Real Python

The Real Python Podcast – Episode #242: Eric Matthes: Maybe Don't Start With Unit Tests

2025-03-07T12:00:00+00:00

Should you always start testing your code with unit tests? When does it make sense to look at integration or end-to-end testing as a first step instead? This week on the show, we speak with previous guest Eric Matthes about where to begin testing your code.

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Bytes Objects: Handling Binary Data in Python

2025-03-05T14:00:00+00:00

The bytes data type is an immutable sequence of unsigned bytes used for handling binary data in Python. You can create a bytes object using the literal syntax, the bytes() function, or the bytes.fromhex() method. Since bytes are closely related to strings, you often convert between the two data types, applying the correct character encoding.

By the end of this tutorial, you’ll understand that:

Python bytes objects are immutable sequences of unsigned bytes used for handling binary data.
The difference between bytes and bytearray is that bytes objects are read-only, while bytearray objects are mutable.
You convert a Python string to bytes using the str.encode() method, the bytes() function, or the codecs module.
Endianness refers to the byte order used to represent binary data in memory, which can be either little-endian or big-endian.

This tutorial starts with a brief overview of binary data fundamentals, setting the scene for the remaining part, which delves into creating and manipulating bytes objects in Python. Along the way, it touches on related topics, such as bytearray, bytes-like objects, and the buffer protocol. To top it off, you’ll find several real-life examples and exercises at the end, which demonstrate the concepts discussed.

To get the most out of this tutorial, you should be familiar with Python basics, particularly built-in data types.

Get Your Code: Click here to download the free sample code that you’ll use to learn about bytes objects and handling binary data in Python.

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

Interactive Quiz

Python Bytes

In this quiz, you'll test your understanding of Python bytes objects. By working through this quiz, you'll revisit the key concepts related to this low-level data type.

Brushing Up on Binary Fundamentals

If you’re new to binary data or need a quick refresher, then consider sticking around. This section will provide a brief overview of binary representations, emphasizing a Python programmer’s perspective. On the other hand, if you’re already comfortable with the basics, then feel free to dive right into creating bytes objects in Python.

Bits, Bytes, and Binary Data

Virtually every piece of information, from books and music to movies, can be stored as binary data in a computer’s memory. The word binary implies that the information is stored as a sequence of binary digits, or bits for short. Each bit can hold a value of either one or zero, which is particularly well-suited for storage in electronic devices since they often use distinct voltage levels to represent these binary states.

For example, the binary sequence below may represent the color of a pixel in an image:


`1 1 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 1 0 0 1`

To make the interpretation of such binary sequences more systematic, you often arrange the individual bits into uniform groups. The standard unit of information in modern computing consists of exactly eight bits, which is why it’s sometimes known as an octet, although most people call it a byte. A single 8-bit byte allows for 256 possible bit combinations (2⁸).

With this in mind, you can break up the bit sequence above into these three bytes:


`0 0 1 1 0 0 0 0`	`1 0 0 0 1 1 0 0`	`1 1 0 0 1 0 0 1`

Notice that the leftmost byte has been padded with two leading zeros to ensure a consistent number of bits across all bytes. Together, they form a 24-bit color depth, letting you choose from more than 16 million (2²⁴) unique colors per pixel.

In this case, each byte corresponds to one of three primary colors (red, green, and blue) within the RGB color model, effectively serving as coordinates in the RGB color space. Changing their proportions can be loosely compared to mixing paints to achieve a desired hue.

Note: Strictly speaking, the RGB color model is an additive one, meaning it combines specific wavelengths of light to synthesize complex colors. In contrast, paint mixing follows a subtractive model, where pigments absorb certain wavelengths of light from the visible spectrum.

To reveal the pixel’s primary colors as decimal numbers, you can open the Python REPL and define binary literals by prefixing the corresponding bit sequences with 0b:

Python
      
>>> 0b00110000, 0b10001100, 0b11001001
(48, 140, 201)

Binary literals are an alternative way of defining integers in Python. Other types of numeric literals include hexadecimal and octal. For example, you can represent the integer 48 as 0x30 in hexadecimal or 0o60 in octal, allowing you to write the same number differently.

Having such flexibility comes in handy since it’s customary to express byte values using the hexadecimal numeral system. By rewriting each byte as a two-digit hex number, you can represent your pixel color much more compactly compared to the equivalent binary sequence:

Python
      
>>> hex(48), hex(140), hex(201)
('0x30', '0x8c', '0xc9')

>>> int("308cc9", base=16)
3181769

>>> int("001100001000110011001001", base=2)
3181769

Calling the built-in hex() function on an integer returns the corresponding hexadecimal literal as a string. When you combine the resulting hex numbers, you’re able to describe a 24-bit color with just six digits (308cc9). Go ahead and open an online color picker to see what that encoded value looks like:

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

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Working With Python Polars

2025-03-04T14:00:00+00:00

In the world of data analysis and manipulation, Python has long been the go-to language. With extensive and user-friendly libraries like NumPy, pandas, and PySpark, there’s a solution available for almost any data-driven task. Among these libraries, one name that’s been generating a significant amount of buzz lately is Polars.

Polars is a high-performance DataFrame library, designed to provide fast and efficient data processing capabilities. Inspired by the reigning pandas library, Polars takes things to another level, offering a seamless experience for working with large datasets that might not fit into memory.

In this video course, you’ll learn:

Why Polars is so performant and attention-grabbing
How to work with DataFrames, expressions, and contexts
How to read data into DataFrames
How to group and aggregate data
What the lazy API is and how to build lazy queries

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Python while Loops: Repeating Tasks Conditionally

2025-03-03T14:00:00+00:00

Python’s while loop enables you to execute a block of code repeatedly as long as a given condition remains true. Unlike for loops, which iterate a known number of times, while loops are ideal for situations where the number of iterations isn’t known upfront.

Loops are a pretty useful construct in Python, so learning how to write and use them is a great skill for you as a Python developer.

By the end of this tutorial, you’ll understand that:

while is a Python keyword used to initiate a loop that repeats a block of code as long as a condition is true.
A while loop works by evaluating a condition at the start of each iteration. If the condition is true, then the loop executes. Otherwise, it terminates.
while loops are useful when the number of iterations is unknown, such as waiting for a condition to change or continuously processing user input.
while True in Python creates an infinite loop that continues until a break statement or external interruption occurs.
Python lacks a built-in do-while loop, but you can emulate it using a while True loop with a break statement for conditional termination.

With this knowledge, you’re prepared to write effective while loops in your Python programs, handling a wide range of iteration needs.

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

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

Interactive Quiz

Python while Loops: Repeating Tasks Conditionally

In this quiz, you'll test your understanding of Python's while loop. This loop allows you to execute a block of code repeatedly as long as a given condition remains true. Understanding how to use while loops effectively is a crucial skill for any Python developer.

Getting Started With Python `while` Loops

In programming, loops are control flow statements that allow you to repeat a given set of operations a number of times. In practice, you’ll find two main types of loops:

for loops are mostly used to iterate a known number of times, which is common when you’re processing data collections with a specific number of data items.
while loops are commonly used to iterate an unknown number of times, which is useful when the number of iterations depends on a given condition.

Python has both of these loops and in this tutorial, you’ll learn about while loops. In Python, you’ll generally use while loops when you need to repeat a series of tasks an unknown number of times.

Python while loops are compound statements with a header and a code block that runs until a given condition becomes false. The basic syntax of a while loop is shown below:

Python Syntax
      
    
while condition:
    <body>

In this syntax, condition is an expression that the loop evaluates for its truth value. If the condition is true, then the loop body runs. Otherwise, the loop terminates. Note that the loop body can consist of one or more statements that must be indented properly.

Here’s a more detailed breakdown of this syntax:

while is the keyword that initiates the loop header.
condition is an expression evaluated for truthiness that defines the exit condition.
<body> consists of one or more statements to execute in each iteration.

Here’s a quick example of how you can use a while loop to iterate over a decreasing sequence of numbers:

Python
      
        
      
    
>>> number = 5

>>> while number > 0:
...     print(number)
...     number -= 1
...
5
4
3
2
1

In this example, number > 0 is the loop condition. If this condition returns a false value, the loop terminates. The body consists of a call to print() that displays the value on the screen. Next, you decrease the value of number. This change will produce a different result when the loop evaluates the condition in the next iteration.

The loop runs while the condition remains true. When the condition turns false, the loop terminates, and the program execution proceeds to the first statement after the loop body. In this example, the loop terminates when number reaches a value less than or equal to 0.

If the loop condition doesn’t become false, then you have a potentially infinite loop. Consider the following loop, and keep your fingers near the Ctrl+C key combination to terminate its execution:

Python
      
        
      
    
>>> number = 5
>>> while number != 0:
...     print(number)
...     number -= 1
...
5
4
3
2
1

>>> number = 5
>>> while number != 0:
...     print(number)
...     number -= 2
...
5
3
1
-1
-3
-5
-7
-9
-11
Traceback (most recent call last):
    ...
KeyboardInterrupt

In this example, the loop condition is number != 0. This condition works when you decrease number by 1. However, if you decrease it by 2, the condition may never become false, resulting in a potentially infinite loop. In such cases, you can usually terminate the loop by pressing Ctrl+C, which raises a KeyboardInterrupt exception on most operating systems.

Note that the while loop checks its condition first before anything else happens. If it’s false to start with, then the loop body will never run:

Python
      
        
      
    
>>> number = 0
>>> while number > 0:
...     print(number)
...     number -= 1
...

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

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Quiz: What Is the Python Global Interpreter Lock (GIL)?

2025-03-03T12:00:00+00:00

In this quiz, you’ll test your understanding of the Python Global Interpreter Lock (GIL).

The GIL allows only one thread to hold the control of the Python interpreter. This has advantages, but can also be a performance bottleneck in CPU-bound and multi-threaded code.

By working through this quiz, you’ll revisit the impact of the GIL on the performance of your Python programs and how to mitigate it.

[ 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 #241: Deciphering Python Jargon & Compiling Python 1.0

2025-02-28T12:00:00+00:00

How do you learn the terms commonly used when speaking about Python? How is the jargon similar to other programming languages? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Quiz: Python Bytes

2025-02-28T12:00:00+00:00

In this quiz, you’ll test your understanding of Bytes Objects: Handling Binary Data in Python. By working through this quiz, you’ll revisit the key concepts related to this low-level data type.

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Quiz: Python while Loops: Repeating Tasks Conditionally

2025-02-28T12:00:00+00:00

In this quiz, you’ll test your understanding of Python while Loops: Repeating Tasks Conditionally.

The while keyword is used to initiate a loop that repeats a block of code while a condition is true. A while loop works by evaluating a condition at the start of each iteration. If the condition is true, then the loop executes. Otherwise, it terminates.

[ 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 Work With Polars LazyFrames

2025-02-26T14:00:00+00:00

A Polars LazyFrame provides an efficient way to handle large datasets through lazy evaluation. Unlike traditional DataFrames, LazyFrames don’t contain data but instead store a set of instructions known as a query plan. Query plans perform operations like predicate and projection pushdown, ensuring only necessary rows and columns are processed. LazyFrames also support the parallel execution of query plans, further enhancing performance.

By the end of this tutorial, you’ll understand that:

A Polars LazyFrame allows efficient data processing by storing query instructions instead of data.
Lazy evaluation in LazyFrames optimizes query plans before data materialization.
Predicate and projection pushdown minimize unnecessary data processing in LazyFrames.
You create a LazyFrame using functions like scan_parquet() or scan_csv().
Switching between lazy and eager modes is sometimes necessary for certain operations.

Dive into this tutorial to discover how LazyFrames can transform your data processing tasks, providing both efficiency and flexibility for managing large datasets.

Before you start your learning journey, you should already be comfortable with the basics of working with DataFrames. This could be from any previous Polars experience you have, or from using any other DataFrame library, such as pandas.

In addition, you may consider using Jupyter Notebook as you work through many of the examples in this tutorial. Alternatively, JupyterLab will enhance your notebook experience, but any Python environment you’re comfortable with will be fine.

To get started, you’ll need some data. For the main part of this tutorial, you’ll use the rides.parquet file included in the downloadable materials. You can download this by clicking the link below:

Get Your Code: Click here to download the free sample code that shows you how work with Polars LazyFrames.

The rides.parquet file is a doctored version of the taxi ride data freely available on the New York City Taxi and Limousine Commission (TLC) website. The dataset contains edited information about New York taxi cab rides from July 2024. Before you go any further, you’ll need to download the file and place it in your project folder.

Note: The Parquet format is a format for storing large volumes of data efficiently. Parquet files use compression to minimize storage space. They also maintain metadata about each column allowing columns to be searched efficiently, often in parallel, and without the need to read the entire file. Because this metadata is useful to LazyFrames when they need to investigate a file’s content, Parquet is an excellent format for LazyFrames to use.

The table below shows details of the rides.parquet file’s columns, along with their Polars data types. The text in parentheses beside each data type shows how these types are annotated in a DataFrame header when Polars displays its results:

Column Name	Polars Data Type	Description
`pick_up`	`String (str)`	Pick-up borough
`drop_off`	`String (str)`	Drop-off borough
`passengers`	`Int32 (i32)`	Number of passengers
`distance`	`Int32 (i32)`	Trip distance (miles)
`fare`	`Int32 (i32)`	Total fare ($)

As a starting point, you’ll create a LazyFrame and take a first look at its data. To use Polars, you first need to install the Polars library into your Python environment. You might also like to install Matplotlib as well. You’ll use this to view the inner workings of a LazyFrame graphically later on. To install both from a command prompt you use:

In a Windows environment use:

Windows PowerShell
      
PS> python -m pip install polars matplotlib

Both libraries will install.

In a Linux or macOS environment use:

Shell
      
$ python -m pip install polars matplotlib

Both libraries will install.

In a Jupyter Notebook, the command is !python -m pip install polars matplotlib.

You can then begin to use the Polars library and all of its cool features. Here’s what your data looks like, and here’s how to create your first LazyFrame:

Python
      
        
      
    
>>> import polars as pl

>>> rides = pl.scan_parquet("rides.parquet")

>>> rides.collect()
shape: (3_076_903, 5)
┌───────────┬───────────┬────────────┬──────────┬──────┐
│ pick_up   ┆ drop_off  ┆ passengers ┆ distance ┆ fare │
│ ---       ┆ ---       ┆ ---        ┆ ---      ┆ ---  │
│ str       ┆ str       ┆ i32        ┆ i32      ┆ i32  │
╞═══════════╪═══════════╪════════════╪══════════╪══════╡
│ Manhattan ┆ Manhattan ┆ 1          ┆ 3        ┆ 24   │
│ Queens    ┆ Manhattan ┆ 1          ┆ 19       ┆ 75   │
│ Manhattan ┆ Queens    ┆ 1          ┆ 1        ┆ 16   │
│ Queens    ┆ Manhattan ┆ 0          ┆ 9        ┆ 60   │
│ Queens    ┆ Manhattan ┆ 1          ┆ 17       ┆ 90   │
│ …         ┆ …         ┆ …          ┆ …        ┆ …    │
│ Manhattan ┆ Manhattan ┆ null       ┆ 5        ┆ 27   │
│ Manhattan ┆ Manhattan ┆ null       ┆ 4        ┆ 26   │
│ Queens    ┆ Brooklyn  ┆ null       ┆ 4        ┆ 26   │
│ Manhattan ┆ Manhattan ┆ null       ┆ 3        ┆ 24   │
│ Manhattan ┆ Manhattan ┆ null       ┆ 8        ┆ 35   │
└───────────┴───────────┴────────────┴──────────┴──────┘

First of all, you import the Polars library using the conventional pl alias. You then use the scan_parquet() function to read rides.parquet. This makes your LazyFrame aware of the data file’s content. A LazyFrame doesn’t contain data but instead contains a set of instructions detailing what processing is to be carried out. To access the data, you need to materialize your LazyFrame by calling its .collect() method. This creates a DataFrame and reads the data.

In this example, .collect() shows there are 3,076,903 rows and five columns of data, as indicated by its shape.

Using LazyFrames may seem like a strange way of working given that you have to materialize them into DataFrames to view the data. You might wonder why not just stick with DataFrames instead. As you’ll see later, despite their name, LazyFrames offer an extremely efficient way to work with data. With their lazy evaluation capabilities, LazyFrames should be your preferred way to work with data in Polars whenever possible.

Next, you’ll learn the main ways you can create LazyFrames.

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

Interactive Quiz

How to Work With Polars LazyFrames

This quiz will challenge your knowledge of working with Polars LazyFrames. You won't find all the answers in the tutorial, so you'll need to do some extra investigating. By finding all the answers, you're sure to learn some interesting things along the way.

How to Create a Polars LazyFrame

You can create LazyFrames in three main ways:

Read the full article at https://realpython.com/polars-lazyframe/ »

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Quiz: How to Work With Polars LazyFrames

2025-02-26T12:00:00+00:00

In this quiz, you’ll test your understanding of the techniques covered in How to Work With Polars LazyFrames.

By working through the questions, you’ll review your understanding of why LazyFrames are an efficient and preferred way of working in Polars.

You’ll need to do some research outside of the tutorial to answer all the questions, so let this challenge take you on a learning journey.

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

Single and Double Underscore Naming Conventions in Python

2025-02-25T14:00:00+00:00

Python has a few important naming conventions that are based on using either a single or double underscore character (_). These conventions allow you to differentiate between public and non-public names in APIs, write safe classes for subclassing purposes, avoid name clashes, and more.

Following and respecting these conventions allows you to write code that looks Pythonic and consistent in the eyes of other Python developers. This skill is especially useful when you’re writing code that’s intended for other developers to work with.

In this video course, you’ll:

Learn about Python naming conventions that rely on using underscores (_)
Differentiate public and non-public names by using a single leading underscore
Use double leading underscores to leverage name mangling in Python classes
Explore other common uses of underscores in Python names

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How to Use sorted() and .sort() in Python

2025-02-24T14:00:00+00:00

Sorting in Python is a fundamental task that you can accomplish using sorted() and .sort(). The sorted() function returns a new sorted list from the elements of any iterable, without modifying the original iterable. On the other hand, the .sort() method modifies a list in place and doesn’t return a value. Both methods support customization through optional keyword arguments like key and reverse.

By the end of this tutorial, you’ll understand that:

You can sort any iterable with the sorted() function.
The sorted() function returns a new sorted list.
The .sort() method sorts the list in place.
You sort items in descending order by setting the reverse argument to True.
The key argument accepts a function to customize the sort order.

In this tutorial, you’ll learn how to sort various types of data in different data structures, customize the order, and work with two different ways of sorting in Python. You’ll need a basic understanding of lists and tuples as well as sets. These are the data structures you’ll be using to perform some basic operations.

Get Your Cheat Sheet: Click here to download a free cheat sheet that summarizes how to use sorted() and .sort() in Python.

Take the Quiz: Test your knowledge with our interactive “How to Use sorted() and .sort() in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Use sorted() and .sort() in Python

In this quiz, you'll test your understanding of sorting in Python using sorted() and .sort(). You'll revisit how to sort various types of data in different data structures, customize the order, and work with two different ways of sorting in Python.

Ordering Values With `sorted()`

In Python, you can sort iterables with the sorted() built-in function. To get started, you’ll work with iterables that contain only one data type.

Sorting Numbers

You can use sorted() to sort a list in Python. In this example, a list of integers is defined, and then sorted() is called with the numbers variable as the argument:

Python
      
        
      
    
>>> numbers = [6, 9, 3, 1]
>>> sorted(numbers)
[1, 3, 6, 9]
>>> numbers
[6, 9, 3, 1]

The output from this code is a new, sorted list. When the original variable is printed, the initial values are unchanged.

This example shows four important characteristics of sorted():

You don’t have to define the sorted() function. It’s a built-in function that’s available in any standard installation of Python.
You’re ordering the values in numbers from smallest to largest when you call sorted(numbers). When you pass no additional arguments or parameters, sorted() orders the values in numbers in ascending order.
You don’t change the original numbers variable because sorted() provides sorted output and doesn’t update the original value in place.
You get an ordered list as a return value when you call sorted().

These points mean that sorted() can be used on a list, and the output can immediately be assigned to a variable:

Python
      
        
      
    
>>> numbers = [6, 9, 3, 1]
>>> numbers_sorted = sorted(numbers)
>>> numbers_sorted
[1, 3, 6, 9]
>>> numbers
[6, 9, 3, 1]

In this example, a new variable called numbers_sorted now stores the output of the sorted() function.

You can confirm all of these observations by calling help() on sorted():

Python
      
>>> help(sorted)
Help on built-in function sorted in module builtins:

sorted(iterable, /, *, key=None, reverse=False)
    Return a new list containing all items from the iterable in ascending order.

    A custom key function can be supplied to customize the sort order, and the
    reverse flag can be set to request the result in descending order.

You’ll cover the optional arguments key and reverse later in the tutorial.

The first parameter of sorted() is an iterable. That means that you can use sorted() on tuples and sets very similarly:

Python
      
>>> numbers_tuple = (6, 9, 3, 1)
>>> sorted(numbers_tuple)
[1, 3, 6, 9]

>>> numbers_set = {5, 10, 1, 0}
>>> sorted(numbers_set)
[0, 1, 5, 10]

Notice how even though the input was a set and a tuple, the output is a list because sorted() returns a new list by definition. The returned object can be cast to a new type if it needs to match the input type. Be careful if attempting to cast the resulting list back to a set, as a set by definition is unordered:

Python
      
        
      
    
>>> numbers_tuple = (6, 9, 3, 1)
>>> numbers_set = {5, 10, 1, 0}
>>> numbers_tuple_sorted = sorted(numbers_tuple)
>>> numbers_set_sorted = sorted(numbers_set)
>>> numbers_tuple_sorted
[1, 3, 6, 9]
>>> numbers_set_sorted
[0, 1, 5, 10]
>>> tuple(numbers_tuple_sorted)
(1, 3, 6, 9)
>>> set(numbers_set_sorted)
{0, 1, 10, 5}

When you cast the numbers_set_sorted value to a set, it’s unordered, as expected. If you’re curious about how sets work in Python, then you can check out the tutorial Sets in Python.

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

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Quiz: How to Use sorted() and .sort() in Python

2025-02-24T12:00:00+00:00

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

By working through this quiz, you’ll revisit how to sort various types of data in different data structures, customize the order, and work with two different ways of sorting 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 #240: Telling Effective Stories With Your Python Visualizations

2025-02-21T12:00:00+00:00

How do you make compelling visualizations that best convey the story of your data? What methods can you employ within popular Python tools to improve your plots and graphs? This week on the show, Matt Harrison returns to discuss his new book "Effective Visualization: Exploiting Matplotlib & Pandas."

[ 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 Manage Python Projects With pyproject.toml

2025-02-19T14:00:00+00:00

The pyproject.toml file simplifies Python project configuration by unifying package setup, managing dependencies, and streamlining builds. In this tutorial, you’ll learn how it can improve your day-to-day Python setup by exploring its key use cases, like configuring your build system, installing packages locally, handling dependencies, and publishing to PyPI.

By the end of this tutorial, you’ll understand that:

pyproject.toml is a key component for defining a Python project’s build system, specifying requirements and the build backend.
Dependencies and optional dependencies can be managed directly within the pyproject.toml file or combined with the traditional requirements.txt.
Scripts for command-line execution are defined in the [project.scripts] section, allowing you to automate common tasks.
Dynamic metadata in pyproject.toml enables flexible project configuration, with attributes like version being resolved at build time.
The Python packaging ecosystem includes various tools that leverage pyproject.toml for project management, enhancing collaboration, flexibility, and configurability.

To get the most out of this tutorial, you should be familiar with the basics of Python. You should know how to import modules and install packages with pip. You should also be able to navigate the terminal and understand how to create virtual environments.

The pyproject.toml package configuration file is the relatively new (circa 2016) standard in the Python ecosystem, intended to unify package configuration. It’s also supported by many major tools for managing your Python projects. Some of the project management tools that support the pyproject.toml file are pip, Setuptools, Poetry, Flit, pip-tools, Hatch, PDM, and uv.

The pyproject.toml file is a configuration file written in the TOML syntax. For many Python project management needs, a minimal pyproject.toml file doesn’t have to contain a lot of information:

TOMLpyproject.toml
      
    
[project]
name = "a-sample-project"
version = "1.0.0"

Different tools have different requirements, but the name and version of your project are officially the only required fields in the [project] table. Typically, you’ll want to include more fields, but if you only want to include a minimal pyproject.toml file, then that’s all you’ll need to get started. Just include this file at the root of your project.

To understand more about why using a pyproject.toml file may be useful, you’ll explore a sample CLI application to show you how the pyproject.toml file fits into a standard project workflow.

Get Your Code: Click here to download the free sample code you’ll use to learn how to manage Python projects with pyproject.toml.

Setting Up a Python Project With `pyproject.toml`

The example project you’ll work with in this tutorial is inspired by the classic cowsay program. The example project is called snakesay and—once installed—you can run it with the ssay command:

Shell
      
        
      
    
$ ssay Hello, World!

 _______________
( Hello, World! )
 ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
  \
   \    ___
    \  (o o)
        \_/ \
         λ \ \
           _\ \_
          (_____)_
         (________)=Oo°

As you can see, the program takes a string argument and echoes it back with a bit of ASCII art.

The structure of the example project follows a popular pattern for Python projects:

snakesay-project/    ← The project root
│
├── snakesay/        ← The main module of this project
│   ├── __init__.py
│   ├── __main__.py  ← The entry point to snakesay
│   └── snake.py     ← The core of the program
│
├── .gitignore
├── LICENSE
├── pyproject.toml   ← What this tutorial is about
└── README.md

The directory snakesay-project is the root location of your project. The main package, where most of the code goes, is the snakesay directory.

Note: A popular and often recommended project layout is the src layout:

snakesay-project/
│
├── src/
│   └── snakesay/
│       ├── __init__.py
│       ├── __main__.py
│       └── snake.py
...

This layout has two key advantages: it makes the location of the source code more explicit and helps avoid some of the issues that can arise with more complex configurations, especially with testing.

At the root level of the project, you’ve got the star of this tutorial, the pyproject.toml file. In this project, the pyproject.toml file currently contains the following content:

TOMLpyproject.toml
      
    
[build-system]
requires = ["setuptools"]
build-backend = "setuptools.build_meta"

[project]
name = "snakesay"
version = "1.0.0"

[project.scripts]
ssay = "snakesay.__main__:main"

[tool.setuptools.packages.find]
where = ["."]

As the tutorial progresses, you’ll examine what all this means in more detail. You’ll also expand this pyproject.toml to include more tables and fields. As it stands, this pyproject.toml file includes:

The [build-system] table: Specifies what’s needed to build the project. The requires key lists the required packages, and the build-backend key defines the module used for the build process.
The [project] table: Contains essential project metadata and has plenty of optional fields, some of which you’ll explore later in this tutorial.
The [project.scripts] table: Allows you to define one or several executable commands to be able to call your application from the command line. In this case, it’s ssay, but it can be anything you like.
The [tools.setuptools.packages.find] table: Tells your build-system, Setuptools, where to find packages in your project. In this case, it’s just the root directory.

With this pyproject.toml file, you’ve already defined all the configuration you need to build and run your project.

The [tools.setuptools.packages.find] table isn’t required since the value of ["."] is the default. Even though it’s the default, sometimes Setuptools can’t find other modules in the project root, and explicitly setting the where key can help with this.

Setuptools has various defaults for package discovery, which include the current project layout and the src layout.

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

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Concatenating Strings in Python Efficiently

2025-02-18T14:00:00+00:00

Python string concatenation is a fundamental operation that combines multiple strings into a single string. In Python, you can concatenate strings using the + operator or append them with +=. For more efficient concatenation, especially when working with lists of strings, the .join() method is recommended. Other techniques include using StringIO for large datasets and the print() function for quick screen output.

By the end of this video course, you’ll understand that you can:

Concatenate strings in Python using the + and += operators.
Use += to append a string to an existing string.
Use the .join() method to combine strings in a list in Python.
Handle a stream of strings efficiently by using StringIO as a container with a file-like 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 ]

Python News Roundup: February 2025

2025-02-17T14:00:00+00:00

The new year has brought a flurry of activity to the Python community. New bugfix releases of Python 3.12 and 3.13 show that the developers seemingly never sleep. A new type of interpreter is slated for the upcoming Python 3.14 as part of ongoing efforts to improve Python’s performance.

Poetry takes a giant leap toward compatibilty with other project management tools with the release of version 2. If you’re interested in challenging yourself with some programming puzzles, check out the new season of Coding Quest.

Time to jump in! Enjoy this tour of what’s happening in the world of Python!

Poetry Version 2 Adds Compatibility

Poetry is a trusted and powerful project and dependency manager for Python. Initially created by Sébastien Eustace in 2018, it reached its Version 1 milestone in 2019. Since then, it has grown to be one of the most commonly used tools for managing Python projects.

On January 5, 2025, the Poetry team announced the release of Poetry 2.0.0. This major release comes with many updates. One of the most requested changes is compatibility with PEP 621, which describes how to specify project metadata in pyproject.toml.

Most of the common tools for project management, including setuptools, uv, Hatch, Flit, and PDM, use pyproject.toml and the project table in a consistent way, as defined in PEP 621. With Poetry on board as well, you can more simply migrate your project from one tool to another.

This improved compatibility with the rest of the Python eco-system comes with a price. There are a few breaking changes in Poetry 2 compared to earlier versions. If you’re already using Poetry, you should take care when updating to the latest version.

The changelog describes all changes, and you can read the documentation for advice on how to migrate your existing projects to the new style of configuration.

The Python Team Releases Bugfix Versions for 3.12 and 3.13

Read the full article at https://realpython.com/python-news-february-2025/ »

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The Real Python Podcast – Episode #239: Behavior-Driven vs Test-Driven Development & Using Regex in Python

2025-02-14T12:00:00+00:00

What is behavior-driven development, and how does it work alongside test-driven development? How do you communicate requirements between teams in an organization? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Python Keywords: An Introduction

2025-02-12T14:00:00+00:00

Python keywords are reserved words with specific functions and restrictions in the language. Currently, Python has thirty-five keywords and four soft keywords. These keywords are always available in Python, which means you don’t need to import them. Understanding how to use them correctly is fundamental for building Python programs.

By the end of this tutorial, you’ll understand that:

There are 35 keywords and four soft keywords in Python.
You can get a list of all keywords using keyword.kwlist from the keyword module.
Soft keywords in Python act as keywords only in specific contexts.
print and exec are keywords that have been deprecated and turned into functions in Python 3.

In this article, you’ll find a basic introduction to all Python keywords and soft keywords along with other resources that will be helpful for learning more about each keyword.

Get Your Cheat Sheet: Click here to download a free cheat sheet that summarizes the main keywords in Python.

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

Interactive Quiz

Python Keywords: An Introduction

In this quiz, you'll test your understanding of Python keywords and soft keywords. These reserved words have specific functions and restrictions in Python, and understanding how to use them correctly is fundamental for building Python programs.

Python Keywords

Python keywords are special reserved words that have specific meanings and purposes and can’t be used for anything but those specific purposes. These keywords are always available—you’ll never have to import them into your code.

Python keywords are different from Python’s built-in functions and types. The built-in functions and types are also always available, but they aren’t as restrictive as the keywords in their usage.

An example of something you can’t do with Python keywords is assign something to them. If you try, then you’ll get a SyntaxError. You won’t get a SyntaxError if you try to assign something to a built-in function or type, but it still isn’t a good idea. For a more in-depth explanation of ways keywords can be misused, check out Invalid Syntax in Python: Common Reasons for SyntaxError.

There are thirty-five keywords in Python. Here’s a list of them, each linked to its relevant section in this tutorial:

Two keywords have additional uses beyond their initial use cases. The else keyword is also used with loops and with try and except in addition to in conditional statements. The as keyword is most commonly used in import statements, but also used with the with keyword.

The list of Python keywords and soft keywords has changed over time. For example, the await and async keywords weren’t added until Python 3.7. Also, both print and exec were keywords in Python 2.7 but were turned into built-in functions in Python 3 and no longer appear in the keywords list.

Python Soft Keywords

As mentioned above, you’ll get an error if you try to assign something to a Python keyword. Soft keywords, on the other hand, aren’t that strict. They syntactically act as keywords only in certain conditions.

This new capability was made possible thanks to the introduction of the PEG parser in Python 3.9, which changed how the interpreter reads the source code.

Leveraging the PEG parser allowed for the introduction of structural pattern matching in Python. In order to use intuitive syntax, the authors picked match, case, and _ for the pattern matching statements. Notably, match and case are widely used for this purpose in many other programming languages.

To prevent conflicts with existing Python code that already used match, case, and _ as variable or function names, Python developers decided to introduce the concept of soft keywords.

Currently, there are four soft keywords in Python:

match

case

_

type

You can use the links above to jump to the soft keywords you’d like to read about, or you can continue reading for a guided tour.

Value Keywords: `True`, `False`, `None`

There are three Python keywords that are used as values. These values are singleton values that can be used over and over again and always reference the exact same object. You’ll most likely see and use these values a lot.

There are a few terms used in the sections below that may be new to you. They’re defined here, and you should be aware of their meaning before proceeding:

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

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Quiz: Python Keywords: An Introduction

2025-02-12T12:00:00+00:00

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

Python keywords are reserved words with specific functions and restrictions in the language. These keywords are always available in Python, which means you don’t need to import them. Understanding how to use them correctly is fundamental for building Python programs.

[ 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 Command-Line To-Do App With Typer

2025-02-11T14:00:00+00:00

Building an application to manage your to-do list can be an interesting project when you’re learning a new programming language or trying to take your skills to the next level. In this video course, you’ll build a functional to-do application for the command line using Python and Typer, which is a relatively young library for creating powerful command-line interface (CLI) applications in almost no time.

With a project like this, you’ll apply a wide set of core programming skills while building a real-world application with real features and requirements.

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

Build a functional to-do application with a Typer CLI in Python
Use Typer to add commands, arguments, and options to your to-do app
Test your Python to-do application with Typer’s CliRunner and pytest

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How to Join Strings in Python

2025-02-10T14:00:00+00:00

Python’s built-in string method .join() lets you combine string elements from an iterable into a single string, using a separator that you specify. You call .join() on the separator, passing the iterable of strings to join.

By the end of this tutorial, you’ll understand that:

You use .join() in Python to combine string elements with a specified separator.
A separator is the piece of text you want inserted between each substring.
To join list elements, you call .join() on a separator string, passing the list as the argument.
.join() inserts the separator between each list element to form a single string.
The .join() method returns a new string that is the concatenation of the elements in the iterable, separated by the specified string.
For smaller string concatenation tasks, you can use the concatenation operator (+) or f-strings instead of .join().

Python’s built-in str.join() method gives you a quick and reliable way to combine multiple strings into a single string. Whether you need to format output or assemble data for storage, .join() provides a clean and efficient approach for joining strings from an iterable.

In the upcoming sections, you’ll learn the basic usage of .join() to concatenate strings effectively. You’ll then apply that knowledge to real-world scenarios, from building CSV files to constructing custom log outputs. You’ll also discover some surprising pitfalls and learn how to avoid them.

Get Your Code: Click here to download the free sample code that shows you how to join strings in Python.

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

Interactive Quiz

How to Join Strings in Python

Test your understanding of Python's .join() string method for combining strings, handling edge cases, and optimizing performance.

How to Join Strings in Python Using `.join()`

To use the string method .join(), you call .join() on a separator string and pass an iterable of other strings as the argument. The method returns a single string, where it has inserted the separator string between each element of the iterable:

Python
      
>>> words = ["Time", "flies", "like", "an", "arrow!"]
>>> " ".join(words)
'Time flies like an arrow!'

In this example, you joined a list of words into one sentence, separated by spaces.

At first glance, this usage might look a little backward. In many other string operations, you call the method on the main string that you want to manipulate. However, with .join(), you call the method on the separator string, then pass the iterable of strings that you want to combine:

Python
      
>>> separator = " "
>>> separator.join(words)
'Time flies like an arrow!'

This example achieves the same result as the earlier one but splits the process into two steps. Defining separator separately makes the code more readable and avoids the potentially odd-looking syntax of calling .join() directly on a short string literal.

Note: Remember that .join() is a string method, which means that you’ll need to call it on a single string object. Keeping that in mind may help you remember why you need to call it on the separator string.

You rarely see code that’s written in multiple steps where you assign the separator string to a variable, like you did in the example above.

In typical usage, you call .join() directly on the separator string, all in one line. This approach is more concise and highlights that any valid string can be your separator, whether it’s whitespace, a dash, or a multicharacter substring.

Join With an Empty String

What if you don’t want any separator at all, but just want to concatenate the items? One valid approach is to use an empty string ("") as the separator:

Python
      
>>> letters = ["A", "B", "C", "D"]
>>> "".join(letters)
'ABCD'

This code snippet concatenates the letters in the list, forming a single string "ABCD". Using an empty string as the separator is a great way to assemble strings without a delimiter between them.

Combine Strings of Characters

Since .join() can take any iterable of strings—not just lists—you can even pass a string as an argument. Because strings are iterable, Python iterates over each character in that string, considering each character as a separate element:

Python
      
>>> characters = "ABCD"
>>> ",".join(characters)
'A,B,C,D'

By calling .join() on "," and passing the string characters, you effectively place a comma between every single character in "ABCD". This might not always be what you intend, but it’s a neat trick to keep in mind if you ever need to treat each character as a separate element.

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

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The Real Python Podcast – Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv

2025-02-07T12:00:00+00:00

Are you looking for fast tools to lint your code and manage your projects? How is the Rust programming language being used to speed up Python tools? This week on the show, we speak with Charlie Marsh about his company, Astral, and their tools, uv and Ruff.

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Quiz: Python "for" Loops: The Pythonic Way

2025-02-06T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s for loop.

By working through this quiz, you’ll revisit how to iterate over items in a data collection, how to use range() for a predefined number of iterations, and how to use enumerate() for index-based iteration.

[ 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: How to Join Strings in Python

2025-02-06T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Join Strings in Python.

By working through this quiz, you’ll review how to use .join() to combine strings with a specified separator and handle non-string data in iterables. You’ll also revisit how .join() compares to other concatenation methods and how it’s optimized in CPython.

[ 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 Split a String in Python

2025-02-05T14:00:00+00:00

Python’s .split() method lets you divide a string into a list of substrings based on a specified delimiter. By default, .split() separates at whitespace, including spaces, tabs, and newlines. You can customize .split() to work with specific delimiters using the sep parameter, and control the amount of splits with maxsplit.

By the end of this tutorial, you’ll understand that:

You split a string by spaces in Python using .split() without arguments.
Python’s .split() method can split on custom delimiters when you pass a character or string as an argument.
You limit splits using maxsplit to control the number of substrings Python extracts.
.splitlines() splits multiline strings into individual lines, excluding or including line breaks with the keepends parameter.
re.split() uses regular expressions for splitting strings based on complex patterns.

Exploring these methods will level up your text-processing capabilities and enable you to confidently tackle real-world data parsing challenges.

Get Your Code: Click here to download the free sample code that shows you how to split strings in Python.

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

Interactive Quiz

How to Split a String in Python

In this quiz, you'll test your understanding of Python's .split() method. This method is useful for text-processing and data parsing tasks, allowing you to divide a string into a list of substrings based on a specified delimiter.

How to Split a String in Python Using `.split()`

The .split() method in Python is a versatile tool that allows you to divide a string into a list of substrings based on a specified delimiter. By default, .split() separates a string at each occurrence of whitespace, which includes spaces, tabs, and newlines. This makes it particularly handy when dealing with plain text where words are separated by spaces:

Python
      
>>> sentence = "Python is a versatile language."
>>> sentence.split()
['Python', 'is', 'a', 'versatile', 'language.']

As you can see, .split() divides the sentence into individual words, removing the spaces in the process. It’s important to note that .split() also strips newlines by default, which might not be immediately obvious:

Python
      
>>> text = """Line one
... Line two
... Line three"""

>>> text.split()
['Line', 'one', 'Line', 'two', 'Line', 'three']

The .split() method treats the newline characters as whitespace, and splits the text into individual words rather than lines. This is a crucial point to remember when working with multiline strings.

The simplicity of .split() makes it a powerful tool for string manipulation. Whether you’re processing text files or parsing user input, using .split() can streamline your workflow.

When you’re faced with the task of cleaning up messy text, you may want to pair .split() with .strip(). You can learn more about using str.strip().split() by expanding the collapsible section below:

You may encounter code that uses .split() in combination with another string method, .strip():

Python
      
>>> text = "    Hello   world  "
>>> text.strip().split()
['Hello', 'world']

In this example, you’re dealing with text that contains whitespace characters at the beginning and end of text. You may see results like this when working with data that you received through web scraping, copy-paste artifacts from PDFs, or even regular user input.

Combining .strip() and .split() is a common and straightforward way to break potentially messy text into manageable tokens.

The idea is that you first remove superfluous whitespace characters from the beginning and end of the string using .strip(). This operation returns a new string object without that whitespace. You then chain .split() to this cleaned output, allowing it to break the text into elements—in this case, individual words.

If you’re only dealing with messy whitespace, however, then you don’t need to use .strip() to get to a clean result:

Python
      
>>> text = "    Hello   world  "
>>> text.split()
['Hello', 'world']

Because .split() splits on any whitespace and ignores extra leading and trailing whitespace, the result is the same as when you first explicitly remove leading and trailing whitespace with .strip().

Nevertheless, there may be advantages to adding .strip() into the mix:

Explicitness: You clearly communicate to other developers that you intend to apply data cleaning—and readability counts!
Future-proofing: You may later need to update your code to split on a different delimiter, in which case .split() won’t remove extra whitespace automatically. A well-designed additional step of explicitly removing whitespace with .strip() can prevent surprises.

If you’re only splitting on whitespace, then you likely won’t need to use str.strip().split(). However, due to developer habits and a preference for explicitness, you may still come across this combination of string methods in the wild.

When you use .split(), it returns a list of substrings. This means that you can iterate over the result, access individual elements using indexing, or unpack the iterable into separate variables.

For example, when you’re dealing with user input, you might want to extract specific pieces of information:

Python
      
        
      
    
>>> user_input = "Deborah Lopez 30"
>>> user_data = user_input.split()
>>> user_data
['Deborah', 'Lopez', '30']

>>> name, surname, age = user_data
>>> print(f"Welcome {name}! You're {age} years old.")
Welcome Deborah! You're 30 years old.

In this code snippet, you take a string containing a name, surname, and age, and split it into a list of three separate strings. Then, you unpack the list into three descriptive variables. Finally, you use an f-string to format the output.

Note: Extracting data pieces like this is useful when you’re dealing with structured data where you know the position of each element beforehand, and you can rely on consistent use of whitespace.

In this section, you’ve learned how to use Python’s .split() method to divide strings into smaller parts based on whitespace. This method is invaluable when working with plain text data, allowing you to extract and manipulate information. By understanding the default behavior of .split(), including its treatment of newlines, you’ll be well-equipped to handle a variety of string manipulation tasks in your Python projects.

In the next section, you’ll explore how to customize the behavior of .split() by specifying different delimiters, enabling you to tackle more complex string splitting scenarios.

Split With Different Delimiters Using `sep`

By default, .split() uses any whitespace as a delimiter to separate a string into a list of substrings. However, many real-world scenarios require splitting strings using other delimiters. This is where the sep parameter comes into play.

Imagine that you’re parsing data from a CSV (comma-separated values) file. CSV files are a popular format for storing tabular data, where each line represents a row and each value within a line is separated by a comma.

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

[ 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 ]

NumPy Techniques and Practical Examples

2025-02-04T14:00:00+00:00

The NumPy library is a Python library used for scientific computing. It provides you with a multidimensional array object for storing and analyzing data in a wide variety of ways. In this video course, you’ll see examples of some features NumPy provides that aren’t always highlighted in other tutorials.

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

Create multidimensional arrays from data stored in files
Identify and remove duplicate data from a NumPy array
Use structured NumPy arrays to reconcile the differences between datasets
Analyze and chart specific parts of hierarchical data
Create vectorized versions of your own 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 ]

Quiz: How to Split a String in Python

2025-02-04T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s .split() method.

[ 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 for Loops: The Pythonic Way

2025-02-03T14:00:00+00:00

Python’s for loop allows you to iterate over the items in a collection, such as lists, tuples, strings, and dictionaries. The for loop syntax declares a loop variable that takes each item from the collection in each iteration. This loop is ideal for repeatedly executing a block of code on each item in the collection. You can also tweak for loops further with features like break, continue, and else.

By the end of this tutorial, you’ll understand that:

Python’s for loop iterates over items in a data collection, allowing you to execute code for each item.
To iterate from 0 to 10, you use the for index in range(11): construct.
To repeat code a number of times without processing the data of an iterable, use the for _ in range(times): construct.
To do index-based iteration, you can use for index, value in enumerate(iterable): to access both index and item.

In this tutorial, you’ll gain practical knowledge of using for loops to traverse various collections and learn Pythonic looping techniques. Additionally, you’ll learn how to handle exceptions and how to use asynchronous iterations to make your Python code more robust and efficient.

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

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

Interactive Quiz

Python "for" Loops: The Pythonic Way

In this quiz, you'll test your understanding of Python's for loop. You'll revisit how to iterate over items in a data collection, how to use range() for a predefined number of iterations, and how to use enumerate() for index-based iteration.

Getting Started With the Python `for` Loop

In programming, loops are control flow statements that allow you to repeat a given set of operations a number of times. In practice, you’ll find two main types of loops:

for loops are mostly used to iterate a known number of times, which is common when you’re processing data collections with a specific number of data items.
while loops are commonly used to iterate an unknown number of times, which is useful when the number of iterations depends on a given condition.

Python has both of these loops and in this tutorial, you’ll learn about for loops. In Python, you’ll generally use for loops when you need to iterate over the items in a data collection. This type of loop lets you traverse different data collections and run a specific group of statements on or with each item in the input collection.

In Python, for loops are compound statements with a header and a code block that runs a predefined number of times. The basic syntax of a for loop is shown below:

Python Syntax
      
    
for variable in iterable:
    <body>

In this syntax, variable is the loop variable. In each iteration, this variable takes the value of the current item in iterable, which represents the data collection you need to iterate over. The loop body can consist of one or more statements that must be indented properly.

Here’s a more detailed breakdown of this syntax:

for is the keyword that initiates the loop header.
variable is a variable that holds the current item in the input iterable.
in is a keyword that connects the loop variable with the iterable.
iterable is a data collection that can be iterated over.
<body> consists of one or more statements to execute in each iteration.

Here’s a quick example of how you can use a for loop to iterate over a list:

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

>>> for color in colors:
...     print(color)
...
red
green
blue
yellow

In this example, color is the loop variable, while the colors list is the target collection. Each time through the loop, color takes on a successive item from colors. In this loop, the body consists of a call to print() that displays the value on the screen. This loop runs once for each item in the target iterable. The way the code above is written is the Pythonic way to write it.

However, what’s an iterable anyway? In Python, an iterable is an object—often a data collection—that can be iterated over. Common examples of iterables in Python include lists, tuples, strings, dictionaries, and sets, which are all built-in data types. You can also have custom classes that support iteration.

Note: Python has both iterables and iterators. Iterables support the iterable protocol consisting of the .__iter__() special method. Similarly, iterators support the iterator protocol that’s based on the .__iter__() and .__next__() special methods.

Both iterables and iterators can be iterated over. All iterators are iterables, but not all iterables are iterators. Python iterators play a fundamental role in for loops because they drive the iteration process.

A deeper discussion on iterables and iterators is beyond the scope of this tutorial. However, to learn more about them, check out the Iterators and Iterables in Python: Run Efficient Iterations tutorial.

You can also have a loop with multiple loop variables:

Python
      
        
      
    
>>> points = [(1, 4), (3, 6), (7, 3)]

>>> for x, y in points:
...     print(f"{x = } and {y = }")
...
x = 1 and y = 4
x = 3 and y = 6
x = 7 and y = 3

In this loop, you have two loop variables, x and y. Note that to use this syntax, you just need to provide a tuple of loop variables. Also, you can have as many loop variables as you need as long as you have the correct number of items to unpack into them. You’ll also find this pattern useful when iterating over dictionary items or when you need to do parallel iteration.

Sometimes, the input iterable may be empty. In that case, the loop will run its header once but won’t execute its body:

Python
      
>>> for item in []:
...     print(item)
...

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

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The Real Python Podcast – Episode #237: Testing Your Python Code Base: Unit vs. Integration

2025-01-31T12:00:00+00:00

What goes into creating automated tests for your Python code? Should you focus on testing the individual code sections or on how the entire system runs? 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 ]

Creating a Scalable Flask Web Application From Scratch

2025-01-28T14:00:00+00:00

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

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

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

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

[ 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 #236: Simon Willison: Using LLMs for Python Development

2025-01-24T12:00:00+00:00

What are the current large language model (LLM) tools you can use to develop Python? What prompting techniques and strategies produce better results? This week on the show, we speak with Simon Willison about his LLM research and his exploration of writing Python code with these rapidly evolving tools.

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Exploring Python's tuple Data Type With Examples

2025-01-21T14:00:00+00:00

In Python, a tuple is a built-in data type that allows you to create immutable sequences of values. The values or items in a tuple can be of any type. This makes tuples pretty useful in those situations where you need to store heterogeneous data, like that in a database record, for example.

Through this tutorial, you’ll dive deep into Python tuples and get a solid understanding of their key features and use cases. This knowledge will allow you to write more efficient and reliable code by taking advantage of tuples.

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

Create tuples in Python
Access the items in an existing tuple
Unpack, return, copy, and concatenate tuples
Reverse, sort, and traverse existing tuples
Explore other features and common gotchas of tuples

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Quiz: How to Deal With Missing Data in Polars

2025-01-21T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Deal With Missing Data in Polars.

By working through the questions, you’ll review your understanding of dealing with null values in Polars and also expand on what you learned in the tutorial.

You’ll need to do some research outside of the tutorial to answer all the questions. Embrace this challenge and let it take you on a learning journey.

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The Real Python Podcast – Episode #235: Principles for Considering Your Python Tooling

2025-01-17T12:00:00+00:00

What are the principles you should consider when making decisions about which Python tools to use? What anti-patterns get in the way of making the right choices for your team? 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 ]

Building Dictionary Comprehensions in Python

2025-01-14T14:00:00+00:00

Dictionary comprehensions are a concise and quick way to create, transform, and filter dictionaries in Python. They can significantly enhance your code’s conciseness and readability compared to using regular for loops to process your dictionaries.

Understanding dictionary comprehensions is crucial for you as a Python developer because they’re a Pythonic tool for dictionary manipulation and can be a valuable addition to your programming toolkit.

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

Create dictionaries using dictionary comprehensions
Transform existing dictionaries with comprehensions
Filter key-value pairs from dictionaries using conditionals
Decide when to use dictionary comprehensions

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The Real Python Podcast – Episode #234: Building New Structures for Learning Python

2025-01-10T12:00:00+00:00

What are the new ways we can teach and share our knowledge about Python? How can we improve the structure of our current offerings and build new educational resources for our audience of Python learners? This week on the show, Real Python core team members Stephen Gruppetta and Martin Breuss join us to discuss enhancements to the site and new ways to learn 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 ]

Ways to Start Interacting With Python

2025-01-07T14:00:00+00:00

There are multiple ways of interacting with Python, and each can be useful for different scenarios. You can quickly explore functionality in Python’s interactive mode using the built-in Read-Eval-Print Loop (REPL), or you can write larger applications to a script file using an editor or Integrated Development Environment (IDE).

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

Use Python interactively by typing code directly into the interpreter
Execute code contained in a script file from the command line
Work within a Python Integrated Development Environment (IDE)
Assess additional options, such as Thonny and Python’s IDLE

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The Real Python Podcast – Episode #233: PyCoder's Weekly 2024 Top Articles & Missing Gems

2025-01-03T12:00:00+00:00

PyCoder's Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2024. Christopher Trudeau is back on the show this week to help wrap it all up by sharing some highlights and uncovering a few missing gems from the pile.

[ 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 HTTP APIs With Django REST Framework

2024-12-31T14:00:00+00:00

REST is a loosely defined protocol for listing, creating, changing, and deleting data on your server over HTTP. The Django REST framework (DRF) is a toolkit built on top of the Django web framework that reduces the amount of code you need to write to create REST HTTP API interfaces.

In this course you’ll learn about:

The REST protocol
DRF Serializers and how to use them with Django objects
Using Django views and DRF ViewSet classes to create REST end-points
Multiple flavors of renderers and how to control their output
Specifying permissions and limiting who can see what data in your REST API

[ 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 ]

`False`	`await`	`else`	`import`	`pass`
`None`	`break`	`except`	`in`	`raise`
`True`	`class`	`finally`	`is`	`return`
`and`	`continue`	`for`	`lambda`	`try`
`as`	`def`	`from`	`nonlocal`	`while`
`assert`	`del`	`global`	`not`	`with`
`async`	`elif`	`if`	`or`	`yield`

Real Python

The Real Python Podcast – Episode #242: Eric Matthes: Maybe Don't Start With Unit Tests

Bytes Objects: Handling Binary Data in Python

Brushing Up on Binary Fundamentals

Bits, Bytes, and Binary Data

Working With Python Polars

Python while Loops: Repeating Tasks Conditionally

Getting Started With Python while Loops

Quiz: What Is the Python Global Interpreter Lock (GIL)?

The Real Python Podcast – Episode #241: Deciphering Python Jargon & Compiling Python 1.0

Quiz: Python Bytes

Quiz: Python while Loops: Repeating Tasks Conditionally

How to Work With Polars LazyFrames

How to Create a Polars LazyFrame

Quiz: How to Work With Polars LazyFrames

Single and Double Underscore Naming Conventions in Python

How to Use sorted() and .sort() in Python

Ordering Values With sorted()

Sorting Numbers

Quiz: How to Use sorted() and .sort() in Python

The Real Python Podcast – Episode #240: Telling Effective Stories With Your Python Visualizations

How to Manage Python Projects With pyproject.toml

Setting Up a Python Project With pyproject.toml

Concatenating Strings in Python Efficiently

Python News Roundup: February 2025

Poetry Version 2 Adds Compatibility

The Python Team Releases Bugfix Versions for 3.12 and 3.13

The Real Python Podcast – Episode #239: Behavior-Driven vs Test-Driven Development & Using Regex in Python

Python Keywords: An Introduction

Python Keywords

Python Soft Keywords

Value Keywords: True, False, None

Quiz: Python Keywords: An Introduction

Building a Python Command-Line To-Do App With Typer

How to Join Strings in Python

How to Join Strings in Python Using .join()

Join With an Empty String

Combine Strings of Characters

The Real Python Podcast – Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv

Quiz: Python "for" Loops: The Pythonic Way

Quiz: How to Join Strings in Python

How to Split a String in Python

How to Split a String in Python Using .split()

Split With Different Delimiters Using sep

NumPy Techniques and Practical Examples

Quiz: How to Split a String in Python

Python for Loops: The Pythonic Way

Getting Started With the Python for Loop

The Real Python Podcast – Episode #237: Testing Your Python Code Base: Unit vs. Integration

Creating a Scalable Flask Web Application From Scratch

The Real Python Podcast – Episode #236: Simon Willison: Using LLMs for Python Development

Exploring Python's tuple Data Type With Examples

Quiz: How to Deal With Missing Data in Polars

The Real Python Podcast – Episode #235: Principles for Considering Your Python Tooling

Building Dictionary Comprehensions in Python

The Real Python Podcast – Episode #234: Building New Structures for Learning Python

Ways to Start Interacting With Python

The Real Python Podcast – Episode #233: PyCoder's Weekly 2024 Top Articles & Missing Gems

Building HTTP APIs With Django REST Framework

Getting Started With Python `while` Loops

Ordering Values With `sorted()`

Setting Up a Python Project With `pyproject.toml`

Value Keywords: `True`, `False`, `None`

How to Join Strings in Python Using `.join()`

How to Split a String in Python Using `.split()`

Split With Different Delimiters Using `sep`

Getting Started With the Python `for` Loop