Real Python

The Real Python Podcast – Episode #104: Building a Hash Table in Python and Thoughtful REST API Design

2022-04-01T12:00:00+00:00

Do you understand how a hash table works? What if you could learn about building one while practicing test-driven development? What are best practices when designing a REST API? This week on the show, Christopher Trudeau is here, and he's brought another batch of PyCoder's Weekly articles and projects.

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Python GUI Programming With Tkinter

2022-03-30T14:00:00+00:00

Python has a lot of GUI frameworks, but Tkinter is the only framework that’s built into the Python standard library. Tkinter has several strengths. It’s cross-platform, so the same code works on Windows, macOS, and Linux. Visual elements are rendered using native operating system elements, so applications built with Tkinter look like they belong on the platform where they’re run.

Although Tkinter is considered the de facto Python GUI framework, it’s not without criticism. One notable criticism is that GUIs built with Tkinter look outdated. If you want a shiny, modern interface, then Tkinter may not be what you’re looking for.

However, Tkinter is lightweight and relatively painless to use compared to other frameworks. This makes it a compelling choice for building GUI applications in Python, especially for applications where a modern sheen is unnecessary, and the top priority is to quickly build something that’s functional and cross-platform.

In this tutorial, you’ll learn how to:

Get started with Tkinter with a Hello, World application
Work with widgets, such as buttons and text boxes
Control your application layout with geometry managers
Make your applications interactive by associating button clicks with Python functions

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

Note: This tutorial is adapted from the chapter “Graphical User Interfaces” of Python Basics: A Practical Introduction to Python 3.

The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell. In this tutorial, references to IDLE have been removed in favor of more general language.

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

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

Building Your First Python GUI Application With Tkinter

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

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

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

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

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

Python on macOS with Homebrew:

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

Ubuntu Linux 20.04:

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

$ sudo apt-get install python3-tk

This installs the Python GUI Tkinter module.

Other Linux Flavors:

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

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

>>>

>>> import tkinter as tk

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

>>>

>>> window = tk.Tk()

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

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

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

>>>

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

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

>>>

>>> greeting.pack()

The window now looks like this:

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

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Using Python's datetime Module

2022-03-29T14:00:00+00:00

Python has several different modules to deal with dates and times. This course concentrates on the primary one, datetime. Dates and times are messy things! Shifts due to daylight savings time and time zones complicate any computing with dates and times.

In this course, you’ll tackle that messiness and learn:

How to use the datetime module
What the zoneinfo module does and how to use it
How to calculate the difference between two datetime objects

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Pagination for a User-Friendly Django App

2022-03-28T14:00:00+00:00

You can improve the user experience of your Django web app significantly by spreading your content over multiple pages instead of serving all of it at once. This practice is called pagination. To implement pagination, you have to consider page count, items per page, and the order of pages.

But if you’re using Django for your web projects, you’re in luck! Django has the pagination functionality built in. With only a few configuration steps, you can provide paginated content to your users.

In this tutorial, you’ll learn how to:

Decide when to use Django’s paginator
Implement pagination in class-based views
Implement pagination in function-based views
Add pagination elements to templates
Browse pages directly with paginated URLs
Combine Django pagination with JavaScript calls

This tutorial is for intermediate Python programmers with basic Django experience. Ideally, you’ve completed some introductory tutorials or created your own smaller Django projects. To have the best experience with this tutorial, you should know what models, views, and templates are and how to create them. If you need a refresher, then check out the tutorial on how to build a portfolio app with Django.

Get Source Code: Click here to get the source code that you’ll use to implement Django pagination.

Pagination in the Wild

Before you try your hand at building your own pagination flows with Django, it’s worth looking around to spot pagination in action. Pagination is so common on bigger websites that you’ve most likely experienced it in one form or another when browsing the Internet.

What Pagination Is

Pagination describes the practice of distributing your website’s content across multiple consecutive pages instead of serving it on a single page. If you visit shopping sites, blogs, or archives, you’re likely to encounter paginated content.

On GitHub, you’ll find paginated content on Django’s pull requests page. When you reach the bottom of the page, you can navigate to other pages:

Imagine how crowded the bottom of the page would be if all the page numbers were displayed. What’s more, consider how long the page would take to load if all the issues were displayed at once instead of being spread over 615 pages.

You could even argue that having page numbers is unnecessary. How could anybody know which issue is on which page? For that reason, some sites ditch page numbers entirely and give you a condensed form of pagination.

The PyCoder’s Weekly Newsletter paginates its archive with Previous and Next buttons. This type of pagination lets you conveniently browse through all newsletter issues:

Underneath the Subscribe button, you see the pagination controls for navigating to the previous and next issues. Thanks to this pagination technique, you’re able hop from one newsletter issue to another instead of selecting issues from the archive one by one.

You can also see pagination in action when you’ve got more than one hundred objects in your Django admin interface. To access more content, you have to click another page number:

Instead of showing a list of all 3,776 items, the Django admin divides the content into 38 pages. Again, imagine how overwhelming the Django admin interface would be if all the content were presented in one giant table!

But pagination is not only used in the front-end design of websites. It’s also very common to paginate the content of API responses. The Random User API is one of many REST APIs that give you the option of paginating the response:

By adding a results=2 parameter, you tell the Random User API that you only want two results per response. With the page parameter, you can navigate to a specific page of these paginated responses.

Note: Do you have any interesting examples of websites or APIs that use pagination? Share them with the community in the comments below!

Once you know what pagination is, you’ll probably notice it often while surfing the Web. In thinking about implementing pagination in your projects, it’s worth taking a closer look at when to use pagination and when not to use it.

Read the full article at https://realpython.com/django-pagination/ »

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The Real Python Podcast – Episode #103: Becoming More Effective at Manipulating Data With Pandas

2022-03-25T12:00:00+00:00

Do you wonder if you're taking the right approach when shaping data in pandas? Is your Jupyter workflow getting out of hand? This week on the show, Matt Harrison talks about his new book, "Effective Pandas: Patterns for Data Manipulation."

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Image Processing With the Python Pillow Library

2022-03-23T14:00:00+00:00

When you look at an image, you see the objects and people in it. However, when you read an image programmatically with Python or any other language, the computer sees an array of numbers. In this tutorial, you’ll learn how to manipulate images and perform basic image processing using the Python Pillow library.

Pillow and its predecessor, PIL, are the original Python libraries for dealing with images. Even though there are other Python libraries for image processing, Pillow remains an important tool for understanding and dealing with images.

To manipulate and process images, Pillow provides tools that are similar to ones found in image processing software such as Photoshop. Some of the more modern Python image processing libraries are built on top of Pillow and often provide more advanced functionality.

In this tutorial, you’ll learn how to:

Read images with Pillow
Perform basic image manipulation operations
Use Pillow for image processing
Use NumPy with Pillow for further processing
Create animations using Pillow

This tutorial provides an overview of what you can achieve with the Python Pillow library through some of its most common methods. Once you gain confidence using these methods, then you can use Pillow’s documentation to explore the rest of the methods in the library. If you’ve never worked with images in Python before, this is a great opportunity to jump right in!

In this tutorial, you’ll use several images, which you can download from the tutorial’s image repository:

Get Images: Click here to get access to the images that you’ll manipulate and process with Pillow.

With those images in hand, you’re now ready to get started with Pillow.

Basic Image Operations With the Python Pillow Library

The Python Pillow library is a fork of an older library called PIL. PIL stands for Python Imaging Library, and it’s the original library that enabled Python to deal with images. PIL was discontinued in 2011 and only supports Python 2. To use its developers’ own description, Pillow is the friendly PIL fork that kept the library alive and includes support for Python 3.

There’s more than one module in Python to deal with images and perform image processing. If you want to deal with images directly by manipulating their pixels, then you can use NumPy and SciPy. Other popular libraries for image processing are OpenCV, scikit-image, and Mahotas. Some of these libraries are faster and more powerful than Pillow.

However, Pillow remains an important tool for dealing with images. It provides image processing features that are similar to ones found in image processing software such as Photoshop. Pillow is often the preferred option for high-level image processing tasks that don’t require more advanced image processing expertise. It’s also often used for exploratory work when dealing with images.

Pillow also has the advantage of being widely used by the Python community, and it doesn’t have the same steep learning curve as some of the other image processing libraries.

You’ll need to install the library before you can use it. You can install Pillow using pip within a virtual environment:

Now that you’ve installed the package, you’re ready to start familiarizing yourself with the Python Pillow library and perform basic manipulations of images.

The `Image` Module and `Image` Class in Pillow

The main class defined in Pillow is the Image class. When you read an image using Pillow, the image is stored in an object of type Image.

For the code in this section, you’ll need the image file named buildings.jpg (image credit), which you can find in the image repository for this tutorial:

Get Images: Click here to get access to the images that you’ll manipulate and process with Pillow.

You can place this image file in the project folder that you’re working in.

When exploring images with Pillow, it’s best to use an interactive REPL environment. You’ll start by opening the image that you just downloaded:

>>>

>>> from PIL import Image
>>> filename = "buildings.jpg"
>>> with Image.open(filename) as img:
...     img.load()
...

>>> type(img)
<class 'PIL.JpegImagePlugin.JpegImageFile'>

>>> isinstance(img, Image.Image)
True

Read the full article at https://realpython.com/image-processing-with-the-python-pillow-library/ »

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Python Basics: Code Your First Python Program

2022-03-22T14:00:00+00:00

In the previous Python Basics video course, you set up Python on your computer. With Python installed, you’re ready ready to start coding!

In this video course, you’ll:

Write your first Python program
Learn what happens when you run a program with an error
Learn how to declare a variable and inspect its value
Learn how to write comments

This course can be enjoyed alone or as an accompaniment to Python Basics: A Practical Introduction to Python 3.

Ready to begin your Python journey? Let’s go!

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Python Timer Functions: Three Ways to Monitor Your Code

2022-03-21T14:00:00+00:00

While many developers recognize Python as an effective programming language, pure Python programs may run more slowly than their counterparts in compiled languages like C, Rust, and Java. In this tutorial, you’ll learn how to use a Python timer to monitor how quickly your programs are running.

In this tutorial, you’ll learn how to use:

time.perf_counter() to measure time in Python
Classes to keep state
Context managers to work with a block of code
Decorators to customize a function

You’ll also gain background knowledge into how classes, context managers, and decorators work. As you explore examples of each concept, you’ll be inspired to use one or several of them in your code, for timing code execution, as well as in other applications. Each method has its advantages, and you’ll learn which to use depending on the situation. Plus, you’ll have a working Python timer that you can use to monitor your programs!

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

Python Timers

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

Python Timer Functions

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

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

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

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

Example: Download Tutorials

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

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

$ python -m pip install realpython-reader

Then, you can import the package as reader.

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

 1# latest_tutorial.py
 2
 3from reader import feed
 4
 5def main():
 6    """Download and print the latest tutorial from Real Python"""
 7    tutorial = feed.get_article(0)
 8    print(tutorial)
 9
10if __name__ == "__main__":
11    main()

realpython-reader handles most of the hard work:

Line 3 imports feed from realpython-reader. This module contains functionality for downloading tutorials from the Real Python feed.
Line 7 downloads the latest tutorial from Real Python. The number 0 is an offset, where 0 means the most recent tutorial, 1 is the previous tutorial, and so on.
Line 8 prints the tutorial to the console.
Line 11 calls main() when you run the script.

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

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

While many developers recognize Python as an effective programming language,
pure Python programs may run more slowly than their counterparts in compiled
languages like C, Rust, and Java. In this tutorial, you'll learn how to use
a Python timer to monitor how quickly your programs are running.

[ ... ]

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

* * *

The code may take a little while to run depending on your network, so you might want to use a Python timer to monitor the performance of the script.

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

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The Real Python Podcast – Episode #102: Making Your Notebook Interactive and Using Python's Assert

2022-03-18T12:00:00+00:00

Would you like to build visualizations that allow your audience to play with data? How do you effectively use Python's assert statement during development? This week on the show, Christopher Trudeau is here, and he's brought another batch of PyCoder's Weekly articles and projects.

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Build a Hash Table in Python With TDD

2022-03-16T14:00:00+00:00

Invented over half a century ago, the hash table is a classic data structure that has been fundamental to programming. To this day, it helps solve many real-life problems, such as indexing database tables, caching computed values, or implementing sets. It often comes up in job interviews, and Python uses hash tables all over the place to make name lookups almost instantaneous.

Even though Python comes with its own hash table called dict, it can be helpful to understand how hash tables work behind the curtain. A coding assessment may even task you with building one. This tutorial will walk you through the steps of implementing a hash table from scratch as if there were none in Python. Along the way, you’ll face a few challenges that’ll introduce important concepts and give you an idea of why hash tables are so fast.

In addition to this, you’ll get a hands-on crash course in test-driven development (TDD) and will actively practice it while building your hash table in a step-by-step fashion. You’re not required to have any prior experience with TDD, but at the same time, you won’t get bored even if you do!

In this tutorial, you’ll learn:

How a hash table differs from a dictionary
How you can implement a hash table from scratch in Python
How you can deal with hash collisions and other challenges
What the desired properties of a hash function are
How Python’s hash() works behind the scenes

It’ll help if you’re already familiar with Python dictionaries and have basic knowledge of object-oriented programming principles. To get the complete source code and the intermediate steps of the hash table implemented in this tutorial, follow the link below:

Source Code: Click here to download the source code that you’ll use to build a hash table in Python.

Get to Know the Hash Table Data Structure

Before diving deeper, you should familiarize yourself with the terminology because it can get slightly confusing. Colloquially, the term hash table or hash map is often used interchangeably with the word dictionary. However, there’s a subtle difference between the two concepts as the former is more specific than the latter.

Hash Table vs Dictionary

In computer science, a dictionary is an abstract data type made up of keys and values arranged in pairs. Moreover, it defines the following operations for those elements:

Add a key-value pair
Delete a key-value pair
Update a key-value pair
Find a value associated with the given key

In a sense, this abstract data type resembles a bilingual dictionary, where the keys are foreign words, and the values are their definitions or translations to other languages. But there doesn’t always have to be a sense of equivalence between keys and values. A phone book is another example of a dictionary, which combines names with the corresponding phone numbers.

Note: Anytime you map one thing to another or associate a value with a key, you’re essentially using a kind of a dictionary. That’s why dictionaries are also known as maps or associative arrays.

Dictionaries have a few interesting properties. One of them is that you can think of a dictionary as a mathematical function that projects one or more arguments to exactly one value. The direct consequences of that fact are the following:

Only Key-Value Pairs: You can’t have a key without the value or the other way around in a dictionary. They always go together.
Arbitrary Keys and Values: Keys and values can belong to two disjoint sets of the same or separate types. Both keys and values may be almost anything, such as numbers, words, or even pictures.
Unordered Key-Value Pairs: Because of the last point, dictionaries don’t generally define any order for their key-value pairs. However, that might be implementation-specific.
Unique Keys: A dictionary can’t contain duplicate keys, because that would violate the definition of a function.
Non-Unique Values: The same value can be associated with many keys, but it doesn’t have to.

There are related concepts that extend the idea of a dictionary. For example, a multimap lets you have more than one value per key, while a bidirectional map not only maps keys to values but also provides mapping in the opposite direction. However, in this tutorial, you’re only going to consider the regular dictionary, which maps exactly one value to each key.

Here’s a graphical depiction of a hypothetical dictionary, which maps some abstract concepts to their corresponding English words:

It’s a one-way map of keys to values, which are two completely different sets of elements. Right away, you can see fewer values than keys because the word bow happens to be a homonym with multiple meanings. Conceptually, this dictionary still contains four pairs, though. Depending on how you decided to implement it, you could reuse repeated values to conserve memory or duplicate them for simplicity.

Now, how do you code such a dictionary in a programming language? The right answer is you don’t, because most modern languages come with dictionaries as either primitive data types or classes in their standard libraries. Python ships with a built-in dict type, which already wraps a highly optimized data structure written in C so that you don’t have to write a dictionary yourself.

Python’s dict lets you perform all the dictionary operations listed at the beginning of this section:

>>>

>>> glossary = {"BDFL": "Benevolent Dictator For Life"}
>>> glossary["GIL"] = "Global Interpreter Lock"  # Add
>>> glossary["BDFL"] = "Guido van Rossum"  # Update
>>> del glossary["GIL"]  # Delete
>>> glossary["BDFL"]  # Search
'Guido van Rossum'
>>> glossary
{'BDFL': 'Guido van Rossum'}

With the square bracket syntax ([ ]), you can add a new key-value pair to a dictionary. You can also update the value of or delete an existing pair identified by a key. Finally, you can look up the value associated with the given key.

That said, you may ask a different question. How does the built-in dictionary actually work? How does it map keys of arbitrary data types, and how does it do it so quickly?

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

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Sorting Data in Python With Pandas

2022-03-15T14:00:00+00:00

Learning pandas sort methods is a great way to start with or practice doing basic data analysis using Python. Most commonly, data analysis is done with spreadsheets, SQL, or pandas. One of the great things about using pandas is that it can handle a large amount of data and offers highly performant data manipulation capabilities.

In this video course, you’ll learn how to use .sort_values() and .sort_index(), which will enable you to sort data efficiently in a DataFrame.

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

Sort a pandas DataFrame by the values of one or more columns
Use the ascending parameter to change the sort order
Sort a DataFrame by its index using .sort_index()
Organize missing data while sorting values
Sort a DataFrame in-place using inplace set to True

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Python Class Constructors: Control Your Object Instantiation

2022-03-14T14:00:00+00:00

Class constructors are a fundamental part of object-oriented programming in Python. They allow you to create and properly initialize objects of a given class, making those objects ready to use. Class constructors internally trigger Python’s instantiation process, which runs through two main steps: instance creation and instance initialization.

If you want to dive deeper into how Python internally constructs objects and learn how to customize the process, then this tutorial is for you.

In this tutorial, you’ll:

Understand Python’s internal instantiation process
Customize object initialization using .__init__()
Fine-tune object creation by overriding .__new__()

With this knowledge, you’ll be able to tweak the creation and initialization of objects in your custom Python classes, which will give you control over the instantiation process at a more advanced level.

To better understand the examples and concepts in this tutorial, you should be familiar with object-oriented programming and special methods in Python.

Free Bonus: Click here to get access to a free Python OOP Cheat Sheet that points you to the best tutorials, videos, and books to learn more about Object-Oriented Programming with Python.

Python’s Class Constructors and the Instantiation Process

Like many other programming languages, Python supports object-oriented programming. At the heart of Python’s object-oriented capabilities, you’ll find the class keyword, which allows you to define custom classes that can have attributes for storing data and methods for providing behaviors.

Once you have a class to work with, then you can start creating new instances or objects of that class, which is an efficient way to reuse functionality in your code.

Creating and initializing objects of a given class is a fundamental step in object-oriented programming. This step is often referred to as object construction or instantiation. The tool responsible for running this instantiation process is commonly known as a class constructor.

Getting to Know Python’s Class Constructors

In Python, to construct an object of a given class, you just need to call the class with appropriate arguments, as you would call any function:

>>>

>>> class SomeClass:
...     pass
...

>>> # Call the class to construct an object
>>> SomeClass()
<__main__.SomeClass object at 0x7fecf442a140>

In this example, you define SomeClass using the class keyword. This class is currently empty because it doesn’t have attributes or methods. Instead, the class’s body only contains a pass statement as a placeholder statement that does nothing.

Then you create a new instance of SomeClass by calling the class with a pair of parentheses. In this example, you don’t need to pass any argument in the call because your class doesn’t take arguments yet.

In Python, when you call a class as you did in the above example, you’re calling the class constructor, which creates, initializes, and returns a new object by triggering Python’s internal instantiation process.

A final point to note is that calling a class isn’t the same as calling an instance of a class. These are two different and unrelated topics. To make a class’s instance callable, you need to implement a .__call__() special method, which has nothing to do with Python’s instantiation process.

Understanding Python’s Instantiation Process

You trigger Python’s instantiation process whenever you call a Python class to create a new instance. This process runs through two separate steps, which you can describe as follows:

Create a new instance of the target class
Initialize the new instance with an appropriate initial state

To run the first step, Python classes have a special method called .__new__(), which is responsible for creating and returning a new empty object. Then another special method, .__init__(), takes the resulting object, along with the class constructor’s arguments.

The .__init__() method takes the new object as its first argument, self. Then it sets any required instance attribute to a valid state using the arguments that the class constructor passed to it.

In short, Python’s instantiation process starts with a call to the class constructor, which triggers the instance creator, .__new__(), to create a new empty object. The process continues with the instance initializer, .__init__(), which takes the constructor’s arguments to initialize the newly created object.

To explore how Python’s instantiation process works internally, consider the following example of a Point class that implements a custom version of both methods, .__new__() and .__init__(), for demonstration purposes:

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

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The Real Python Podcast – Episode #101: Tools for Setting Up Python on a New Machine

2022-03-11T12:00:00+00:00

There are many ways to get Python installed on your computer. If you were going to start fresh, what tools would you use? What if you need to manage multiple versions of Python and virtual environments? What about all the additional tools that make your coding workflow complete? This week on the show, Calvin Hendryx-Parker is here to talk about bootstrapping your Python environment.

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Python 3.11 Preview: Even Better Error Messages

2022-03-09T14:00:00+00:00

Python 3.11 will be released in October 2022. Even though October is still months away, you can already preview some of the upcoming features, including how Python 3.11 will offer more readable and actionable error messages.

In this tutorial, you’ll:

Install Python 3.11 Alpha on your computer, next to your current Python installations
Interpret the improved error messages in Python 3.11 and learn to more efficiently debug your code
Connect these improvements to the PEG parser and the better error messages in Python 3.10
Explore third-party packages offering enhanced error messages
Test smaller improvements in Python 3.11, including new math functions and more readable fractions

There are many other improvements and features coming in Python 3.11. Keep track of what’s new in the changelog for an up-to-date list.

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

Python 3.11 Alpha

A new version of Python is released in October each year. The code is developed and tested over a seventeen-month period before the release date. New features are implemented during the alpha phase, which lasts until May, about five months before the final release.

About once a month during the alpha phase, Python’s core developers release a new alpha version to show off the new features, test them, and get early feedback. Currently, the latest version of Python 3.11 is 3.11.0 alpha 5, released on February 3, 2022.

Note: This tutorial uses the fifth alpha version of Python 3.11. You might experience small differences if you use a later version. However, you can expect most of what you learn here to stay the same through the alpha and beta phases and in the final release of Python 3.11.

The first beta release of Python 3.11 is planned for May 6, 2022. Typically, no new features are added during the beta phase. Instead, the time between the feature freeze and the release date is used to test and solidify the code.

Cool New Features

Some of the currently announced highlights of Python 3.11 include:

Enhanced error messages, which will help you more effectively debug your code
Exception groups, which will allow programs to raise and handle multiple exceptions at the same time
Optimizations, promising to make Python 3.11 significantly faster than previous versions
Static typing improvements, which will let you annotate your code more precisely

There’s a lot to look forward to in the upcoming Python 3.11 release! In this tutorial, you’ll focus on how the enhanced error reporting can improve your developer experience by letting you debug your code more efficiently. You’ll also get a peek at some of the other, smaller features that’ll be shipping with Python 3.11.

Installation

To play with the code examples in this tutorial, you’ll need to install a version of Python 3.11 onto your system. In this section, you’ll learn about a few different ways to do this: using Docker, using pyenv, or installing from source. Pick the one that works best for you and your system.

Note: Alpha versions are previews of upcoming features. While most features will work well, you shouldn’t depend on any Python 3.11 alpha version in production or anywhere else where bugs will have serious consequences.

If you have access to Docker on your system, then you can download the latest version of Python 3.11 by pulling and running the python:3.11-rc-slim Docker image:

$ docker pull python:3.11-rc-slim
Unable to find image 'python:3.11-rc-slim' locally
latest: Pulling from library/python
[...]

$ docker run -it --rm python:3.11-rc-slim

This drops you into a Python 3.11 REPL. Check out Run Python Versions in Docker for more information about how you can work with Python through Docker, including how you can run scripts.

The pyenv tool is great for managing different versions of Python on your system, and you can use it to install Python 3.11 Alpha if you like. It comes with two different versions, one for Windows and one for Linux and macOS:

On Windows, you can use pyenv-win. First update your pyenv installation:

C:\> pyenv update
:: [Info] ::  Mirror: https://www.python.org/ftp/python
[...]

Doing an update ensures that you can install the latest version of Python. You could also update pyenv manually.

On Linux and macOS, you can use pyenv. First update your pyenv installation, using the pyenv-update plugin:

$ pyenv update
Updating /home/realpython/.pyenv...
[...]

Doing an update ensures that you can install the latest version of Python. If you don’t want to use the update plugin, you can update pyenv manually.

Use pyenv install --list to check which versions of Python 3.11 are available. Then, install the latest one:

$ pyenv install 3.11.0a5
Downloading Python-3.11.0a5.tar.xz...
[...]

Read the full article at https://realpython.com/python311-error-messages/ »

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Counting With Python's Counter

2022-03-08T14:00:00+00:00

Counting several repeated objects at once is a common problem in programming. Python offers a bunch of tools and techniques you can use to approach this problem. However, Python’s Counter from collections provides a clean, efficient, and Pythonic solution.

This dictionary subclass provides efficient counting capabilities out of the box. Understanding Counter and how to use it efficiently is a convenient skill to have as a Python developer.

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

Count several repeated objects at once
Create counters with Python’s Counter
Retrieve the most common objects in a counter
Update object counts
Use Counter to facilitate further computations
Implement Counter instances as multisets

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Python News: What's New From February 2022?

2022-03-07T14:00:00+00:00

In February 2022, another preview release of Python 3.11 became available for early adopters to test and share their feedback. Shortly afterward, the Python steering council announced that Python 3.11 would also include a TOML parser in the standard library. In addition, GitHub Issues will soon become the official bug tracking system for Python.

In other news, PyCon US 2022 shared its conference schedule. The Python Software Foundation (PSF) wants to hire two contract developers to improve the Python Package Index (PyPI). Apple is finally removing Python 2.7 from macOS.

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

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Python 3.11 Alpha 5 Released

While the final release of Python 3.11 is planned for October 2022, which is still months ahead, you can already check out what’s coming. According to the development and release schedule described in PEP 664, Python 3.11 is now in the alpha phase of its release cycle, which is meant to collect early feedback from users like you. At the beginning of February, 3.11.0a5, the fifth of seven planned alpha releases, became available for testing.

To play around with an alpha release of Python, you can grab the corresponding Docker image from Docker Hub, install an alternative Python interpreter with pyenv, or build the CPython source code using a compiler tool. The source code method lets you clone CPython’s repository from GitHub and check out a bleeding-edge snapshot without waiting for an alpha release.

Note: Features under development might be unstable and buggy, and they can be modified or removed from the final release without notice. As a result of that, you should never use preview releases in a production environment!

If you’d like to explore some of the most exciting features coming to Python 3.11, then make sure you can run the alpha release of the interpreter. You’ll find the commands to download and run Python 3.11.0a5 below:

$ docker pull python:3.11.0a5-slim
$ docker run -it --rm python:3.11.0a5-slim
Python 3.11.0a5 (main, Feb 25 2022, 20:02:52) [GCC 10.2.1 20210110] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

$ pyenv update
$ pyenv install 3.11.0a5
$ pyenv local 3.11.0a5
$ python
Python 3.11.0a5 (main, Mar  1 2022, 10:05:02) [GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

$ git clone git@github.com:python/cpython.git
$ cd cpython/
$ git checkout v3.11.0a5
$ ./configure
$ make
$ ./python
Python 3.11.0a5 (tags/v3.11.0a5:c4e4b91557, Mar  1 2022, 17:48:56) [GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

So what’s all the hype about this alpha release? There are several big and small improvements, but for now, let’s focus on some of the Python 3.11 highlights!

PEP 657: Error Locations in Tracebacks

Python 3.10 has already greatly improved its error messages. By pinpointing the root cause, providing context, and even suggesting fixes, error messages have become more human-friendly and helpful for Python beginners. Python 3.11 takes error messaging one step further to improve the debugging experience and expose an API for code analysis tools.

Sometimes, a single line of code can contain multiple instructions or a complex expression, which would be hard to debug in older Python versions:

$ cat test.py
x, y, z = 1, 2, 0
w = x / y / z

$ python3.10 test.py
Traceback (most recent call last):
  File "/home/realpython/test.py", line 2, in <module>
    w = x / y / z
ZeroDivisionError: float division by zero

$ python3.11a5 test.py
  File "/home/realpython/test.py", line 2, in <module>
    w = x / y / z
        ~~~~~~^~~
ZeroDivisionError: float division by zero

Here, one of the variables causes a zero division error. Python 3.10 tells you about the problem, but it doesn’t indicate the culprit. In Python 3.11, the traceback will include visual feedback about the exact location on a line that raised an exception. You’ll also have a programmatic way of getting that same information for tools.

Note that some of these enhanced tracebacks won’t work for code evaluated on the fly in the Python REPL, because the tracebacks require pre-compiled bytecode to keep track of the source lines:

>>>

Python 3.11.0a5 (main, Mar  1 2022, 10:05:02) [GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> x, y, z = 1, 2, 0
>>> w = x / y / z
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ZeroDivisionError: float division by zero

For more information on error locations in tracebacks, see PEP 657.

PEP 654: Exception Groups and `except*`

Read the full article at https://realpython.com/python-news-february-2022/ »

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The Real Python Podcast – Episode #100: Defining Optional Arguments and Moving Beyond "Beginner" Python

2022-03-04T12:00:00+00:00

How do you define Python functions that accept optional arguments or default values? Are you wondering how to go beyond being a beginner with Python? This week on the show, Christopher Trudeau is here, and he's brought another batch of PyCoder's Weekly articles and projects.

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Manage Your To-Do Lists Using Python and Django

2022-03-02T14:00:00+00:00

Have you ever struggled to keep track of the things that you need to do? Perhaps you’re in the habit of using a handwritten to-do list to remind you of what needs doing, and by when. But handwritten notes have a way of getting lost or forgotten. Because you’re a Python coder, it makes sense to build a Django to-do list manager!

In this step-by-step tutorial, you’re going to create a web app using Django. You’ll learn how Django can integrate with a database that stores all your to-do items in lists that you can define. Each item will have a title, a description, and a deadline. With this app, you can manage your own deadlines and help your entire team stay on track!

In this tutorial, you’ll learn how to:

Create a web app using Django
Build a data model with one-to-many relationships
Use the Django admin interface to explore your data model and add test data
Design templates for displaying your lists
Leverage class-based views to handle the standard database operations
Control the Django URL dispatcher by creating URL configurations

Along the way, you’ll learn how Django’s class-based views leverage the power of object-oriented programming. That’ll save you a ton of development effort!

Get Source Code: Click here to get the source code you’ll use to build your to-do list app.

Demo

In this tutorial, you’ll build a Django to-do list manager. Your main page will display all of your to-do lists. By clicking the Add a new list button, you’ll display a page where you can name and create a new list:

You’ll be able to add to-do items to your list by clicking Add a new item. There, you can give your item a title, and you can add more details in the Description box. You can even set a due date.

Project Overview

To build this app, you’ll start by creating a virtual environment and setting up a Django project. Next, you’ll design a data model that represents the relationships between to-do items and lists. You’ll use Django’s built-in object-relational mapping tool to automatically generate the database and tables that’ll support this model.

As you develop your Django to-do list app, you’ll use Django’s handy runserver command whenever you need to verify that things are working as expected. This can help even before your web pages are ready, thanks to Django’s ready-made admin interface.

Next, you’ll develop your own web pages to display your app. In Django, these take the form of templates. Templates are skeleton HTML pages that can be populated with real application data.

Templates aren’t meant to provide much logic, such as deciding which template to display and what data to send it. To perform that logic, you’ll need views. Django’s views are the natural home for the application’s logic.

You’ll code views and templates for list creation and updates, as well as for the items that those lists will contain. You’ll learn how to use Django’s URL dispatcher to connect your pages and pass them the data that they need. Next, you’ll add more views and templates that enable your users to delete lists and items.

Finally, you’ll test your new user interface by adding, editing, and deleting to-do lists and to-do items.

By completing this project, you’ll learn how to build this app and also understand how the various components fit together. Then, you’ll be ready to undertake your next Django project on your own.

Prerequisites

To complete this tutorial, you should be comfortable with the following skills:

Running Python scripts from the command line
Coding in Python IDLE, or your favorite code editor
Using Python modules and packages
Understanding object-oriented programming in Python, including object inheritance

If you don’t have all of the prerequisite knowledge before starting this tutorial, that’s okay. In fact, you might learn more by going ahead and getting started! You can always stop and review the resources linked here if you get stuck.

You don’t need to have used Django before, because you’ll get step-by-step instructions for installing and using it below. However, if you’re interested in a more detailed introduction to this powerful web framework, there’s a whole range of Django tutorials that you can consult.

Step 1: Set Up Your Virtual Environment and Django

In this step, you’re going to perform a few standard housekeeping tasks that you should only need to do once per Django project. Specifically, you’ll make and activate a virtual environment, install Django, and test that Django is installed correctly.

Read the full article at https://realpython.com/django-todo-lists/ »

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Exploring the Fibonacci Sequence With Python

2022-03-01T14:00:00+00:00

The Fibonacci sequence is a pretty famous sequence of integer numbers. The sequence comes up naturally in many problems and has a nice recursive definition. Learning how to generate it is an essential step in the pragmatic programmer’s journey toward mastering recursion. In this video course, you’ll focus on learning what the Fibonacci sequence is and how to generate it using Python.

In this course, you’ll learn how to:

Generate the Fibonacci sequence using a recursive algorithm
Optimize the recursive Fibonacci algorithm using memoization
Generate the Fibonacci sequence using an iterative algorithm

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Python's urllib.request for HTTP Requests

2022-02-28T14:00:00+00:00

If you need to make HTTP requests with Python, then you may find yourself directed to the brilliant requests library. Though it’s a great library, you may have noticed that it’s not a built-in part of Python. If you prefer, for whatever reason, to limit your dependencies and stick to standard-library Python, then you can reach for urllib.request!

In this tutorial, you’ll:

Learn how to make basic HTTP requests with urllib.request
Dive into the nuts and bolts of an HTTP message and how urllib.request represents it
Understand how to deal with character encodings of HTTP messages
Explore some common errors when using urllib.request and learn how to resolve them
Dip your toes into the world of authenticated requests with urllib.request
Understand why both urllib and the requests library exist and when to use one or the other

If you’ve heard of HTTP requests, including GET and POST, then you’re probably ready for this tutorial. Also, you should’ve already used Python to read and write to files, ideally with a context manager, at least once.

Ultimately, you’ll find that making a request doesn’t have to be a frustrating experience, although it does tend to have that reputation. Many of the issues that you tend to run into are due to the inherent complexity of this marvelous thing called the Internet. The good news is that the urllib.request module can help to demystify much of this complexity.

Learn More: Click here to join 290,000+ Python developers on the Real Python Newsletter and get new Python tutorials and news that will make you a more effective Pythonista.

Basic HTTP GET Requests With `urllib.request`

Before diving into the deep end of what an HTTP request is and how it works, you’re going to get your feet wet by making a basic GET request to a sample URL. You’ll also make a GET request to a mock REST API for some JSON data. In case you’re wondering about POST Requests, you’ll be covering them later in the tutorial, once you have some more knowledge of urllib.request.

Beware: Depending on your exact setup, you may find that some of these examples don’t work. If so, skip ahead to the section on common urllib.request errors for troubleshooting.

If you’re running into a problem that’s not covered there, be sure to comment below with a precise and reproducible example.

To get started, you’ll make a request to www.example.com, and the server will return an HTTP message. Ensure that you’re using Python 3 or above, and then use the urlopen() function from urllib.request:

>>>

>>> from urllib.request import urlopen
>>> with urlopen("https://www.example.com") as response:
...     body = response.read()
...
>>> body[:15]
b'<!doctype html>'

In this example, you import urlopen() from urllib.request. Using the context manager with, you make a request and receive a response with urlopen(). Then you read the body of the response and close the response object. With that, you display the first fifteen positions of the body, noting that it looks like an HTML document.

There you are! You’ve successfully made a request, and you received a response. By inspecting the content, you can tell that it’s likely an HTML document. Note that the printed output of the body is preceded by b. This indicates a bytes literal, which you may need to decode. Later in the tutorial, you’ll learn how to turn bytes into a string, write them to a file, or parse them into a dictionary.

The process is only slightly different if you want to make calls to REST APIs to get JSON data. In the following example, you’ll make a request to {JSON}Placeholder for some fake to-do data:

>>>

>>> from urllib.request import urlopen
>>> import json
>>> url = "https://jsonplaceholder.typicode.com/todos/1"
>>> with urlopen(url) as response:
...     body = response.read()
...
>>> todo_item = json.loads(body)
>>> todo_item
{'userId': 1, 'id': 1, 'title': 'delectus aut autem', 'completed': False}

In this example, you’re doing pretty much the same as in the previous example. But in this one, you import urllib.request and json, using the json.loads() function with body to decode and parse the returned JSON bytes into a Python dictionary. Voila!

If you’re lucky enough to be using error-free endpoints, such as the ones in these examples, then maybe the above is all that you need from urllib.request. Then again, you may find that it’s not enough.

Now, before doing some urllib.request troubleshooting, you’ll first gain an understanding of the underlying structure of HTTP messages and learn how urllib.request handles them. This understanding will provide a solid foundation for troubleshooting many different kinds of issues.

The Nuts and Bolts of HTTP Messages

To understand some of the issues that you may encounter when using urllib.request, you’ll need to examine how a response is represented by urllib.request. To do that, you’ll benefit from a high-level overview of what an HTTP message is, which is what you’ll get in this section.

Before the high-level overview, a quick note on reference sources. If you want to get into the technical weeds, the Internet Engineering Task Force (IETF) has an extensive set of Request for Comments (RFC) documents. These documents end up becoming the actual specifications for things like HTTP messages. RFC 7230, part 1: Message Syntax and Routing, for example, is all about the HTTP message.

If you’re looking for some reference material that’s a bit easier to digest than RFCs, then the Mozilla Developer Network (MDN) has a great range of reference articles. For example, their article on HTTP messages, while still technical, is a lot more digestible.

Now that you know about these essential sources of reference information, in the next section you’ll get a beginner-friendly overview of HTTP messages.

Understanding What an HTTP Message Is

In a nutshell, an HTTP message can be understood as text, transmitted as a stream of bytes, structured to follow the guidelines specified by RFC 7230. A decoded HTTP message can be as simple as two lines:

Read the full article at https://realpython.com/urllib-request/ »

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The Real Python Podcast – Episode #99: OAuth 2 and Authentication Choices for Your Python Project

2022-02-25T12:00:00+00:00

Have you thought about what authentication system you want to use for your Python project? Should you use an existing Python library or a third-party service? This week on the show, Dan Moore is here to talk about authentication systems and OAuth 2.

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Starting With Linear Regression in Python

2022-02-22T14:00:00+00:00

We’re living in the era of large amounts of data, powerful computers, and artificial intelligence. This is just the beginning. Data science and machine learning are driving image recognition, autonomous vehicle development, decisions in the financial and energy sectors, advances in medicine, the rise of social networks, and more. Linear regression is an important part of this.

Linear regression is one of the fundamental statistical and machine learning techniques. Whether you want to do statistics, machine learning, or scientific computing, there’s a good chance that you’ll need it. It’s advisable to learn it first and then proceed toward more complex methods.

In this video course, you’ll learn:

What linear regression is
What linear regression is used for
How linear regression works
How to implement linear regression in Python, step by step

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The Real Python Podcast – Episode #98: Drawing Fractals With Python and Working With a Weather API

2022-02-18T12:00:00+00:00

Have you been wanting to explore fractals and complex numbers in Python? Would you like to practice working with APIs in Python through a new project? This week on the show, Christopher Trudeau is here, and he's taking on the task of curating new issues of PyCoder's Weekly going forward. He'll be joining me as a cohost every other week and bringing a fresh batch of PyCoder's Weekly articles and projects.

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Python any(): Powered Up Boolean Function

2022-02-15T14:00:00+00:00

As a Python programmer, you’ll frequently deal with Booleans and conditional statements—sometimes very complex ones. In those situations, you may need to rely on tools that can simplify logic and consolidate information. Fortunately, any() in Python is such a tool. It looks through the elements in an iterable and returns a single value indicating whether any element is true in a Boolean context, or truthy.

In this course, you’ll learn how to:

Use any() and not any()
Elimate long or chains
Use any() with list comprehensions
Evalute values that aren’t explicitly True or False
Distinguish between any() and or
Use short-circuiting

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The Real Python Podcast – Episode #97: Improving Your Django and Python Developer Experience

2022-02-11T12:00:00+00:00

How often have you thought about your Developer Experience (DX)? How do you improve your workflow, find documentation, and simplify code formatting? This week on the show, Adam Johnson is here to talk about his new book, "Boost Your Django DX."

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Defining Python Functions With Optional Arguments

2022-02-08T14:00:00+00:00

Defining your own functions is an essential skill for writing clean and effective code. In this tutorial, you’ll explore the techniques you have available for defining Python functions that take optional arguments. When you master Python optional arguments, you’ll be able to define functions that are more powerful and more flexible.

In this course, you’ll learn how to:

Distinguish between parameters and arguments
Define functions with optional arguments and default parameter values
Define functions using args and kwargs
Deal with error messages about optional arguments

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The Real Python Podcast – Episode #96: Manipulating and Analyzing Audio in Python

2022-02-04T12:00:00+00:00

Would you like to experiment with analyzing or manipulating audio with Python? This week on the show, we have Braden Riggs from DolbyIO to discuss extracting audio features and Python libraries for reshaping audio. Braden shares techniques from his recent talk at PyData Global, "Unlocking More From Your Audio Data!"

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Python's len() Function

2022-02-01T14:00:00+00:00

In many situations, you’ll need to find the number of items stored in a data structure. Python’s built-in function len() is the tool that will help you with this task.

There are some cases in which the use of len() is straightforward. However, there are other times when you’ll need to understand how this function works in more detail and how to apply it to different data types.

In this course, you’ll learn how to:

Find the length of built-in data types using len()
Use len() with third-party data types
Provide support for len() with user-defined classes

[ 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 #95: What Is a JIT and How Can Pyjion Speed Up Your Python?

2022-01-28T12:00:00+00:00

How can you can speed up Python? Have you thought of using a JIT (Just-In-Time Compiler)? This week on the show, we have Real Python author and previous guest Anthony Shaw to talk about his project Pyjion, a drop-in JIT compiler for CPython 3.10.

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Looping With Python enumerate()

2022-01-25T14:00:00+00:00

In Python, a for loop is usually written as a loop over an iterable object. This means that you don’t need a counting variable to access items in the iterable. Sometimes, though, you do want to have a variable that changes on each loop iteration. Rather than creating and incrementing a variable yourself, you can use Python’s enumerate() to get a counter and the value from the iterable at the same time!

In this course, you’ll see how to:

Use enumerate() to get a counter in a loop
Apply enumerate() to display item counts
Implement your own equivalent function to enumerate()
Unpack values returned by enumerate()

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

Real Python

The Real Python Podcast – Episode #104: Building a Hash Table in Python and Thoughtful REST API Design

Python GUI Programming With Tkinter

Building Your First Python GUI Application With Tkinter

Adding a Widget

Using Python's datetime Module

Pagination for a User-Friendly Django App

Pagination in the Wild

What Pagination Is

The Real Python Podcast – Episode #103: Becoming More Effective at Manipulating Data With Pandas

Image Processing With the Python Pillow Library

Basic Image Operations With the Python Pillow Library

The Image Module and Image Class in Pillow

Python Basics: Code Your First Python Program

Python Timer Functions: Three Ways to Monitor Your Code

Python Timers

Python Timer Functions

Example: Download Tutorials

The Real Python Podcast – Episode #102: Making Your Notebook Interactive and Using Python's Assert

Build a Hash Table in Python With TDD

Get to Know the Hash Table Data Structure

Hash Table vs Dictionary

Sorting Data in Python With Pandas

Python Class Constructors: Control Your Object Instantiation

Python’s Class Constructors and the Instantiation Process

Getting to Know Python’s Class Constructors

Understanding Python’s Instantiation Process

The Real Python Podcast – Episode #101: Tools for Setting Up Python on a New Machine

Python 3.11 Preview: Even Better Error Messages

Python 3.11 Alpha

Cool New Features

Installation

Counting With Python's Counter

Python News: What's New From February 2022?

Python 3.11 Alpha 5 Released

PEP 657: Error Locations in Tracebacks

PEP 654: Exception Groups and except*

The Real Python Podcast – Episode #100: Defining Optional Arguments and Moving Beyond "Beginner" Python

Manage Your To-Do Lists Using Python and Django

Demo

Project Overview

Prerequisites

Step 1: Set Up Your Virtual Environment and Django

Exploring the Fibonacci Sequence With Python

Python's urllib.request for HTTP Requests

Basic HTTP GET Requests With urllib.request

The Nuts and Bolts of HTTP Messages

Understanding What an HTTP Message Is

The Real Python Podcast – Episode #99: OAuth 2 and Authentication Choices for Your Python Project

Starting With Linear Regression in Python

The Real Python Podcast – Episode #98: Drawing Fractals With Python and Working With a Weather API

Python any(): Powered Up Boolean Function

The Real Python Podcast – Episode #97: Improving Your Django and Python Developer Experience

Defining Python Functions With Optional Arguments

The Real Python Podcast – Episode #96: Manipulating and Analyzing Audio in Python

Python's len() Function

The Real Python Podcast – Episode #95: What Is a JIT and How Can Pyjion Speed Up Your Python?

Looping With Python enumerate()

The `Image` Module and `Image` Class in Pillow

PEP 654: Exception Groups and `except*`

Basic HTTP GET Requests With `urllib.request`