superset of Python that’s specifically designed for use in machine-learning contexts.

Here at Real Python, we’ve published over a hundred written tutorials and video courses this year, plus weekly podcast episodes. Keep reading to revisit an old favorite or embark on a new learning journey. You can also check out the wrap-up podcast episode.

Join Now: Click here to join the Real Python Newsletter and you'll never miss another Python tutorial, course update, or post.

Practice Your Skills

Practice makes perfect, especially when it comes to programming. This year, the Real Python team expanded the Python Basics learning path with exercises designed to strengthen your Python skills and help you apply your knowledge in real-world scenarios.

In these courses, you can test your knowledge by completing review exercises and challenges before comparing your work to the instructors’ solutions:

By actively engaging with these exercises, you’ll solidify your understanding of Python concepts and build confidence in your programming abilities. Another way to test your knowledge is by taking quizzes, and we added several in 2023. For help along the way, be sure to join Real Python’s community chat and weekly office hours.

Customize Your Coding Environment

Your coding environment plays a crucial role in your productivity as a programmer, so it’s definitely worthwhile to explore your options and make yourself at home. That means picking the perfect editor or IDE for your use case and then tweaking how it works for you.

In these tutorials, you’ll learn how to customize your coding environment to suit your needs:

By exploring these resources, you’ll discover tools and techniques that can enhance your coding experience, making you more efficient and comfortable as you write Python code.

Read the full article at https://realpython.com/popular-python-tutorials-2023/ »

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The Real Python Podcast – Episode #186: Exploring Python in Excel

2024-01-05T12:00:00+00:00

Are you interested in using your Python skills within Excel? Would you like to share a data science project or visualization as a single Office file? This week on the show, we speak with Principal Architect John Lam and Sr. Cloud Developer Advocate Sarah Kaiser from Microsoft about Python in Excel.

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Python's Magic Methods: Leverage Their Power in Your Classes

2024-01-03T14:00:00+00:00

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

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

In this tutorial, you’ll:

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

To get the most out of this tutorial, you should be familiar with general Python programming. More importantly, you should know the basics of object-oriented programming and classes in Python.

Get Your Code: Click here to download the free sample code that shows you how to use Python’s magic methods in your classes.

Getting to Know Python’s Magic or Special Methods

In Python, special methods are also called magic methods, or dunder methods. This latter terminology, dunder, refers to a particular naming convention that Python uses to name its special methods and attributes. The convention is to use double leading and trailing underscores in the name at hand, so it looks like .__method__().

Note: In this tutorial, you’ll find the terms special methods, dunder methods, and magic methods used interchangeably.

The double underscores flag these methods as core to some Python features. They help avoid name collisions with your own methods and attributes. Some popular and well-known magic methods include the following:

Special Method	Description
`.__init__()`	Provides an initializer in Python classes
`.__str__()` and `.__repr__()`	Provide string representations for objects
`.__call__()`	Makes the instances of a class callable
`.__len__()`	Supports the `len()` function

This is just a tiny sample of all the special methods that Python has. All these methods support specific features that are core to Python and its object-oriented infrastructure.

Note: For the complete list of magic methods, refer to the special method section on the data model page of Python’s official documentation.

The Python documentation organizes the methods into several distinct groups:

Take a look at the documentation for more details on how the methods work and how to use them according to your specific needs.

Here’s how the Python documentation defines the term special methods:

A method that is called implicitly by Python to execute a certain operation on a type, such as addition. Such methods have names starting and ending with double underscores. (Source)

There’s an important detail to highlight in this definition. Python implicitly calls special methods to execute certain operations in your code. For example, when you run the addition 5 + 2 in a REPL session, Python internally runs the following code under the hood:

Python
      
>>> (5).__add__(2)
7

The .__add__() special method of integer numbers supports the addition that you typically run as 5 + 2.

Reading between the lines, you’ll realize that even though you can directly call special methods, they’re not intended for direct use. You shouldn’t call them directly in your code. Instead, you should rely on Python to call them automatically in response to a given operation.

Note: Even though special methods are also called magic methods, some people in the Python community may not like this latter terminology. The only magic around these methods is that Python calls them implicitly under the hood. So, the official documentation refers to them as special methods instead.

Magic methods exist for many purposes. All the available magic methods support built-in features and play specific roles in the language. For example, built-in types such as lists, strings, and dictionaries implement most of their core functionality using magic methods. In your custom classes, you can use magic methods to make callable objects, define how objects are compared, tweak how you create objects, and more.

Note that because magic methods have special meaning for Python itself, you should avoid naming custom methods using leading and trailing double underscores. Your custom method won’t trigger any Python action if its name doesn’t match any official special method names, but it’ll certainly confuse other programmers. New dunder names may also be introduced in future versions of Python.

Magic methods are core to Python’s data model and are a fundamental part of object-oriented programming in Python. In the following sections, you’ll learn about some of the most commonly used special methods. They’ll help you write better object-oriented code in your day-to-day programming adventure.

Controlling the Object Creation Process

When creating custom classes in Python, probably the first and most common method that you implement is .__init__(). This method works as an initializer because it allows you to provide initial values to any instance attributes that you define in your classes.

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

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HTTP Requests With Python's urllib.request

2024-01-02T14:00:00+00:00

If you need to perform HTTP requests using Python, then the widely used Requests library is often the way to go. However, if you prefer to use only standard-library Python and minimize dependencies, then you can turn to urllib.request instead.

In this video course, you’ll:

Learn the essentials of making basic HTTP requests with urllib.request
Explore the inner workings of an HTTP message and how urllib.request represents it
Grasp the concept of handling character encodings in HTTP messages
Understand common hiccups when using urllib.request and learn how to resolve them

If you’re already familiar with HTTP requests such as GET and POST, then you’re well prepared for this video course. Additionally, you should have prior experience using Python to read and write files, ideally using a context manager.

In the end, you’ll discover that making HTTP requests doesn’t have to be a frustrating experience, despite its reputation. Many of the challenges people face in this process stem from the inherent complexity of the Internet. The good news is that the urllib.request module can help demystify much of this complexity.

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Python's Array: Working With Numeric Data Efficiently

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

When you start your programming adventure, one of the most fundamental concepts that you encounter early on is the array. If you’ve recently switched to Python from another programming language, then you might be surprised that arrays are nowhere to be found as a built-in syntactical construct in Python. Instead of arrays, you typically use lists, which are slightly different and more flexible than classic arrays.

That said, Python ships with the lesser-known array module in its standard library, providing a specialized sequence type that can help you process binary data. Because it’s not as widely used or well documented as other sequences, there are many misconceptions surrounding the use of the array module. After reading this tutorial, you’ll have a clear idea of when to use Python’s array module and the corresponding data type that it provides.

In this tutorial, you’ll learn how to:

Create homogeneous arrays of numbers in Python
Modify numeric arrays just like any other sequence
Convert between arrays and other data types
Choose the right type code for Python arrays
Emulate nonstandard types in arrays
Pass a Python array’s pointer to a C function

Before you dive in, you may want to brush up on your knowledge of manipulating Python sequences like lists and tuples, defining custom classes and data classes, and working with files. Ideally, you should be familiar with bitwise operators and be able to handle binary data in Python.

You can download the complete source code and other resources mentioned in this tutorial by clicking the link below:

Get Your Code: Click here to download the free source code that shows you how to use Python’s array with your numeric data.

Understanding Arrays in Programming

Some developers treat arrays and Python’s lists as synonymous. Others argue that Python doesn’t have traditional arrays, as seen in languages like C, C++, or Java. In this brief section, you’ll try to answer whether Python has arrays.

Arrays in Computer Science

To understand arrays better, it helps to zoom out a bit and look at them through the lens of theory. This will clarify some baseline terminology, including:

Abstract data types
Data structures
Data types

Computer science models collections of data as abstract data types (ADTs) that support certain operations like insertion or deletion of elements. These operations must satisfy additional constraints that describe the abstract data type’s unique behaviors.

The word abstract in this context means these data types leave the implementation details up to you, only defining the expected semantics or the set of available operations that an ADT must support. As a result, you can often represent one abstract data type using a few alternative data structures, which are concrete implementations of the same conceptual approach to organizing data.

Programming languages usually provide a few data structures in the form of built-in data types as a convenience so that you don’t have to implement them yourself. This means you can focus on solving more abstract problems instead of starting from scratch every time. For example, the Python dict data type is a hash table data structure that implements the dictionary abstract data type.

To reiterate the meaning of these terms, abstract data types define the desired semantics, data structures implement them, and data types represent data structures in programming languages as built-in syntactic constructs.

Some of the most common examples of abstract data types include these:

In some cases, you can build more specific kinds of abstract data types on top of existing ADTs by incorporating additional constraints. For instance, you can build a stack by modifying the queue or the other way around.

As you can see, the list of ADTs doesn’t include arrays. That’s because the array is a specific data structure representing the list abstract data type. The list ADT dictates what operations the array must support and which behaviors it should exhibit. If you’ve worked with the Python list, then you should already have a pretty good idea of what the list in computer science is all about.

Note: Don’t confuse the list abstract data type in computer science with the list data type in Python, which represents the former. Similarly, it’s easy to mistake the theoretical array data structure for a specific array data type, which many programming languages provide as a convenient primitive type built into their syntax.

The list abstract data type is a linear collection of values forming an ordered sequence of elements. These elements follow a specific arrangement, meaning that each element has a position relative to the others, identified by a numeric index that usually starts at zero. The list has a variable but finite length. It may or may not contain values of different types, as well as duplicates.

The interface of the list abstract data type resembles Python’s list, typically including the following operations:

List ADT	Python’s `list`
Get an element by an index	`fruits[0]`
Set an element at a given index	`fruits[0] = "banana"`
Insert an element at a given index	`fruits.insert(0, "banana")`
Delete an element by an index	`fruits.pop(0)`, `del fruits[0]`
Delete an element by a value	`fruits.remove("banana")`
Delete all elements	`fruits.clear()`
Find the index of a given element	`fruits.index("banana")`
Append an element at the right end	`fruits.append("banana")`
Merge with another list	`fruits.extend(veggies)`, `fruits + veggies`
Sort elements	`fruits.sort()`
Get the number of elements	`len(fruits)`
Iterate over the elements	`iter(fruits)`
Check if an element is present	`"banana" in fruits`

Now that you understand where the array data structure fits into the bigger picture, it’s time to take a closer look at it.

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

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The Real Python Podcast – Episode #185: 2023 Real Python Tutorial & Video Course Wrap-Up

2023-12-29T12:00:00+00:00

Three members of the Real Python team are joining us this week: Kate Finegan, Tappan Moore, and Philipp Acsany. We wanted to share a year-end wrap-up with tutorials, step-by-step projects, code conversations, and video courses that showcase what our team created this year.

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The Real Python Podcast – Episode #184: PyCoder's Weekly 2023 Wrap Up

2023-12-22T12:00:00+00:00

It's been a fascinating year for the Python language and community. PyCoder's Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2023. Christopher Trudeau is back on the show this week to help wrap up everything by sharing some highlights and Python trends from across the year.

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Add Logging and Notification Messages to Flask Web Projects

2023-12-20T14:00:00+00:00

After implementing the main functionality of a web project, you may find that your app is rough around the edges. Often, this boils down to the user experience (UX) of your project. For example, when you send a form, you don’t receive a success message, although everything worked fine. You want to build an immersive experience, but wrong URLs display unwelcoming error messages. Luckily, you can get valuable information from Flask through logging.

As an admin of a web project, you may know how to navigate your app without running into errors. But looking at the messages that the server sends you, you may notice that not all users do. That’s when a more verbose logging system can give you insights.

In this tutorial, you’ll learn how to:

Create error pages
Improve the user experience with notification messages
Gain information with logging

Adding features like the ones listed above may keep your current functionality intact and enhance your web project with a better user experience that your visitors will love.

Get Your Code: Click here to download the free source code that shows you how to add error pages and logging to your Flask app.

Prerequisites

You’ll gain the most value from this tutorial if you’ve already created one or more Flask web projects yourself. It’ll also be helpful if you’ve worked with Jinja templating and HTML and CSS before.

Also, you should be comfortable using the terminal and have basic knowledge of Python. Although it helps to know about virtual environments and pip, you’ll learn how to set everything up as you work through the tutorial.

Project Overview

In this tutorial, you’ll continue to work on an existing Flask project. The project at hand is a public message board.

You’ll start with an existing Flask project and then implement logging and notification messages:

The demo video above shows the Flask project on the left and the terminal with server logs on the right. When you interact with the web app, you get helpful feedback with notification messages. As an admin, you can also see the info messages in your logs to help you understand how your users interact with your app.

In the next step, you’ll download the source code of the Flask project. However, you’ll notice that the codebase is quite generic so that you can transfer the instructions of this tutorial into your own Flask project.

Get Started

In this section, you’ll download all the requirements that you need for this tutorial and set up the development environment. Generally, you can leverage this tutorial to expand any Flask project that you’re currently working on. However, if you want to follow along closely, then you should perform the steps outlined below.

Grab the Prerequisites

To hit the ground running, you’ll build up on an existing Flask project with a database. That way, you don’t need to create a Flask project from scratch. Instead, you can focus on the main objectives of this tutorial, like adding an error page and displaying helpful messages to your users.

The code that you need is already in place for you. All you need to do is download the source code by clicking the link below:

Get Your Code: Click here to download the free source code that shows you how to add error pages and logging to your Flask app.

Alternatively, you can follow the Flask project with a database tutorial. Either way, you should end up with a folder structure that looks like this:

rp_flask_board/
├── board/
│   │
│   ├── static/
│   │   └── styles.css
│   │
│   ├── templates/
│   │   │
│   │   ├── pages/
│   │   │   ├── about.html
│   │   │   └── home.html
│   │   │
│   │   ├── posts/
│   │   │   ├── create.html
│   │   │   └── posts.html
│   │   │
│   │   ├── _navigation.html
│   │   └── base.html
│   │
│   ├── __init__.py
│   ├── database.py
│   ├── pages.py
│   ├── posts.py
│   └── schema.sql
│
└── board.sqlite

Once you’ve got the folder structure for your Flask project in place, you can read on to prepare the development environment that you’ll need to work on your web app.

Prepare Your Development Environment

Read the full article at https://realpython.com/flask-logging-messages/ »

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Python Basics Exercises: Reading and Writing Files

2023-12-19T14:00:00+00:00

Files play a key role in computing, as they store and transfer data. You likely come across numerous files on a daily basis. In Python Basics: Reading and Writing Files, you dove into the world of file manipulation using Python.

In this video course, you’ll practice:

Differentiating between text and binary files
Using character encodings and line endings
Manipulating file objects in Python
Reading and writing character data with different file modes
Using open(), Path.open(), and the with statement
Leveraging the csv module to manipulate CSV data

This video course is part of the Python Basics series, which is complements the book Python Basics: A Practical Introduction to Python 3. Additionally, you can explore other Python Basics courses.

Please note that throughout this course, you’ll be using IDLE to interact with Python. If you’re new to Python, then you might want to check out Python Basics: Setting Up Python before diving into this course.

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Enhance Your Flask Web Project With a Database

2023-12-18T14:00:00+00:00

Adding a database to your Flask project comes with many advantages. By connecting a database, you’ll be able to store user information, monitor user interactions, and maintain dynamic site content.

In this tutorial, you’ll learn how to:

Hide secrets in environment variables
Formulate your database structure with models
Connect your Flask app with a database
Receive data from users with forms

By the end of this tutorial, you’ll have a robust understanding of how to connect your Flask app to a database and how to leverage this connection to expand your project’s capabilities.

Get Your Code: Click here to download the free source code for connecting a database to your Flask project.

Prerequisites

You’ll gain the most value from this tutorial if you’ve already created one or more Flask web projects yourself.

Project Overview

In this tutorial, you’ll continue to work on an existing Flask project. The project at hand is a public message board.

You’ll start with an existing Flask project and then implement forms and a database. In the end, you can post and save messages:

In the demo video above, you get an impression of how you can interact with the message board. However, the functionality is very similar to other interactions with a web app. If you feel inspired to adjust the project to your needs, then this Flask project is the perfect starting ground for that.

In the next step, you’ll download the source code of the Flask boilerplate project. However, you’ll notice that the codebase is quite generic so that you can transfer the instructions of this tutorial into your own Flask project.

Get Started

Grab the Prerequisites

To hit the ground running, you’ll build upon an existing Flask boilerplate project. That way, you don’t need to create a Flask project from scratch. Instead, you can focus on the main objectives of this tutorial, like adding a database and working with forms.

The code that you need is already in place for you. All you need to do is download the source code by clicking the link below:

Get Your Code: Click here to download the free source code for connecting a database to your Flask project.

Alternatively, you can follow the Flask boilerplate project tutorial. Either way, you should end up with a folder structure that looks like this:

rp_flask_board/
│
└── board/
    │
    ├── static/
    │   └── styles.css
    │
    ├── templates/
    │   │
    │   ├── pages/
    │   │   ├── about.html
    │   │   └── home.html
    │   │
    │   ├── _navigation.html
    │   └── base.html
    │
    ├── __init__.py
    └── pages.py

Once you’ve got the folder structure for your Flask project in place, you can read on to prepare the development environment that you’ll need to work on your web app.

Prepare Your Development Environment

Before you continue working on your Flask project, it’s a good idea to create and activate a virtual environment. That way, you’re installing any project dependencies not system-wide but only in your project’s virtual environment.

Read the full article at https://realpython.com/flask-database/ »

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Build a Scalable Flask Web Project From Scratch

2023-12-13T14:00:00+00:00

Flask is a powerful and flexible micro web framework for Python, ideal for both small and large web projects. It provides a straightforward way to get a web application up and running, with all the features that you need to get started.

Over the course of this tutorial, you’ll explore the process of creating a boilerplate for a Flask web project. This boilerplate will serve as a great starting point for any scalable Flask web app that you wish to develop in the future, from basic web pages to complex web applications.

In this tutorial, you’ll learn how to:

Set up a Flask project
Create a "Hello, World!" Flask app
Add multiple pages with blueprints
Leverage Jinja templates
Store static files

At the end of this tutorial, you’ll have created a working Flask web application that you can use to bootstrap your future Flask projects.

Get Your Code: Click here to download the free source code that shows you how to build a Flask project boilerplate.

Prerequisites

You don’t need any previous knowledge of Flask to complete this project. If you want to learn more about the topics that you encounter in this tutorial, then you’ll find links to resources along the way.

However, you should be comfortable using the terminal and have basic knowledge of Python. Although it helps to know about virtual environments and pip, you’ll learn how to set everything up as you work through the tutorial.

Project Overview

In this tutorial, you’ll start building a Flask project, which could become a public message board at some point. Here’s what the project will look at the end of this tutorial:

You create a basic Flask project with two pages that inherit content and style from a parent template.

The Flask project that you’ll build in this tutorial will be very generic. That way, it’s the perfect base for any of your future projects. For example, maybe you’ll want to add a database or implement logging and notification messages.

Get Started

In this section, you’ll prepare the development environment for your Flask project. First, you’ll create a virtual environment and install all the dependencies that you need for your project.

Create a Virtual Environment

In this section, you’ll build your project structure. You can name the root folder of your project any way you like. For example, you could name it rp_flask_board/ if your ultimate goal is to create a message board. If you have another project in mind, then feel free to name your folder accordingly. Create your folder and navigate into it:

Shell
      
$ mkdir rp_flask_board
$ cd rp_flask_board

In this case, you name the root folder of your project rp_flask_board/. The files and folders that you create over the course of this series will be located in either this folder or its subfolders.

After you navigate to the project folder, it’s a good idea to create and activate a virtual environment. That way, you’re installing any project dependencies not system-wide but only in your project’s virtual environment.

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

Read the full article at https://realpython.com/flask-project/ »

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Python Basics Exercises: Strings and String Methods

2023-12-12T14:00:00+00:00

In Python Basics: Strings and String Methods, you used strings for text data in Python. You also learned how to manipulate strings with string methods. For example, you changed strings from lowercase to uppercase, removed whitespace from the beginning or end of a string, and replaced parts of a string with different text.

In this video course, you’ll practice:

Manipulating strings with string methods
Working with user input
Dealing with strings of numbers
Formatting strings for printing

This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3.

Note that you’ll be using IDLE to interact with Python throughout this course. If you’re just getting started, then you might want to check out Python Basics: Setting Up Python before diving into this course.

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Python News: What's New From November 2023

2023-12-11T14:00:00+00:00

November brought exciting news to the Python community, from PyPI’s first security audit to a new version of PyScript! The month also gave Python developers like you ample opportunities to get involved in the ecosystem through the annual Python Developers Survey and the PyCon US call for proposals. Development has also continued on Python 3.13 and Pydantic.

Get ready to explore the recent highlights!

Join Now: Click here to join the Real Python Newsletter and you'll never miss another Python tutorial, course update, or post.

PyPI Completes First Security Audit

With the support of the Open Technology Fund (OTF), the Python Package Index (PyPI) completed its first external security audit in November. Because PyPI is the official index and repository for the Python ecosystem, maintaining its security is of vital importance for the community at large.

Note: PyPI has been doing a lot to boost security recently, including hiring a safety and security engineer. You can hear from him on The Real Python Podcast Episode 177: Welcoming PyPI’s Safety & Security Engineer Mike Fiedler.

The audit began in late summer 2023 and involved searching for security vulnerabilities in Warehouse and cabotage, the codebases that power and deploy PyPI. Trail of Bits, a security firm with significant open-source and Python experience, performed the audit.

Overall, the auditors didn’t identify any high-severity issues in either of the codebases, which is great news. The audit did flag some issues, like weak signature verification, unintentional information leaks, and weak cryptographic hashes, but but ultimately noted that the codebases demonstrated best practices in the industry. The PyPI team has already made the repository safer by working to remediate the risks that came up in the audit.

If you’d like to learn more about how Trail of Bits conducted the audit and what the team found, then check out the blog post on the audit. You can also read the full report.

Python 3.13.0a2 Released

Just over a month ago, Python 3.12 introduced a ton of cool new features. But work never stops, and now the second alpha version of Python 3.13 is available.

Note: This is an alpha release, which isn’t intended for production environments. Instead, alpha versions are useful for testing features and fixing bugs.

The most notable change for this release, compared to 3.12, is the elimination of many modules. Python 3.13 closes out a deprecation schedule that began in Python 3.11 with PEP 594. If you’ve been using these deprecated modules on newer Python versions, then you’ve likely run into a DeprecationWarning. Some of the modules eliminated are aifc, audioop, cgi, cgitb, crypt, pipes, telnetlib, and lib2to3.

But Python 3.13 isn’t just about deprecation. It also adds improvements to some modules. Here are a few examples:

In asyncio, the asyncio.loop.create_unix_server() method will now automatically remove the Unix socket when the connection to the server is closed, which means you won’t have an unnecessary socket file hanging around on disk.
In the copy module, copy.replace() makes working with immutable objects more convenient by allowing you to create a modified copy.
In the ipaddress module, the new ipaddress.IPV4Address.ipv6_mapped property lets you represent an IPv4 address as an IPv6 address.

This is just a small sampling of the new functionality added to this version. To see the full list of removals, deprecations, additions, and improvements in 3.13.0a2, check out What’s New In Python 3.13.

Read the full article at https://realpython.com/python-news-november-2023/ »

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The Real Python Podcast – Episode #183: Exploring Code Reviews in Python and Automating the Process

2023-12-08T12:00:00+00:00

What goes into a code review in Python? Is there a difference in how a large organization practices code review compared to a smaller one? What do you do if you're a solo developer? This week on the show, Brendan Maginnis and Nick Thapen from Sourcery return to talk about code review and automated code assistance.

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Build a Hangman Game With Python and PySimpleGUI

2023-12-06T14:00:00+00:00

Wouldn’t it be cool to build a hangman game with a nice graphical user interface (GUI) in Python? Maybe you’ve built a text-based user interface version of hangman, and now you want to make an even more attractive game. For you as a Python programmer, building a GUI version of this game can be a rewarding challenge that can take you to the next skill level.

Throughout this tutorial, you’ll build the hangman game in Python in a series of steps. The game will have a neat graphical interface based on the PySimpleGUI library.

In this tutorial, you’ll:

Create a GUI for your hangman game using PySimpleGUI
Implement the logic for the hangman game
Connect the game’s logic with its GUI layer
Handle the game results and provide other options

To get the most from this tutorial, you should be comfortable with general Python programming, including how to lay out a project. You should also have some experience working with graphical user interfaces, especially those based on PySimpleGUI. You don’t need to have any previous knowledge about game programming, but it would be a plus.

All the code that you’ll write in this tutorial is available for download. You can get it by clicking the link below:

Get Your Code: Click here to download the free Python source code for your PySimpleGUI hangman game.

Demo: Hangman With Python and PySimpleGUI

In this tutorial, you’ll write an implementation of hangman using Python and PySimpleGUI. Once you’ve run all the steps to build the game, then you’ll end up with a graphical user interface (GUI) that will work something like the following:

In this tutorial, you’ll run through several challenges designed to help you solve specific game-related problems. Ultimately, you’ll have a hangman game that works like the demo above.

Project Overview

When creating a GUI version of hangman, you’ll use a GUI framework that can handle a text-based game like hangman while still providing the ability to draw a hanged man. There are several good GUI engines available for Python. While you can use any of them to good effect, you’ll rely on PySimpleGUI in this tutorial.

PySimpleGUI is a wrapper around several well-known GUI frameworks and libraries, such as Tkinter, wxPython, and PyQt. It allows you to design a GUI as data. You’ll use the Tkinter wrapper in this tutorial, but you can explore the wxPython and QT wrappers on PyPI.

While many people know hangman well, it’s helpful to have a formal description of the game. You’ll use this description to resolve programming issues later during development. The game’s description could vary from person to person. In this tutorial, you’ll describe the game as follows:

Game setup: The game of hangman is for two or more players, comprising a selecting player and one or more guessing players.
Word selection: The selecting player selects a word that the guessing players will try to guess.
- The selected word is traditionally represented as a series of underscores for each letter in the word.
- The selecting player also draws a scaffold to hold the hangman illustration.
Guessing: The guessing players attempt to guess the word by selecting letters one at a time.
Feedback: The selecting player indicates whether each guessed letter appears in the word.
- If the letter appears, then the selecting player replaces each underscore with the letter as it appears in the word.
- If the letter doesn’t appear, then the selecting player writes the letter in a list of guessed letters. Then, they draw the next piece of the hanged man. To draw the hanged man, they begin with the head, then the torso, the arms, and the legs for a total of six parts.
Winning conditions: The selecting player wins if the hanged man drawing is complete after six incorrect guesses, in which case the game is over. The guessing players win if they guess the word.
- If the guess is right, the game is over, and the guessing players win.
- If the guess is wrong, the game continues.

A game in progress is shown below. In this game, the word is hangman:

In this tutorial, you’ll make a few additional design decisions while writing the hangman game in Python:

The game will be between the computer and one human player.
The computer will act as the selecting player and will select the word to guess, process human input, and handle all output.
The human player is the guessing player, hereafter simply referred to as the player. When the player knows the word, they continue to guess correct letters until the word is complete.

With this basic understanding of the game and some design decisions for the computer version, you can begin creating the game. First, however, you need to be aware of some knowledge requirements.

Prerequisites

The project that that you’ll build in this tutorial will require familiarity with general Python programming. You should have basic knowledge of the following topics:

Creating GUI apps with PySimpleGUI
Working with files and the with statement
Defining your own classes and working with object-oriented programming
Writing for loops, while loops, and comprehensions
Working with conditional statements
Working with Python strings, lists, and sets

However, if you don’t have all this knowledge yet, then that’s okay! You might learn more by going ahead and giving the project a shot. You can always stop and review the resources linked here if you get stuck.

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

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How to Get the Current Time in Python

2023-12-05T14:00:00+00:00

Getting the current time in Python is a nice starting point for many time-related operations. One very important use case is creating timestamps. In this tutorial, you’ll learn how to get, display, and format the current time with the datetime module.

To effectively use the current time in your Python applications, you’ll add a few tools to your belt. For instance, you’ll learn how to read attributes of the current time, like the year, minutes, or seconds. To make the time more easily readable, you’ll explore options for printing it. You’ll also get to know different formats of time and understand how to deal with time zones.

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Serialize Your Data With Python

2023-12-04T14:00:00+00:00

Whether you’re a data scientist crunching big data in a distributed cluster, a back-end engineer building scalable microservices, or a front-end developer consuming web APIs, you should understand data serialization. In this comprehensive guide, you’ll move beyond XML and JSON to explore several data formats that you can use to serialize data in Python. You’ll explore them based on their use cases, learning about their distinct categories.

By the end of this tutorial, you’ll have a deep understanding of the many data interchange formats available. You’ll master the ability to persist and transfer stateful objects, effectively making them immortal and transportable through time and space. Finally, you’ll learn to send executable code over the network, unlocking the potential of remote computation and distributed processing.

In this tutorial, you’ll learn how to:

Choose a suitable data serialization format
Take snapshots of stateful Python objects
Send executable code over the wire for distributed processing
Adopt popular data formats for HTTP message payloads
Serialize hierarchical, tabular, and other shapes of data
Employ schemas for validating and evolving the structure of data

To get the most out of this tutorial, you should have a good understanding of object-oriented programming principles, including classes and data classes, as well as type hinting in Python. Additionally, familiarity with the HTTP protocol and Python web frameworks would be a plus. This knowledge will make it easier for you to follow along with the tutorial.

You can download all the code samples accompanying this tutorial by clicking the link below:

Get Your Code: Click here to download the free sample code that shows you how to serialize your data with Python.

Feel free to skip ahead and focus on the part that interests you the most, or buckle up and get ready to catapult your data management skills to a whole new level!

Get an Overview of Data Serialization

Serialization, also known as marshaling, is the process of translating a piece of data into an interim representation that’s suitable for transmission through a network or persistent storage on a medium like an optical disk. Because the serialized form isn’t useful on its own, you’ll eventually want to restore the original data. The inverse operation, which can occur on a remote machine, is called deserialization or unmarshaling.

Note: Although the terms serialization and marshaling are often used interchangeably, they can have slightly different meanings for different people. In some circles, serialization is only concerned with the translation part, while marshaling is also about moving data from one place to another.

The precise meaning of each term depends on whom you ask. For example, Java programmers tend to use the word marshaling in the context of remote method invocation (RMI). In Python, marshaling refers almost exclusively to the format used for storing the compiled bytecode instructions.

Check out the comparison of serialization and marshaling on Wikipedia for more details.

The name serialization implies that your data, which may be structured as a dense graph of objects in the computer’s memory, becomes a linear sequence—or a series—of bytes. Such a linear representation is perfect to transmit or store. Raw bytes are universally understood by various programming languages, operating systems, and hardware architectures, making it possible to exchange data between otherwise incompatible systems.

When you visit an online store using your web browser, chances are it runs a piece of JavaScript code in the background to communicate with a back-end system. That back end might be implemented in Flask, Django, or FastAPI, which are Python web frameworks. Because JavaScript and Python are two different languages with distinct syntax and data types, they must share information using an interchange format that both sides can understand.

In other words, parties on opposite ends of a digital conversation may deserialize the same piece of information into wildly different internal representations due to their technical constraints and specifications. However, it would still be the same information from a semantic point of view.

Tools like Node.js make it possible to run JavaScript on the back end, including isomorphic JavaScript that can run on both the client and the server in an unmodified form. This eliminates language discrepancies altogether but doesn’t address more subtle nuances, such as big-endian vs little-endian differences in hardware.

Other than that, transporting data from one machine to another still requires converting it into a network-friendly format. Specifically, the format should allow the sender to partition and put the data into network packets, which the receiving machine can later correctly reassemble. Network protocols are fairly low-level, so they deal with streams of bytes rather than high-level data types.

Depending on your use case, you’ll want to pick a data serialization format that offers the best trade-off between its pros and cons. In the next section, you’ll learn about various categories of data formats used in serialization. If you already have prior knowledge about these formats and would like to explore their respective scenarios, then feel free to skip the basic introduction coming up next.

Compare Data Serialization Formats

There are many ways to classify data serialization formats. Some of these categories aren’t mutually exclusive, making certain formats fall under a few of them simultaneously. In this section, you’ll find an overview of the different categories, their trade-offs, and use cases, as well as examples of popular data serialization formats.

Later, you’ll get your hands on some practical applications of these data serialization formats under different programming scenarios. To follow along, download the sample code mentioned in the introduction and install the required dependencies from the included requirements.txt file into an active virtual environment by issuing the following command:

Shell
      
(venv) $ python -m pip install -r requirements.txt

This will install several third-party libraries, frameworks, and tools that will allow you to navigate through the remaining part of this tutorial smoothly.

Textual vs Binary

At the end of the day, all serialized data becomes a stream of bytes regardless of its original shape or form. But some byte values—or their specific arrangement—may correspond to Unicode code points with a meaningful and human-readable representation. Data serialization formats whose syntax consists purely of characters visible to the naked eye are called textual data formats, as opposed to binary data formats meant for machines to read.

The main benefit of a textual data format is that people like you can read serialized messages, make sense of them, and even edit them by hand when needed. In many cases, these data formats are self-explanatory, with descriptive element or attribute names. For example, take a look at this excerpt from the Real Python web feed with information about the latest tutorials and courses published:

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

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The Real Python Podcast – Episode #182: Building a Python JSON Parser & Discussing Ideas for PEPs

2023-12-01T12:00:00+00:00

Have you thought of a way to improve the Python language? How do you share your idea with core developers and start a discussion in the Python community? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Advent of Code: Solving Puzzles With Python

2023-11-28T14:00:00+00:00

Advent of Code is an online Advent calendar where you’ll find new programming puzzles offered each day from December 1 to 25. While you can solve the puzzles at any time, the excitement when new puzzles unlock is really something special. You can participate in Advent of Code in any programming language—including Python!

With the help of this Code Conversation, you’ll be ready to start solving puzzles and earning your first gold stars.

In this video course, you’ll learn:

What an online Advent calendar is
How solving puzzles can advance your programming skills
How you can participate in Advent of Code

Advent of Code puzzles are designed to be approachable by anyone with an interest in problem-solving. You don’t need a heavy computer science background to participate. Instead, Advent of Code is a great arena for learning new skills and testing out new features of Python.

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Python Basics Exercises: Modules and Packages

2023-11-21T14:00:00+00:00

In Python Basics: Modules and Packages, you learned how to build an application by putting related code into separate files called modules. You also used the import statement to use modules in another file.

In this video course, you’ll practice:

Creating your own modules
Using modules in another file through the import statement
Organizing several modules into a package with __init__.py

Along the way, you’ll also get some insight into how to tackle coding challenges in general, which can be a great way to level up as a developer.

This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out the other Python Basics courses.

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The Real Python Podcast – Episode #181: Computational Thinking & Learning Python During an AI Revolution

2023-11-17T12:00:00+00:00

Has the current growth of artificial intelligence (AI) systems made you wonder what the future holds for Python developers? What are the hidden benefits of learning to program in Python and practicing computational thinking? This week on the show, we speak with author Lawrence Gray about his upcoming book "Mastering Python: A Problem Solving Approach."

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Python Basics: Modules and Packages

2023-11-14T14:00:00+00:00

As you gain experience writing code, you’ll eventually work on projects that are so large that keeping all the code in a single file becomes cumbersome.

Instead of writing a single file, you can put related code into separate files called modules. You can put individual modules together like building blocks to create a larger application.

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

Create your own modules
Use modules in another file through the import statement
Organize several modules into a package

This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out the other Python Basics courses.

Note that you’ll be using IDLE to interact with Python throughout this course.

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The Real Python Podcast – Episode #180: Studying Python Software Architecture & Creating Lambda Expressions

2023-11-10T12:00:00+00:00

Have you moved through the fundamentals of Python, and are you now considering building a more extensive project or complete application? Where can you study the architecture of existing Python projects and learn best practices? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Python Basics Exercises: Numbers and Math

2023-11-07T14:00:00+00:00

In Python Basics: Numbers and Math, you learned the mathematical skills that you’ll need as a Python programmer. Now, you’ll take those skills to the next level by actively practicing and applying what you’ve learned.

In this hands-on course, you’ll have the opportunity to reinforce your understanding of numbers and math in Python programming through a series of exercises and coding challenges. By actively engaging with the material, you’ll gain the confidence you need to apply your learning in real-world scenarios.

In this course, you’ll practice:

Creating integers and floating-point numbers
Using arithmetic expressions, math functions, and number methods
Formatting and displaying numbers in strings
Taking numbers as user input

In working through these exercises, you’ll deepen your knowledge and solidify your understanding of the Python language. You’ll not only gain confidence in your programming abilities but also enhance your problem-solving skills.

This course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out other Python Basics courses.

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The Real Python Podcast – Episode #179: Improving Your Git Developer Experience in Python

2023-11-03T12:00:00+00:00

Are you getting by with a few fundamental commands for Git when building your Python projects? Would you like to improve your version control techniques and become more efficient with the Git command line? This week on the show, Adam Johnson is back to talk about his new book, "Boost Your Git DX."

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Using the bpython Enhanced REPL

2023-10-31T14:00:00+00:00

The standard Python interpreter lets you run scripts from files or interactively execute code on the fly in a so-called read-evaluate-print loop (REPL). While this is a powerful tool for exploring the language and discovering its libraries through instant feedback on your code inputs, the default REPL shipped with Python has several limitations. Luckily, alternatives like bpython offer a much more programmer-friendly and convenient experience.

You can use bpython to experiment with your code or quickly test an idea without switching contexts between different programs, just like in an integrated development environment (IDE). In addition, bpython may be a valuable teaching tool in either a virtual or physical classroom.

In this course, you’ll learn how to:

Install and use bpython as your alternative Python REPL
Boost your productivity thanks to bpython’s unique features
Tweak bpython’s configuration and its color theme
Use common keyboard shortcuts to code more quickly
Contribute to bpython’s open-source project on GitHub

Before starting this course, make sure you’re already familiar with Python basics and know how to start the standard Python REPL in the command line. In addition, you should be able to install packages with pip, ideally into a virtual environment.

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The Real Python Podcast – Episode #178: Guiding Scientific Python Library Development

2023-10-27T12:00:00+00:00

How do you prepare a scientific Python project for sharing with others? Could you use some best practices and guidance for packaging, documentation, and testing? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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