Real Python

The Real Python Podcast – Episode #209: Python's Command-Line Utilities & Music Information Retrieval Tools

2024-06-21T12:00:00+00:00

What are the built-in Python modules that can work as useful command-line tools? How can these tools add more functionality to Windows machines? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Build a Guitar Synthesizer: Play Musical Tablature in Python

2024-06-19T14:00:00+00:00

Have you ever wanted to compose music without expensive gear or a professional studio? Maybe you’ve tried to play a musical instrument before but found the manual dexterity required too daunting or time-consuming. If so, you might be interested in harnessing the power of Python to create a guitar synthesizer. By following a few relatively simple steps, you’ll be able to turn your computer into a virtual guitar that can play any song.

In this tutorial, you’ll:

Implement the Karplus-Strong plucked string synthesis algorithm
Mimic different types of string instruments and their tunings
Combine multiple vibrating strings into polyphonic chords
Simulate realistic guitar picking and strumming finger techniques
Use impulse responses of real instruments to replicate their unique timbre
Read musical notes from scientific pitch notation and guitar tablature

At any point, you’re welcome to download the complete source code of the guitar synthesizer, as well as the sample tablature and other resources that you’ll use throughout this tutorial. They might prove useful in case you want to explore the code in more detail or get a head start. To download the bonus materials now, visit the following link:

Get Your Code: Click here to download the free sample code that you’ll use to build a guitar synthesizer in Python.

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

Interactive Quiz

Build a Guitar Synthesizer

In this quiz, you'll test your understanding of what it takes to build a guitar synthesizer in Python. By working through this quiz, you'll revisit a few key concepts from music theory and sound synthesis.

Demo: Guitar Synthesizer in Python

In this step-by-step guide, you’ll build a plucked string instrument synthesizer based on the Karplus-Strong algorithm in Python. Along the way, you’ll create an ensemble of virtual instruments, including an acoustic, bass, and electric guitar, as well as a banjo and ukulele. Then, you’ll implement a custom guitar tab reader so that you can play your favorite songs.

By the end of this tutorial, you’ll be able to synthesize music from guitar tablature, or guitar tabs for short, which is a simplified form of musical notation that allows you to play music without having to learn how to read standard sheet music. Finally, you’ll store the result in an MP3 file for playback.

Below is a short demonstration of the synthesizer, re-creating the iconic soundtracks of classic video games like Doom and Diablo. Click the play button to listen to the sample output:

E1M1 - At Doom's Gate (Bobby Prince), Tristram (Matt Uelmen)

Once you find a guitar tab that you like, you can plug it into your Python guitar synthesizer and bring the music to life. For example, the Songsterr website is a fantastic resource with a wide range of songs you can choose from.

Project Overview

For your convenience, the project that you’re about to build, along with its third-party dependencies, will be managed by Poetry. The project will contain two Python packages with distinctly different areas of responsibility:

digitar: For the synthesis of the digital guitar sound
tablature: For reading and interpreting guitar tablature from a file

You’ll also design and implement a custom data format to store guitar tabs on disk or in memory. This will allow you to play music based on a fairly standard tablature notation, which you’ll find in various places on the Internet. Your project will also provide a Python script to tie everything together, which will let you interpret the tabs with a single command right from your terminal.

Now, you can dive into the details of what you’ll need to set up your development environment and start coding.

Prerequisites

Although you don’t need to be a musician to follow along with this tutorial, a basic understanding of musical concepts such as notes, semitones, octaves, and chords will help you grasp the information more quickly. It’d also be nice if you had a rough idea of how computers represent and process digital audio in terms of sampling rate, bit depth, and file formats like WAV.

But don’t worry if you’re new to these ideas! You’ll be guided through each step in small increments with clear explanations and examples. So, even if you’ve never done any music synthesis before, you’ll have a working digital guitar or digitar by the end of this tutorial.

Note: You can learn music theory in half an hour by watching an excellent and free video by Andrew Huang.

The project that you’ll build was tested against Python 3.12 but should work fine in earlier Python versions, too, down to Python 3.10. In case you need a quick refresher, here’s a list of helpful resources covering the most important language features that you’ll take advantage of in your digital guitar journey:

Other than that, you’ll use the following third-party Python packages in your project:

NumPy to simplify and speed up the underlying sound synthesis
Pedalboard to apply special effects akin to electric guitar amplifiers
Pydantic and PyYAML to parse musical tablature representing finger movements on a guitar neck

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

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Quiz: Creating Great README Files for Your Python Projects

2024-06-19T12:00:00+00:00

Test your understanding of how a great README file can make your Python project stand out and how to create your own README files.

Take this quiz after reading our Creating Great README Files for Your Python Projects tutorial.

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

Rounding Numbers in Python

2024-06-18T14:00:00+00:00

With many businesses turning to Python’s powerful data science ecosystem to analyze their data, understanding how to avoid introducing bias into datasets is absolutely vital. If you’ve studied some statistics, then you’re probably familiar with terms like reporting bias, selection bias, and sampling bias. There’s another type of bias that plays an important role when you’re dealing with numeric data: rounding bias.

Understanding how rounding works in Python can help you avoid biasing your dataset. This is an important skill. After all, drawing conclusions from biased data can lead to costly mistakes.

In this video course, you’ll learn:

Why the way you round numbers is important
How to round a number according to various rounding strategies
How to implement each strategy in pure Python
How rounding affects data and which rounding strategy minimizes this effect
How to round numbers in NumPy arrays and pandas DataFrames
When to apply different rounding strategies

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

Quiz: Build a Guitar Synthesizer

2024-06-18T12:00:00+00:00

In this quiz, you’ll test your understanding of what it takes to build a guitar synthesizer in Python. By working through this quiz, you’ll revisit a few key concepts from the music theory and sound synthesis.

[ 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 ]

Ruff: A Modern Python Linter for Error-Free and Maintainable Code

2024-06-17T14:00:00+00:00

Linting is essential to writing clean and readable code that you can share with others. A linter, like Ruff, is a tool that analyzes your code and looks for errors, stylistic issues, and suspicious constructs. Linting allows you to address issues and improve your code quality before you commit your code and share it with others.

Ruff is a modern linter that’s extremely fast and has a simple interface, making it straightforward to use. It also aims to be a drop-in replacement for many other linting and formatting tools, such as Flake8, isort, and Black. It’s quickly becoming one of the most popular Python linters.

In this tutorial, you’ll learn how to:

Install Ruff
Check your Python code for errors
Automatically fix your linting errors
Use Ruff to format your code
Add optional configurations to supercharge your linting

To get the most from this tutorial, you should be familiar with virtual environments, installing third-party modules, and be comfortable with using the terminal.

Ruff cheat sheet: Click here to get access to a free Ruff cheat sheet that summarizes the main Ruff commands you’ll use in this tutorial.

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

Interactive Quiz

Ruff: A Modern Python Linter

In this quiz, you'll test your understanding of Ruff, a modern linter for Python. By working through this quiz, you'll revisit why you'd want to use Ruff to check your Python code and how it automatically fixes errors, formats your code, and provides optional configurations to enhance your linting.

Installing Ruff

Now that you know why linting your code is important and how Ruff is a powerful tool for the job, it’s time to install it. Thankfully, Ruff works out of the box, so no complicated installation instructions or configurations are needed to start using it.

Assuming your project is already set up with a virtual environment, you can install Ruff in the following ways:

Shell
      
$ python -m pip install ruff

In addition to pip, you can also install Ruff with Homebrew if you’re on macOS or Linux:

Shell
      
$ brew install ruff

Conda users can install Ruff using conda-forge:

Shell
      
$ conda install -c conda-forge ruff

If you use Arch, Alpine, or openSUSE Linux, you can also use the official distribution repositories. You’ll find specific instructions on the Ruff installation page of the official documentation.

Additionally, if you’d like Ruff to be available for all your projects, you might want to install Ruff with pipx.

You can check that Ruff installed correctly by using the ruff version command:

Shell
      
$ ruff version
ruff 0.4.7

For the ruff command to appear in your PATH, you may need to close and reopen your terminal application or start a new terminal session.

Linting Your Python Code

While linting helps keep your code consistent and error-free, it doesn’t guarantee that your code will be bug-free. Finding the bugs in your code is best handled with a debugger and adequate testing, which won’t be covered in this tutorial. Coming up in the next sections, you’ll learn how to use Ruff to check for errors and speed up your workflow.

Checking for Errors

The code below is a simple script called one_ring.py. When you run it, it gets a random Lord of the Rings character name from a tuple and lets you know if that character bore the burden of the One Ring. This code has no real practical use and is just a bit of fun. Regardless of the size of your code base, the steps are going to be the same:

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

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Quiz: Ruff: A Modern Python Linter

2024-06-17T12:00:00+00:00

In this quiz, you’ll test your understanding of Ruff, a modern linter for Python.

By working through this quiz, you’ll revisit why you’d want to use Ruff to check your Python code and how it automatically fixes errors, formats your code, and provides optional configurations to enhance your linting.

[ 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 #208: Detecting Outliers in Your Data With Python

2024-06-14T12:00:00+00:00

How do you find the most interesting or suspicious points within your data? What libraries and techniques can you use to detect these anomalies with Python? This week on the show, we speak with author Brett Kennedy about his book "Outlier Detection in Python."

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

Python Mappings: A Comprehensive Guide

2024-06-12T14:00:00+00:00

One of the main data structures you learn about early in your Python learning journey is the dictionary. Dictionaries are the most common and well-known of Python’s mappings. However, there are other mappings in Python’s standard library and third-party modules. Mappings share common characteristics, and understanding these shared traits will help you use them more effectively.

In this tutorial, you’ll learn about:

Basic characteristics of a mapping
Operations that are common to most mappings
Abstract base classes Mapping and MutableMapping
User-defined mutable and immutable mappings and how to create them

This tutorial assumes that you’re familiar with Python’s built-in data types, especially dictionaries, and with the basics of object-oriented programming.

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

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

Interactive Quiz

Python Mappings

In this quiz, you'll test your understanding of the basic characteristics and operations of Python mappings. By working through this quiz, you'll revisit the key concepts and techniques of creating a custom mapping.

Understanding the Main Characteristics of Python Mappings

A mapping is a collection that allows you to look up a key and retrieve its value. The keys in mappings can be objects of a broad range of types. However, in most mappings, there are object types that can’t be used as keys, as you’ll learn later in this tutorial.

The previous paragraph described mappings as collections. A collection is an iterable container that has a defined size. However, mappings also have additional features. You’ll explore each of these mapping characteristics with examples from Python’s main mapping types.

The feature that’s most characteristic of mappings is the ability to retrieve a value using a key. You can use a dictionary to demonstrate this operation:

Python
      
        
      
    
>>> points = {
...     "Denise": 3,
...     "Igor": 2,
...     "Sarah": 3,
...     "Trevor": 1,
... }
>>> points["Sarah"]
3
>>> points["Matt"]
Traceback (most recent call last):
  ...
KeyError: 'Matt'

The dictionary points contains four items, each with a key and a value. You can use the key within the square brackets to fetch the value associated with that key. However, if the key doesn’t exist in the dictionary, the code raises a KeyError.

You can use one of the mappings in the standard-library collections module to assign a default value for keys that aren’t present in the collection. The defaultdict type includes a callable that’s called each time you try to access a key that doesn’t exist. If you want the default value to be zero, you can use a lambda function that returns 0 as the first argument in defaultdict:

Python
      
        
      
    
>>> from collections import defaultdict
>>> points_default = defaultdict(
...     lambda: 0,
...     points,
... )

>>> points_default
defaultdict(<function <lambda> at 0x104a95da0>, {'Denise': 3,
    'Igor': 2, 'Sarah': 3, 'Trevor': 1})
>>> points_default["Sarah"]
3
>>> points_default["Matt"]
0
>>> points_default
defaultdict(<function <lambda> at 0x103e6c700>, {'Denise': 3,
    'Igor': 2, 'Sarah': 3, 'Trevor': 1, 'Matt': 0})

The defaultdict constructor has two arguments in this example. The first argument is the callable that’s used when a default value is needed. The second argument is the dictionary you created earlier. You can use any valid argument when you call dict() as the second argument in defaultdict() or omit this argument to create an empty defaultdict.

When you access a key that’s missing from the dictionary, the key is added, and the default value is assigned to it. You can also create the same points_default object using the callable int as the first argument since calling int() with no arguments returns 0.

All mappings are also collections, which means they’re iterable containers with a defined length. You can explore these characteristics with another mapping in Python’s standard library, collections.Counter:

Python
      
        
      
    
>>> from collections import Counter
>>> letters = Counter("learning python")
>>> letters
Counter({'n': 3, 'l': 1, 'e': 1, 'a': 1, 'r': 1, 'i': 1, 'g': 1,
    ' ': 1, 'p': 1, 'y': 1, 't': 1, 'h': 1, 'o': 1})

The letters in the string "learning python" are converted into keys in Counter, and the number of occurrences of each letter is used as the value corresponding to each key.

You can confirm that this mapping is iterable, has a defined length, and is a container:

Python
      
        
      
    
>>> for letter in letters:
...     print(letter)
...
l
e
a
r
n
i
g

p
y
t
h
o

>>> len(letters)
13

>>> "n" in letters
True
>>> "x" in letters
False

You can use the Counter object letters in a for loop, which confirms it’s iterable. All mappings are iterable. However, the iteration loops through the keys and not the values. You’ll see how to iterate through the values or through both keys and values later in this tutorial.

The built-in len() function returns the number of items in the mapping. This is equal to the number of unique characters in the original string, including the space character. The object is sized since len() returns a value.

You can use the in keyword to confirm which elements are in the mapping. This check alone isn’t sufficient to confirm that the mapping is a container. However, you can also access the object’s .__contains__() special method directly:

Python
      
>>> letters.__contains__("n")
True

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

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Quiz: Python Mappings

2024-06-12T12:00:00+00:00

In this quiz, you’ll test your understanding of the basic characteristics and operations of Python mappings. By working through this quiz, you’ll revisit the key concepts and techniques of creating a custom mapping.

[ 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 ]

Listing All Files in a Directory With Python

2024-06-11T14:00:00+00:00

Getting a list of all the files and folders in a directory is a natural first step for many file-related operations in Python. When looking into it, though, you may be surprised to find various ways to go about it.

When you’re faced with many ways of doing something, it can be a good indication that there’s no one-size-fits-all solution to your problems. Most likely, every solution will have its own advantages and trade-offs. This is the case when it comes to getting a list of the contents of a directory in Python.

In this video course, you’ll be focusing on the most general-purpose techniques in the pathlib module to list items in a directory, but you’ll also learn a bit about some alternative tools.

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

Python News: What's New From May 2024

2024-06-10T14:00:00+00:00

May was packed with exciting updates and events in the Python community. This month saw the release of the first beta version of Python 3.13, the conclusion of PyCon US 2024, and the announcement of the keynote speakers for EuroPython 2024. Additionally, PEP 649 has been delayed until the Python 3.14 release, and the Python Software Foundation published its 2023 Annual Impact Report.

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.

The First Beta Version of Python 3.13 Released

After nearly a year of continuous development, the first beta release of Python 3.13 was made available to the general public. It marks a significant milestone in Python’s annual release cycle, officially kicking off the beta testing phase and introducing a freeze on new features. Beyond this point, Python’s core developers will shift their focus to only identifying and fixing bugs, enhancing security, and improving the interpreter’s performance.

While it’s still months before the final release planned for October 2024, as indicated by the Python 3.13 release schedule, third-party library maintainers are strongly encouraged to test their packages with this new Python version. The goal of early beta testing is to ensure compatibility and address any issues that may arise so that users can expect a smoother transition when Python 3.13 gets officially released later this year.

Although you shouldn’t use a beta version in any of your projects, especially in production environments, you can go ahead and try out the new version today. To check out Python’s latest features, you must install Python 3.13.0b1 using one of several approaches.

Note: If you’d like to share your feedback or file a bug against a pre-release development version, then open an issue on Python’s GitHub repository.

The quickest and arguably the most straightforward way to manage multiple Python versions alongside your system-wide interpreter is to use a tool like pyenv in the terminal:

Shell
      
        
      
    
$ pyenv install 3.13.0b1
$ pyenv shell 3.13.0b1
$ python
Python 3.13.0b1 (main, May 15 2024, 10:41:55) [GCC 13.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 

The highlighted line brings the first beta release of Python 3.13 onto your computer, while the following command temporarily sets the path to the python executable in your current shell session. As a result, the python command points to the specified Python interpreter.

Alternatively, you can use a Python installer, which you’ll find at the bottom of the downloads page, or run Python in an isolated Docker container to keep it completely separate from your operating system. However, for ultimate control, you can try building the interpreter from source code based on the instructions in the README file. This method will let you experiment with more advanced features, like turning off the GIL.

Unlike previous Python releases, which introduced a host of tangible syntactical features that you could get your hands on, this one mainly emphasizes internal optimizations and cleanup. That said, according to the official release summary document, there are a few notable new features that will be immediately visible to most Python programmers:

Some of these features don’t work on Windows at the moment because they rely on Unix-specific libraries, so you won’t see any difference unless you’re a macOS or Linux user. The good news is that Windows support is coming in the second beta release, which will arrive soon, thanks to Real Python team member Anthony Shaw.

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

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The Real Python Podcast – Episode #207: Decomposing Software Problems & Avoiding the Trap of Clever Code

2024-06-07T12:00:00+00:00

How do you effectively break a software problem into individual steps? What are signs you're writing overly clever code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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

Python String Formatting: Available Tools and Their Features

2024-06-05T14:00:00+00:00

String formatting is the process of applying a proper format to a given value while using this value to create a new string through interpolation. Python has several tools for string interpolation that support many formatting features. In modern Python, you’ll use f-strings or the .format() method most of the time. However, you’ll see the modulo operator (%) being used in legacy code.

In this tutorial, you’ll learn how to:

Format strings using f-strings for eager interpolation
Use the .format() method to format your strings lazily
Work with the modulo operator (%) for string formatting
Decide which interpolation and formatting tool to use

To get the most out of this tutorial, you should be familiar with Python’s string data type and the available string interpolation tools. Having a basic knowledge of the string formatting mini-language is also a plus.

Get Your Code: Click here to download the free sample code you’ll use to learn about Python’s string formatting tools.

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

Interactive Quiz

Python String Formatting: Available Tools and Their Features

Take this quiz to test your understanding of the available tools for string formatting in Python, as well as their strengths and weaknesses. These tools include f-strings, the .format() method, and the modulo operator.

Interpolating and Formatting Strings in Python

String interpolation involves generating strings by inserting other strings or objects into specific places in a base string or template. For example, here’s how you can do some string interpolation using an f-string:

Python
      
>>> name = "Bob"

>>> f"Hello, {name}!"
'Hello, Bob!'

In this quick example, you first have a Python variable containing a string object, "Bob". Then, you create a new string using an f-string. In this string, you insert the content of your name variable using a replacement field. When you run this last line of code, Python builds a final string, 'Hello, Bob!'. The insertion of name into the f-string is an interpolation.

Note: To dive deeper into string interpolation, check out the String Interpolation in Python: Exploring Available Tools tutorial.

When you do string interpolation, you may need to format the interpolated values to produce a well-formatted final string. To do this, you can use different string interpolation tools that support string formatting. In Python, you have these three tools:

The first two tools support the string formatting mini-language, a feature that allows you to fine-tune your strings. The third tool is a bit old and has fewer formatting options. However, you can use it to do some minimal formatting.

Note: The built-in format() function is yet another tool that supports the format specification mini-language. This function is typically used for date and number formatting, but you won’t cover it in this tutorial.

In the following sections, you’ll start by learning a bit about the string formatting mini-language. Then, you’ll dive into using this language, f-strings, and the .format() method to format your strings. Finally, you’ll learn about the formatting capabilities of the modulo operator.

Using F-Strings to Format Strings

Python 3.6 added a string interpolation and formatting tool called formatted string literals, or f-strings for short. As you’ve already learned, f-strings let you embed Python objects and expressions inside your strings. To create an f-string, you must prefix the string with an f or F and insert replacement fields in the string literal. Each replacement field must contain a variable, object, or expression:

Python
      
>>> f"The number is {42}"
'The number is 42'

>>> a = 5
>>> b = 10
>>> f"{a} plus {b} is {a + b}"
'5 plus 10 is 15'

In the first example, you define an f-string that embeds the number 42 directly into the resulting string. In the second example, you insert two variables and an expression into the string.

Formatted string literals are a Python parser feature that converts f-strings into a series of string constants and expressions. These are then joined up to build the final string.

Using the Formatting Mini-Language With F-Strings

When you use f-strings to create strings through interpolation, you need to use replacement fields. In f-strings, you can define a replacement field using curly brackets ({}) as in the examples below:

Python
      
>>> debit = 300.00
>>> credit = 450.00

>>> f"Debit: ${debit}, Credit: ${credit}, Balance: ${credit - debit}"
'Debit: $300, Credit: $450.0, Balance: $150.0'

Inside the brackets, you can insert Python objects and expressions. In this example, you’d like the resulting string to display the currency values using a proper format. However, you get a string that shows the currency values with at most one digit on its decimal part.

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

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Python Interfaces: Object-Oriented Design Principles

2024-06-04T14:00:00+00:00

Interfaces play an important role in software engineering. As an application grows, updates and changes to the code base become more difficult to manage. More often than not, you wind up having classes that look very similar but are unrelated, which can lead to some confusion. In this video course, you’ll see how you can use a Python interface to help determine what class you should use to tackle the current problem.

In this video course, you’ll be able to:

Understand how interfaces work and the caveats of Python interface creation
Comprehend how useful interfaces are in a dynamic language like Python
Implement an informal Python interface
Use abc.ABCMeta and @abc.abstractmethod to implement a formal Python interface

[ 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 ]

String Interpolation in Python: Exploring Available Tools

2024-06-03T14:00:00+00:00

String interpolation allows you to create strings by inserting objects into specific places in a target string template. Python has several tools for string interpolation, including f-strings, the str.format() method, and the modulo operator (%). Python’s string module also provides the Template class, which you can use for string interpolation.

In this tutorial, you’ll:

Learn how to use f-strings for eager string interpolation
Perform lazy string interpolation using the str.format() method
Learn the basics of using the modulo operator (%) for string interpolation
Decide whether to use f-strings or the str.format() method for interpolation
Create templates for string interpolation with string.Template

To get the most out of this tutorial, you should be familiar with Python strings, which are represented by the str class.

Get Your Code: Click here to download the free sample code you’ll use to explore string interpolation tools in Python.

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

Interactive Quiz

String Interpolation in Python: Exploring Available Tools

Take this quiz to test your understanding of the available tools for string interpolation in Python, as well as their strengths and weaknesses. These tools include f-strings, the .format() method, and the modulo operator.

String Interpolation in Python

Sometimes, when working with strings, you’d make up strings by using multiple different string values. Initially, you could use the plus operator (+) to concatenate strings in Python. However, this approach results in code with many quotes and pluses:

Python
      
>>> name = "Pythonista"
>>> day = "Friday"  # Of course 😃

>>> "Hello, " + name + "! Today is " + day + "."
'Hello, Pythonista! Today is Friday.'

In this example, you build a string using some text and a couple of variables that hold string values. The many plus signs make the code hard to read and write. Python must have a better and cleaner way.

Note: To learn more about string concatenation in Python, check out the Efficient String Concatenation in Python tutorial.

The modulo operator (%) came to make the syntax a bit better:

Python
      
>>> "Hello, %s! Today is %s." % (name, day)
'Hello, Pythonista! Today is Friday.'

In this example, you use the modulo operator to insert the name and day variables into the string literals. The process of creating strings by inserting other strings into them, as you did here, is known as string interpolation.

Note: Formatting with the modulo operator is inspired by printf() formatting used in C and many other programming languages.

The %s combination of characters is known as a conversion specifier. They work as replacement fields. The % operator marks the start of the specifier, while the s letter is the conversion type and tells the operator that you want to convert the input object into a string. You’ll learn more about conversion specifiers in the section about the modulo operator.

Note: In this tutorial, you’ll learn about two different types of string interpolation:

Eager interpolation
Lazy interpolation

In eager interpolation, Python inserts the values into the string at execution time in the same place where you define the string. In lazy interpolation, Python delays the insertion until the string is actually needed. In this latter case, you create string templates at one point in your code and fill the template with values at another point.

But the story doesn’t end with the modulo operator. Later, Python introduced the str.format() method:

Python
      
>>> "Hello, {}! Today is {}.".format(name, day)
'Hello, Pythonista! Today is Friday.'

The method interpolates its arguments into the target string using replacement fields limited by curly brackets. Even though this method can produce hard-to-read code, it represents a significant advance over the modulo operator: it supports the string formatting mini-language.

Note: String formatting is a fundamental topic in Python, and sometimes, people think that formatting and interpolation are the same. However, they’re not. In this tutorial, you’ll only learn about interpolation. To learn about string formatting and the formatting mini-language, check out the Python’s Format Mini-Language for Tidy Strings tutorial.

Python continues to evolve, and every new version brings new, exciting features. Python 3.6 introduced formatted string literals, or f-strings for short:

Python
      
>>> f"Hello, {name}! Today is {day}."
'Hello, Pythonista! Today is Friday.'

F-strings offer a more readable and clean way to create strings that include other strings. To make an f-string, you must prefix it with an f or F. Again, curly brackets delimit the replacement fields.

Note: To learn more about f-strings, check out the Python’s F-String for String Interpolation and Formatting tutorial.

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

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Quiz: String Interpolation in Python: Exploring Available Tools

2024-06-03T12:00:00+00:00

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

Take this quiz after reading our String Interpolation in Python: Exploring Available Tools tutorial.

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

The Real Python Podcast – Episode #206: Building Python Unit Tests & Exploring a Data Visualization Gallery

2024-05-31T12:00:00+00:00

How do you start adding unit tests to your Python code? Can the built-in unittest framework cover most or all of your needs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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What Are CRUD Operations?

2024-05-29T14:00:00+00:00

CRUD operations are at the heart of nearly every application you interact with. As a developer, you usually want to create data, read or retrieve data, update data, and delete data. Whether you access a database or interact with a REST API, only when all four operations are present are you able to make a complete data roundtrip in your app.

Creating, reading, updating, and deleting are so vital in software development that these methods are widely referred to as CRUD. Understanding CRUD will give you an actionable blueprint when you build applications and help you understand how the applications you use work behind the scenes. So, what exactly does CRUD mean?

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

Take the Quiz: Test your knowledge with our interactive “What Are CRUD Operations?” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

What Are CRUD Operations?

In this quiz, you'll revisit the key concepts and techniques related to CRUD operations. These operations are fundamental to any system that interacts with a database, and understanding them is crucial for effective data management.

In Short: CRUD Stands for Create, Read, Update, and Delete

CRUD operations are the cornerstone of application functionality, touching every aspect of how apps store, retrieve, and manage data. Here’s a brief overview of the four CRUD operations:

Create: This is about adding new entries to your database. But it’s also applicable to other types of persistent storage, such as files or networked services. When you perform a create operation, you’re initiating a journey for a new piece of data within your system.
Read: Through reading, you retrieve or view existing database entries. This operation is as basic as checking your email or reloading a website. Every piece of information you get has been received from a database, thanks to the read operation.
Update: Updating allows you to modify the details of data already in the database. For example, when you update a profile picture or edit a chat message. Each time, there’s an update operation at work, ensuring your new data is stored in the database.
Delete: Deleting removes existing entries from the database. Whether you’re closing an account or removing a post, delete operations ensure that unwanted or unnecessary data can be properly discarded.

CRUD operations describe the steps that data takes from creation to deletion, regardless of what programming language you use. Every time you interact with an application, you’re likely engaging in one of the four CRUD operations.

Why Are CRUD Operations Essential?

Whether you’re working on a basic task list app or a complex e-commerce platform, CRUD operations offer a universal language for designing and manipulating data models. Knowing about CRUD as a user helps you understand what’s happening behind the curtains. As a developer, understanding CRUD provides you with a structured framework for storing data in your application with persistence:

In computer science, persistence refers to the characteristic of state of a system that outlives (persists more than) the process that created it. This is achieved in practice by storing the state as data in computer data storage. (Source)

So even when a program crashes or a user disconnects, the data is safe and can be retrieved later. This also means that the order of the operations is important. You can only read, update, or delete items that were previously created.

It’s good practice to implement each CRUD operation separately in your applications. For example, when you retrieve items, then you shouldn’t update them at the same time.

Note: An exception to this rule may be when you update a “last time retrieved” value after a read operation. Although the user performs a read CRUD operation to retrieve data, you may want to trigger an update operation in the back end to keep track of a user’s retrievals. This can be handy if you want to show the last visited posts to the user.

While CRUD describes a concept that’s independent of specific programming languages, one could argue that CRUD operations are strongly connected to SQL commands and HTTP methods.

What Are CRUD Operations in SQL?

The idea of CRUD is strongly connected with databases. That’s why it’s no surprise that CRUD operations correspond almost one-to-one with SQL commands:

CRUD Operation	SQL Command
Create	`INSERT`
Read	`SELECT`
Update	`UPDATE`
Delete	`DELETE`

When you create data, you’re using the INSERT command to add new records to a table. After creation, you may read data using SELECT. With a SELECT query, you’re asking the database to retrieve the specific pieces of information you need, whether it’s a single value, a set of records, or complex relationships between data points.

The update operation corresponds to the UPDATE command in SQL, which allows you to modify data. It lets you edit or change an existing item.

Lastly, the delete operation relates to the DELETE command. This is the digital equivalent of shredding a confidential document. With DELETE, you permanently remove an item from the database.

Writing CRUD Operations in Raw SQL

CRUD operations describe actions. That’s why it’s a good idea to pull up your sleeves and write some code to explore how CRUD operations translate into raw SQL commands.

In the examples below, you’ll use Python’s built-in sqlite3 package. SQLite is a convenient SQL library to try things out, as you’ll work with a single SQLite database file.

You’ll name the database birds.db. As the name suggests, you’ll use the database to store the names of birds you like. To keep the example small, you’ll only keep track of the bird names and give them an ID as a unique identifier.

Read the full article at https://realpython.com/crud-operations/ »

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Efficient Iterations With Python Iterators and Iterables

2024-05-28T14:00:00+00:00

Python’s iterators and iterables are two different but related tools that come in handy when you need to iterate over a data stream or container. Iterators power and control the iteration process, while iterables typically hold data that you want to iterate over one value at a time.

Iterators and iterables are fundamental components of Python programming, and you’ll have to deal with them in almost all your programs. Learning how they work and how to create them is key for you as a Python developer.

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

Create iterators using the iterator protocol in Python
Understand the differences between iterators and iterables
Work with iterators and iterables in your Python code
Use generator functions and the yield statement to create generator iterators
Build your own iterables using different techniques, such as the iterable protocol

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

How to Create Pivot Tables With pandas

2024-05-27T14:00:00+00:00

A pivot table is a data analysis tool that allows you to take columns of raw data from a pandas DataFrame, summarize them, and then analyze the summary data to reveal its insights.

Pivot tables allow you to perform common aggregate statistical calculations such as sums, counts, averages, and so on. Often, the information a pivot table produces reveals trends and other observations your original raw data hides.

Pivot tables were originally implemented in early spreadsheet packages and are still a commonly used feature of the latest ones. They can also be found in modern database applications and in programming languages. In this tutorial, you’ll learn how to implement a pivot table in Python using pandas’ DataFrame.pivot_table() method.

Before you start, you should familiarize yourself with what a pandas DataFrame looks like and how you can create one. Knowing the difference between a DataFrame and a pandas Series will also prove useful.

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

The other thing you’ll need for this tutorial is, of course, data. You’ll use the Sales Data Presentation - Dashboards data, which is freely available for you to use under the Apache 2.0 License. The data has been made available for you in the sales_data.csv file that you can download by clicking the link below.

Get Your Code: Click here to download the free sample code you’ll use to create a pivot table with pandas.

This table provides an explanation of the data you’ll use throughout this tutorial:

Column Name	Data Type (PyArrow)	Description
`order_number`	`int64`	Order number (unique)
`employee_id`	`int64`	Employee’s identifier (unique)
`employee_name`	`string`	Employee’s full name
`job_title`	`string`	Employee’s job title
`sales_region`	`string`	Sales region employee works within
`order_date`	`timestamp[ns]`	Date order was placed
`order_type`	`string`	Type of order (Retail or Wholesale)
`customer_type`	`string`	Type of customer (Business or Individual)
`customer_name`	`string`	Customer’s full name
`customer_state`	`string`	Customer’s state of residence
`product_category`	`string`	Category of product (Bath Products, Gift Basket, Olive Oil)
`product_number`	`string`	Product identifier (unique)
`product_name`	`string`	Name of product
`quantity`	`int64`	Quantity ordered
`unit_price`	`double`	Selling price of one product
`sale_price`	`double`	Total sale price (`unit_price` × `quantity`)

As you can see, the table stores data for a fictional set of orders. Each row contains information about a single order. You’ll become more familiar with the data as you work through the tutorial and try to solve the various challenge exercises contained within it.

Throughout this tutorial, you’ll use the pandas library to allow you to work with DataFrames and the newer PyArrow library. The PyArrow library provides pandas with its own optimized data types, which are faster and less memory-intensive than the traditional NumPy types pandas uses by default.

If you’re working at the command line, you can install both pandas and pyarrow using python -m pip install pandas pyarrow, perhaps within a virtual environment to avoid clashing with your existing environment. If you’re working within a Jupyter Notebook, you should use !python -m pip install pandas pyarrow. With the libraries in place, you can then read your data into a DataFrame:

Python
      
>>> import pandas as pd

>>> sales_data = pd.read_csv(
...     "sales_data.csv",
...     parse_dates=["order_date"],
...     dayfirst=True,
... ).convert_dtypes(dtype_backend="pyarrow")

First of all, you used import pandas to make the library available within your code. To construct the DataFrame and read it into the sales_data variable, you used pandas’ read_csv() function. The first parameter refers to the file being read, while parse_dates highlights that the order_date column’s data is intended to be read as the datetime64[ns] type. But there’s an issue that will prevent this from happening.

In your source file, the order dates are in dd/mm/yyyy format, so to tell read_csv() that the first part of each date represents a day, you also set the dayfirst parameter to True. This allows read_csv() to now read the order dates as datetime64[ns] types.

With order dates successfully read as datetime64[ns] types, the .convert_dtypes() method can then successfully convert them to a timestamp[ns][pyarrow] data type, and not the more general string[pyarrow] type it would have otherwise done. Although this may seem a bit circuitous, your efforts will allow you to analyze data by date should you need to do this.

If you want to take a look at the data, you can run sales_data.head(2). This will let you see the first two rows of your dataframe. When using .head(), it’s preferable to do so in a Jupyter Notebook because all of the columns are shown. Many Python REPLs show only the first and last few columns unless you use pd.set_option("display.max_columns", None) before you run .head().

If you want to verify that PyArrow types are being used, sales_data.dtypes will confirm it for you. As you’ll see, each data type contains [pyarrow] in its name.

Note: If you’re experienced in data analysis, you’re no doubt aware of the need for data cleansing. This is still important as you work with pivot tables, but it’s equally important to make sure your input data is also tidy.

Tidy data is organized as follows:

Each row should contain a single record or observation.
Each column should contain a single observable or variable.
Each cell should contain an atomic value.

If you tidy your data in this way, as part of your data cleansing, you’ll also be able to analyze it better. For example, rather than store address details in a single address field, it’s usually better to split it down into house_number, street_name, city, and country component fields. This allows you to analyze it by individual streets, cities, or countries more easily.

In addition, you’ll also be able to use the data from individual columns more readily in calculations. For example, if you had columns room_length and room_width, they can be multiplied together to give you room area information. If both values are stored together in a single column in a format such as "10 x 5", the calculation becomes more awkward.

The data within the sales_data.csv file is already in a suitably clean and tidy format for you to use in this tutorial. However, not all raw data you acquire will be.

It’s now time to create your first pandas pivot table with Python. To do this, first you’ll learn the basics of using the DataFrame’s .pivot_table() method.

Get Your Code: Click here to download the free sample code you’ll use to create a pivot table with pandas.

Take the Quiz: Test your knowledge with our interactive “How to Create Pivot Tables With pandas” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Create Pivot Tables With pandas

This quiz is designed to push your knowledge of pivot tables a little bit further. You won't find all the answers by reading the tutorial, so you'll need to do some investigating on your own. By finding all the answers, you're sure to learn some other interesting things along the way.

How to Create Your First Pivot Table With pandas

Now that your learning journey is underway, it’s time to progress toward your first learning milestone and complete the following task:

Calculate the total sales for each type of order for each region.

Read the full article at https://realpython.com/how-to-pandas-pivot-table/ »

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Quiz: How to Create Pivot Tables With pandas

2024-05-24T12:00:00+00:00

In this quiz, you’ll test your understanding of how to create pivot tables with pandas.

By working through this quiz, you’ll review your knowledge of pivot tables and also expand beyond what you learned in the tutorial. For some of the questions, you’ll need to do some research outside of the tutorial itself.

[ 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 Python calendar Module: Create Calendars With Python

2024-05-22T14:00:00+00:00

The Python calendar module provides several ways to generate calendars for Python programs. It also includes a variety of functions for working with calendar data as strings, numbers, and datetime objects.

In this tutorial, you’ll learn how to use the calendar module to create and customize calendars with Python.

By the end of this tutorial, you’ll be able to:

Display calendars in your terminal with Python
Create plain text and HTML calendars
Format calendars for specific locales and display conventions
Use calendar-related functions and methods to access lower-level calendar data in a variety of formats

Get Your Code: Click here to download the free sample code you’ll use to learn about creating calendars with the calendar module in Python.

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

Interactive Quiz

The Python calendar Module

In this quiz, you'll test your understanding of the calendar module in Python. It'll evaluate your proficiency in manipulating, customizing, and displaying calendars directly within your terminal. By working through this quiz, you'll revisit the fundamental functions and methods provided by the calendar module.

Displaying Calendars in Your Terminal

Unix and Unix-like operating systems such as macOS and Linux include a cal command-line utility for displaying calendars in an interactive console:

Shell
      
        
      
    
$ cal
      May 2024        
Su Mo Tu We Th Fr Sa  
          1  2  3  4  
 5  6  7  8  9 10 11  
12 13 14 15 16 17 18  
19 20 21 22 23 24 25  
26 27 28 29 30 31     

Python provides a similar tool, which allows you to run the calendar module as a command-line script. To begin exploring the Python calendar module, open up your terminal program and enter the following command:

Shell
      
        
      
    
$ python -m calendar
                                  2024

      January                   February                   March
Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su
 1  2  3  4  5  6  7                1  2  3  4                   1  2  3
 8  9 10 11 12 13 14       5  6  7  8  9 10 11       4  5  6  7  8  9 10
15 16 17 18 19 20 21      12 13 14 15 16 17 18      11 12 13 14 15 16 17
22 23 24 25 26 27 28      19 20 21 22 23 24 25      18 19 20 21 22 23 24
29 30 31                  26 27 28 29               25 26 27 28 29 30 31

       April                      May                       June
Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su
 1  2  3  4  5  6  7             1  2  3  4  5                      1  2
 8  9 10 11 12 13 14       6  7  8  9 10 11 12       3  4  5  6  7  8  9
15 16 17 18 19 20 21      13 14 15 16 17 18 19      10 11 12 13 14 15 16
22 23 24 25 26 27 28      20 21 22 23 24 25 26      17 18 19 20 21 22 23
29 30                     27 28 29 30 31            24 25 26 27 28 29 30

        July                     August                  September
Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su
 1  2  3  4  5  6  7                1  2  3  4                         1
 8  9 10 11 12 13 14       5  6  7  8  9 10 11       2  3  4  5  6  7  8
15 16 17 18 19 20 21      12 13 14 15 16 17 18       9 10 11 12 13 14 15
22 23 24 25 26 27 28      19 20 21 22 23 24 25      16 17 18 19 20 21 22
29 30 31                  26 27 28 29 30 31         23 24 25 26 27 28 29
                                                    30

      October                   November                  December
Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su
    1  2  3  4  5  6                   1  2  3                         1
 7  8  9 10 11 12 13       4  5  6  7  8  9 10       2  3  4  5  6  7  8
14 15 16 17 18 19 20      11 12 13 14 15 16 17       9 10 11 12 13 14 15
21 22 23 24 25 26 27      18 19 20 21 22 23 24      16 17 18 19 20 21 22
28 29 30 31               25 26 27 28 29 30         23 24 25 26 27 28 29
                                                    30 31

Running python -m calendar with no arguments outputs a full year’s calendar for the current year. To display the full calendar for a different year, pass in the integer representation of a year as the first argument of the calendar command:

Shell
      
$ python -m calendar 1989

To view a single month, pass in both a year and a month as the second parameter:

Shell
      
        
      
    
$ python -m calendar 2054 07
     July 2054
Mo Tu We Th Fr Sa Su
       1  2  3  4  5
 6  7  8  9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30 31

As you can see in these examples, the calendar module can display calendars for both past and future dates. According to the official documentation, the calendar module uses the current Gregorian calendar, extended indefinitely in both directions. It also uses the ISO 8601 standard, which is an international standard for exchanging and communicating date and time-related data.

Now that you know how to display calendars in your terminal with Python, you can move on and explore other approaches to creating calendars as plain text or HTML markup representations.

Creating Text-Based Calendars

To generate plain text calendars, the calendar module provides calendar.TextCalendar with methods to format and print monthly and yearly calendars.

TextCalendar.formatyear() accepts a single parameter for the year, like the calendar command-line script. Try it out in your Python REPL by executing the following code:

Python
      
>>> import calendar
>>> text_calendar = calendar.TextCalendar()
>>> text_calendar.formatyear(2024)
'                                  2024\n\n      January    (...)'

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

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

Building a Python GUI Application With Tkinter

2024-05-21T14:00:00+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 video course, 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

[ 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 ]

Basic Data Types in Python: A Quick Exploration

2024-05-20T14:00:00+00:00

Python has several basic data types that are built into the language. With these types, you can represent numeric values, text and binary data, and Boolean values in your code. So, these data types are the basic building blocks of most Python programs and projects.

In this tutorial, you’ll learn about:

Numeric types, such as int, float, and complex
The str data type, which represents textual data
The bytes and bytearray data types for storing bytes
Boolean values with the bool data type

In this tutorial, you’ll learn only the basics of each data type. To learn more about a specific data type, you’ll find useful resources in the corresponding section.

Get Your Code: Click here to download the free sample code that you’ll use to learn about basic data types in Python.

Take the Quiz: Test your knowledge with our interactive “Basic Data Types in Python: A Quick Exploration” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Basic Data Types in Python: A Quick Exploration

Take this quiz to test your understanding of the basic data types that are built into Python, like numbers, strings, bytes, and Booleans.

Python’s Basic Data Types

Python has several built-in data types that you can use out of the box because they’re built into the language. From all the built-in types available, you’ll find that a few of them represent basic objects, such as numbers, strings and characters, bytes, and Boolean values.

Note that the term basic refers to objects that can represent data you typically find in real life, such as numbers and text. It doesn’t include composite data types, such as lists, tuples, dictionaries, and others.

In Python, the built-in data types that you can consider basic are the following:

Class	Basic Type
`int`	Integer numbers
`float`	Floating-point numbers
`complex`	Complex numbers
`str`	Strings and characters
`bytes`, `bytearray`	Bytes
`bool`	Boolean values

In the following sections, you’ll learn the basics of how to create, use, and work with all of these built-in data types in Python.

Integer Numbers

Integer numbers are whole numbers with no decimal places. They can be positive or negative numbers. For example, 0, 1, 2, 3, -1, -2, and -3 are all integers. Usually, you’ll use positive integer numbers to count things.

In Python, the integer data type is represented by the int class:

Python
      
>>> type(42)
<class 'int'>

In the following sections, you’ll learn the basics of how to create and work with integer numbers in Python.

Integer Literals

When you need to use integer numbers in your code, you’ll often use integer literals directly. Literals are constant values of built-in types spelled out literally, such as integers. Python provides a few different ways to create integer literals. The most common way is to use base-ten literals that look the same as integers look in math:

Python
      
>>> 42
42

>>> -84
-84

>>> 0
0

Here, you have three integer numbers: a positive one, a negative one, and zero. Note that to create negative integers, you need to prepend the minus sign (-) to the number.

Python has no limit to how long an integer value can be. The only constraint is the amount of memory your system has. Beyond that, an integer can be as long as you need:

Python
      
>>> 123123123123123123123123123123123123123123123123 + 1
123123123123123123123123123123123123123123123124

For a really, really long integer, you can get a ValueError when converting it to a string:

Python
      
        
      
    
>>> 123 ** 10000
Traceback (most recent call last):
  ...
ValueError: Exceeds the limit (4300 digits) for integer string conversion;
            use sys.set_int_max_str_digits() to increase the limit

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

[ 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 #205: Considering Accessibility & Assistive Tech as a Python Developer

2024-05-17T12:00:00+00:00

What's it like to learn Python as a visually impaired or blind developer? How can you improve the accessibility of your Python web applications and learn current guidelines? This week on the show, Real Python community member Audrey van Breederode discusses her programming journey, web accessibility, and assistive technology.

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

Quiz: What Are CRUD Operations?

2024-05-15T12:00:00+00:00

In this quiz, you’ll test your understanding of CRUD Operations.

By working through this quiz, you’ll revisit the key concepts and techniques related to CRUD operations. Good luck!

[ 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 ]

HTML and CSS Foundations for Python Developers

2024-05-14T14:00:00+00:00

When you want to build websites as a Python programmer, there’s no way around HTML and CSS. Almost every website on the Internet is built with HTML markup to structure the page. To make a website look nice, you can style HTML with CSS.

If you’re interested in web development with Python, then knowing HTML and CSS will help you understand web frameworks like Django and Flask better. But even if you’re just getting started with Python, HTML and CSS can enable you to create small websites to impress your friends.

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

Structure a basic HTML file
View and inspect HTML in your browser
Insert images and page links
Style a website with CSS
Format HTML with accessibility in mind
Use Python to write and parse HTML code

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

The Real Python Podcast – Episode #204: Querying OpenStreetMaps via API & Lazy Evaluation in Python

2024-05-10T12:00:00+00:00

Would you like to get more practice working with APIs in Python? How about exploring the globe using the data from OpenStreetMap? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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

Flattening a List of Lists in Python

2024-05-07T14:00:00+00:00

Sometimes, when you’re working with data, you may have the data as a list of nested lists. A common operation is to flatten this data into a one-dimensional list in Python. Flattening a list involves converting a multidimensional list, such as a matrix, into a one-dimensional list.

In this video course, you’ll learn how to do that in Python.

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

The Real Python Podcast – Episode #203: Embarking on a Relaxed and Friendly Python Coding Journey

2024-05-03T12:00:00+00:00

Do you get stressed while trying to learn Python? Do you prefer to build small programs or projects as you continue your coding journey? This week on the show, Real Python author Stephen Gruppetta is here to talk about his new book, "The Python Coding Book."

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Quiz: The Python calendar Module

2024-05-02T12:00:00+00:00

In this quiz, you’ll test your understanding of creating calendars in Python using the calendar module.

By working through this quiz, you’ll revisit the fundamental functions and methods provided by the calendar module.

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

Working With Global Variables in Python Functions

2024-04-30T14:00:00+00:00

A global variable is a variable that you can use from any part of a program, including within functions. Using global variables inside your Python functions can be tricky. You’ll need to differentiate between accessing and changing the values of the target global variable if you want your code to work correctly.

Global variables can play a fundamental role in many software projects because they enable data sharing across an entire program. However, you should use them judiciously to avoid issues.

In this video course, you’ll:

Understand global variables and how they work in Python
Access global variables within your Python functions directly
Modify and create global variables within functions using the global keyword
Access, create, and modify global variables within your functions with the globals() function
Explore strategies to avoid using global variables in Python code

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

The Real Python Podcast – Episode #202: Pydantic Data Validation & Python Web Security Practices

2024-04-26T12:00:00+00:00

How do you verify and validate the data coming into your Python web application? What tools and security best practices should you consider as a developer? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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

Quiz: What Is the pycache Folder in Python?

2024-04-26T12:00:00+00:00

As your Python project grows, you typically organize your code in modules and packages for easier maintenance and reusability. When you do that, you’ll likely notice the sudden emergence of a __pycache__ folder alongside your original files, popping up in various locations unexpectedly.

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

Python Basics Exercises: Scopes

2024-04-23T14:00:00+00:00

On your Python journey, you’ve worked with functions and loops. To fully understand functions and loops in Python, you need to be familiar with the issue of scope.

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

Identifying the scope of objects
Working with the global keyword
Exploring the LEGB rule for scope resolution
Using the return statement in a function

Scope can be one of the more difficult programming concepts to understand, so in this video course, you’ll get additional practice working with it.

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. If you’re just getting started, then you might want to check out Python Basics: Setting Up Python before diving into this course.

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

Quiz: Python Sequences: A Comprehensive Guide

2024-04-23T12:00:00+00:00

In this quiz, you’ll test your understanding of Python sequences.

By working through this quiz, you’ll revisit the basic characteristics of a sequence, operations common to most sequences, special methods associated with sequences, and how to create user-defined mutable and immutable sequences.

[ 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 #201: Decoupling Systems to Get Closer to the Data

2024-04-19T12:00:00+00:00

What are the benefits of using a decoupled data processing system? How do you write reusable queries for a variety of backend data platforms? This week on the show, Phillip Cloud, the lead maintainer of Ibis, will discuss this portable Python dataframe library.

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

Using raise for Effective Exceptions

2024-04-16T14:00:00+00:00

In your Python journey, you’ll come across situations where you need to signal that something is going wrong in your code. For example, maybe a file doesn’t exist, a network or database connection fails, or your code gets invalid input. A common approach to tackle these issues is to raise an exception, notifying the user that an error has occurred. That’s what Python’s raise statement is for.

Learning about the raise statement allows you to effectively handle errors and exceptional situations in your code. This way, you’ll develop more robust programs and higher-quality code.

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

Raise exceptions in Python using the raise statement
Decide which exceptions to raise and when to raise them in your code
Explore common use cases for raising exceptions in Python
Apply best practices for raising exceptions in your Python code

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

The Real Python Podcast – Episode #200: Avoiding Error Culture and Getting Help Inside Python

2024-04-12T12:00:00+00:00

What is error culture, and how do you avoid it within your organization? How do you navigate alert and notification fatigue? Hey, it's episode #200! Real Python's editor-in-chief, Dan Bader, joins us this week to celebrate. Christopher Trudeau also returns to bring another batch of PyCoder's Weekly articles and projects.

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

Real Python

The Real Python Podcast – Episode #209: Python's Command-Line Utilities & Music Information Retrieval Tools

Build a Guitar Synthesizer: Play Musical Tablature in Python

Demo: Guitar Synthesizer in Python

Project Overview

Prerequisites

Quiz: Creating Great README Files for Your Python Projects

Rounding Numbers in Python

Quiz: Build a Guitar Synthesizer

Ruff: A Modern Python Linter for Error-Free and Maintainable Code

Installing Ruff

Linting Your Python Code

Checking for Errors

Quiz: Ruff: A Modern Python Linter

The Real Python Podcast – Episode #208: Detecting Outliers in Your Data With Python

Python Mappings: A Comprehensive Guide

Understanding the Main Characteristics of Python Mappings

Quiz: Python Mappings

Listing All Files in a Directory With Python

Python News: What's New From May 2024

The First Beta Version of Python 3.13 Released

The Real Python Podcast – Episode #207: Decomposing Software Problems & Avoiding the Trap of Clever Code

Python String Formatting: Available Tools and Their Features

Interpolating and Formatting Strings in Python

Using F-Strings to Format Strings

Using the Formatting Mini-Language With F-Strings

Python Interfaces: Object-Oriented Design Principles

String Interpolation in Python: Exploring Available Tools

String Interpolation in Python

Quiz: String Interpolation in Python: Exploring Available Tools

The Real Python Podcast – Episode #206: Building Python Unit Tests & Exploring a Data Visualization Gallery

What Are CRUD Operations?

In Short: CRUD Stands for Create, Read, Update, and Delete

Why Are CRUD Operations Essential?

What Are CRUD Operations in SQL?

Writing CRUD Operations in Raw SQL

Efficient Iterations With Python Iterators and Iterables

How to Create Pivot Tables With pandas

How to Create Your First Pivot Table With pandas

Quiz: How to Create Pivot Tables With pandas

The Python calendar Module: Create Calendars With Python

Displaying Calendars in Your Terminal

Creating Text-Based Calendars

Building a Python GUI Application With Tkinter

Basic Data Types in Python: A Quick Exploration

Python’s Basic Data Types

Integer Numbers

Integer Literals

The Real Python Podcast – Episode #205: Considering Accessibility & Assistive Tech as a Python Developer

Quiz: What Are CRUD Operations?

HTML and CSS Foundations for Python Developers

The Real Python Podcast – Episode #204: Querying OpenStreetMaps via API & Lazy Evaluation in Python

Flattening a List of Lists in Python

The Real Python Podcast – Episode #203: Embarking on a Relaxed and Friendly Python Coding Journey

Quiz: The Python calendar Module

Working With Global Variables in Python Functions

The Real Python Podcast – Episode #202: Pydantic Data Validation & Python Web Security Practices

Quiz: What Is the __pycache__ Folder in Python?

Python Basics Exercises: Scopes

Quiz: Python Sequences: A Comprehensive Guide

The Real Python Podcast – Episode #201: Decoupling Systems to Get Closer to the Data

Using raise for Effective Exceptions

The Real Python Podcast – Episode #200: Avoiding Error Culture and Getting Help Inside Python

Quiz: What Is the pycache Folder in Python?