Real Python

Managing Dependencies With Python Poetry

2024-11-26T14:00:00+00:00

When your Python project relies on external packages, you need to make sure you’re using the right version of each package. After an update, a package might not work as it did before. A dependency manager like Python Poetry helps you specify, install, and resolve external packages in your projects. This way, you can be sure that you always work with the correct dependency version on every machine.

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

Create a new project using Poetry
Add Poetry to an existing project
Configure your project through pyproject.toml
Pin your project’s dependency versions
Install dependencies from a poetry.lock file
Run basic Poetry commands using the Poetry CLI

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Speed Up Your Python Program With Concurrency

2024-11-25T14:00:00+00:00

Concurrency refers to the ability of a program to manage multiple tasks at once, improving performance and responsiveness. It encompasses different models like threading, asynchronous tasks, and multiprocessing, each offering unique benefits and trade-offs. In Python, threads and asynchronous tasks facilitate concurrency on a single processor, while multiprocessing allows for true parallelism by utilizing multiple CPU cores.

Understanding concurrency is crucial for optimizing programs, especially those that are I/O-bound or CPU-bound. Efficient concurrency management can significantly enhance a program’s performance by reducing wait times and better utilizing system resources.

In this tutorial, you’ll learn how to:

Understand the different forms of concurrency in Python
Implement multi-threaded and asynchronous solutions for I/O-bound tasks
Leverage multiprocessing for CPU-bound tasks to achieve true parallelism
Choose the appropriate concurrency model based on your program’s needs

To get the most out of this tutorial, you should be familiar with Python basics, including functions and loops. A rudimentary understanding of system processes and CPU operations will also be helpful. You can download the sample code for this tutorial by clicking the link below:

Get Your Code: Click here to download the free sample code that you’ll use to learn about speeding up your Python program with concurrency.

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

Interactive Quiz

Python Concurrency

In this quiz, you'll test your understanding of Python concurrency. You'll revisit the different forms of concurrency in Python, how to implement multi-threaded and asynchronous solutions for I/O-bound tasks, and how to achieve true parallelism for CPU-bound tasks.

Exploring Concurrency in Python

In this section, you’ll get familiar with the terminology surrounding concurrency. You’ll also learn that concurrency can take different forms depending on the problem it aims to solve. Finally, you’ll discover how the different concurrency models translate to Python.

What Is Concurrency?

The dictionary definition of concurrency is simultaneous occurrence. In Python, the things that are occurring simultaneously are called by different names, including these:

Thread
Task
Process

At a high level, they all refer to a sequence of instructions that run in order. You can think of them as different trains of thought. Each one can be stopped at certain points, and the CPU or brain that’s processing them can switch to a different one. The state of each train of thought is saved so it can be restored right where it was interrupted.

You might wonder why Python uses different words for the same concept. It turns out that threads, tasks, and processes are only the same if you view them from a high-level perspective. Once you start digging into the details, you’ll find that they all represent slightly different things. You’ll see more of how they’re different as you progress through the examples.

Now, you’ll consider the simultaneous part of that definition. You have to be a little careful because, when you get down to the details, you’ll discover that only multiple system processes can enable Python to run these trains of thought at literally the same time.

In contrast, threads and asynchronous tasks always run on a single processor, which means they can only run one at a time. They just cleverly find ways to take turns to speed up the overall process. Even though they don’t run different trains of thought simultaneously, they still fall under the concept of concurrency.

Note: Threads in most other programming languages often run in parallel. To learn why Python threads can’t, check out What Is the Python Global Interpreter Lock (GIL)?

If you’re curious about even more details, then you can also read about Bypassing the GIL for Parallel Processing in Python or check out the experimental free threading introduced in Python 3.13.

The way the threads, tasks, or processes take turns differs. In a multi-threaded approach, the operating system actually knows about each thread and can interrupt it at any time to start running a different thread. This mechanism is also true for processes. It’s called preemptive multitasking since the operating system can preempt your thread or process to make the switch.

Preemptive multitasking is handy in that the code in the thread doesn’t need to do anything special to make the switch. It can also be difficult because of that at any time phrase. The context switch can happen in the middle of a single Python statement, even a trivial one like x = x + 1. This is because Python statements typically consist of several low-level bytecode instructions.

On the other hand, asynchronous tasks use cooperative multitasking. The tasks must cooperate with each other by announcing when they’re ready to be switched out without the operating system’s involvement. This means that the code in the task has to change slightly to make it happen.

The benefit of doing this extra work upfront is that you always know where your task will be swapped out, making it easier to reason about the flow of execution. A task won’t be swapped out in the middle of a Python statement unless that statement is appropriately marked. You’ll see later how this can simplify parts of your design.

What Is Parallelism?

So far, you’ve looked at concurrency that happens on a single processor. What about all of those CPU cores your cool, new laptop has? How can you make use of them in Python? The answer is to execute separate processes!

A process can be thought of as almost a completely different program, though technically, it’s usually defined as a collection of resources including memory, file handles, and things like that. One way to think about it is that each process runs in its own Python interpreter.

Because they’re different processes, each of your trains of thought in a program leveraging multiprocessing can run on a different CPU core. Running on a different core means that they can actually run at the same time, which is fabulous. There are some complications that arise from doing this, but Python does a pretty good job of smoothing them over most of the time.

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

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Python range(): Represent Numerical Ranges

2024-11-24T14:00:00+00:00

In Python, the range() function generates a sequence of numbers, often used in loops for iteration. By default, it creates numbers starting from 0 up to but not including a specified stop value. You can also reverse the sequence with reversed(). If you need to count backwards, then you can use a negative step, like range(start, stop, -1), which counts down from start to stop.

The range() function is not just about iterating over numbers. It can also be used in various programming scenarios beyond simple loops. By mastering range(), you can write more efficient and readable Python code. Explore how range() can simplify your code and when alternatives might be more appropriate.

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

A range in Python is an object representing an interval of integers, often used for looping.
The range() function can be used to generate sequences of numbers that can be converted to lists.
for i in range(5) is a loop that iterates over the numbers from 0 to 4, inclusive.
The range parameters start, stop, and step define where the sequence begins, ends, and the interval between numbers.
Ranges can go backward in Python by using a negative step value and reversed by using reversed().

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

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

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

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

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

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

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

Construct Numerical Ranges

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

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

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

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

Count From Zero

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

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

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

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

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

Count From Start to Stop

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

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

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

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

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

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Efficient String Concatenation in Python

2024-11-24T14:00:00+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 the += operator for appending. For more efficient concatenation of multiple strings, the .join() method is recommended, especially when working with strings in a list. Other techniques include using StringIO for large datasets or the print() function for quick screen outputs.

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

You can concatenate strings in Python using the + operator and the += operator.
You can use += to append a string to an existing string.
The .join() method is used to combine strings in a list in Python.
You can handle a stream of strings efficiently by using StringIO as a container with a file-like interface.

To get the most out of this tutorial, you should have a basic understanding of Python, especially its built-in string data type.

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

Doing String Concatenation With Python’s Plus Operator (`+`)

String concatenation is a pretty common operation consisting of joining two or more strings together end to end to build a final string. Perhaps the quickest way to achieve concatenation is to take two separate strings and combine them with the plus operator (+), which is known as the concatenation operator in this context:

Python
      
>>> "Hello, " + "Pythonista!"
'Hello, Pythonista!'

>>> head = "String Concatenation "
>>> tail = "is Fun in Python!"
>>> head + tail
'String Concatenation is Fun in Python!'

Using the concatenation operator to join two strings provides a quick solution for concatenating only a few strings.

For a more realistic example, say you have an output line that will print an informative message based on specific criteria. The beginning of the message might always be the same. However, the end of the message will vary depending on different criteria. In this situation, you can take advantage of the concatenation operator:

Python
      
        
      
    
>>> def age_group(age):
...     if 0 <= age <= 9:
...         result = "a Child!"
...     elif 9 < age <= 18:
...         result = "an Adolescent!"
...     elif 19 < age <= 65:
...         result = "an Adult!"
...     else:
...         result = "in your Golden Years!"
...     print("You are " + result)
...

>>> age_group(29)
You are an Adult!
>>> age_group(14)
You are an Adolescent!
>>> age_group(68)
You are in your Golden Years!

In the above example, age_group() prints a final message constructed with a common prefix and the string resulting from the conditional statement. In this type of use case, the plus operator is your best option for quick string concatenation in Python.

The concatenation operator has an augmented version that provides a shortcut for concatenating two strings together. The augmented concatenation operator (+=) has the following syntax:

Python
      
string += other_string

This expression will concatenate the content of string with the content of other_string. It’s equivalent to saying string = string + other_string.

Here’s a short example of how the augmented concatenation operator works in practice:

Python
      
        
      
    
>>> word = "Py"
>>> word += "tho"
>>> word += "nis"
>>> word += "ta"
>>> word
'Pythonista'

In this example, every augmented assignment adds a new syllable to the final word using the += operator. This concatenation technique can be useful when you have several strings in a list or any other iterable and want to concatenate them in a for loop:

Python
      
        
      
    
>>> def concatenate(iterable, sep=" "):
...     sentence = iterable[0]
...     for word in iterable[1:]:
...         sentence += (sep + word)
...     return sentence
...

>>> concatenate(["Hello,", "World!", "I", "am", "a", "Pythonista!"])
'Hello, World! I am a Pythonista!'

Inside the loop, you use the augmented concatenation operator to quickly concatenate several strings in a loop. Later you’ll learn about .join(), which is an even better way to concatenate a list of strings.

Python’s concatenation operators can only concatenate string objects. If you use them with a different data type, then you get a TypeError:

Python
      
        
      
    
>>> "The result is: " + 42
Traceback (most recent call last):
    ...
TypeError: can only concatenate str (not "int") to str

>>> "Your favorite fruits are: " + ["apple", "grape"]
Traceback (most recent call last):
    ...
TypeError: can only concatenate str (not "list") to str

The concatenation operators don’t accept operands of different types. They only concatenate strings. A work-around to this issue is to explicitly use the built-in str() function to convert the target object into its string representation before running the actual concatenation:

Python
      
>>> "The result is: " + str(42)
'The result is: 42'

By calling str() with your integer number as an argument, you’re retrieving the string representation of 42, which you can then concatenate to the initial string because both are now string objects.

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

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How to Iterate Through a Dictionary in Python

2024-11-23T14:00:00+00:00

Python offers several ways to iterate through a dictionary, such as using .items() to access key-value pairs directly and .values() to retrieve values only.

By understanding these techniques, you’ll be able to efficiently access and manipulate dictionary data. Whether you’re updating the contents of a dictionary or filtering data, this guide will equip you with the tools you need.

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

You can directly iterate over the keys of a Python dictionary using a for loop and access values with dict_object[key].
You can iterate through a Python dictionary in different ways using the dictionary methods .keys(), .values(), and .items().
You should use .items() to access key-value pairs when iterating through a Python dictionary.
The fastest way to access both keys and values when you iterate over a dictionary in Python is to use .items() with tuple unpacking.

To get the most out of this tutorial, you should have a basic understanding of Python dictionaries, know how to use Python for loops, and be familiar with comprehensions. Knowing other tools like the built-in map() and filter() functions, as well as the itertools and collections modules, is also a plus.

Get Your Code: Click here to download the sample code that shows you how to iterate through a dictionary with Python.

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

Interactive Quiz

Python Dictionary Iteration

Dictionaries are one of the most important and useful data structures in Python. Learning how to iterate through a Dictionary can help you solve a wide variety of programming problems in an efficient way. Test your understanding on how you can use them better!

Getting Started With Python Dictionaries

Dictionaries are a cornerstone of Python. Many aspects of the language are built around dictionaries. Modules, classes, objects, globals(), and locals() are all examples of how dictionaries are deeply wired into Python’s implementation.

Here’s how the Python official documentation defines a dictionary:

An associative array, where arbitrary keys are mapped to values. The keys can be any object with __hash__() and __eq__() methods. (Source)

There are a couple of points to notice in this definition:

Dictionaries map keys to values and store them in an array or collection. The key-value pairs are commonly known as items.
Dictionary keys must be of a hashable type, which means that they must have a hash value that never changes during the key’s lifetime.

Unlike sequences, which are iterables that support element access using integer indices, dictionaries are indexed by keys. This means that you can access the values stored in a dictionary using the associated key rather than an integer index.

The keys in a dictionary are much like a set, which is a collection of hashable and unique objects. Because the keys need to be hashable, you can’t use mutable objects as dictionary keys.

On the other hand, dictionary values can be of any Python type, whether they’re hashable or not. There are literally no restrictions for values. You can use anything as a value in a Python dictionary.

Note: The concepts and topics that you’ll learn about in this section and throughout this tutorial refer to the CPython implementation of Python. Other implementations, such as PyPy, IronPython, and Jython, could exhibit different dictionary behaviors and features that are beyond the scope of this tutorial.

Before Python 3.6, dictionaries were unordered data structures. This means that the order of items typically wouldn’t match the insertion order:

Python
      
>>> # Python 3.5
>>> likes = {"color": "blue", "fruit": "apple", "pet": "dog"}

>>> likes
{'color': 'blue', 'pet': 'dog', 'fruit': 'apple'}

Note how the order of items in the resulting dictionary doesn’t match the order in which you originally inserted the items.

In Python 3.6 and greater, the keys and values of a dictionary retain the same order in which you insert them into the underlying dictionary. From 3.6 onward, dictionaries are compact ordered data structures:

Python
      
>>> # Python 3.6
>>> likes = {"color": "blue", "fruit": "apple", "pet": "dog"}

>>> likes
{'color': 'blue', 'fruit': 'apple', 'pet': 'dog'}

Keeping the items in order is a pretty useful feature. However, if you work with code that supports older Python versions, then you must not rely on this feature, because it can generate buggy behaviors. With newer versions, it’s completely safe to rely on the feature.

Another important feature of dictionaries is that they’re mutable data types. This means that you can add, delete, and update their items in place as needed. It’s worth noting that this mutability also means that you can’t use a dictionary as a key in another dictionary.

Understanding How to Iterate Through a Dictionary in Python

Read the full article at https://realpython.com/iterate-through-dictionary-python/ »

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The Real Python Podcast – Episode #229: The Joy of Tinkering & Python Free-Threading Performance

2024-11-22T12:00:00+00:00

What keeps your spark alive for developing software and learning Python? Do you like to try new frameworks, build toy projects, or collaborate with other developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Quiz: Expression vs Statement in Python: What's the Difference?

2024-11-21T12:00:00+00:00

In this quiz, you’ll test your understanding of Expression vs Statement in Python: What’s the Difference?

By working through this quiz, you’ll revisit the key differences between expressions and statements in Python, and how to use them effectively in your code.

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Quiz: Interacting With Python

2024-11-21T12:00:00+00:00

In this quiz, you’ll test your understanding of the different ways you can interact with Python.

By working through this quiz, you’ll revisit key concepts related to Python interaction in interactive mode using the Read-Eval-Print Loop (REPL), through Python script files, and within Integrated Development Environments (IDEs) and code editors.

You’ll also test your knowledge of some other options that may be useful, such as Jupyter Notebooks.

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NumPy Practical Examples: Useful Techniques

2024-11-20T14:00:00+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 tutorial, you’ll see examples of some features NumPy provides that aren’t always highlighted in other tutorials. You’ll also get the chance to practice your new skills with various exercises.

In this tutorial, 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

If you’re new to NumPy, it’s a good idea to familiarize yourself with the basics of data science in Python before you start. Also, you’ll be using Matplotlib in this tutorial to create charts. While it’s not essential, getting acquainted with Matplotlib beforehand might be beneficial.

Get Your Code: Click here to download the free sample code that you’ll use to work through NumPy practical examples.

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

Interactive Quiz

NumPy Practical Examples: Useful Techniques

This quiz will test your understanding of working with NumPy arrays. 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.

Setting Up Your Working Environment

Before you can get started with this tutorial, you’ll need to do some initial setup. In addition to NumPy, you’ll need to install the Matplotlib library, which you’ll use to chart your data. You’ll also be using Python’s pathlib library to access your computer’s file system, but there’s no need to install pathlib because it’s part of Python’s standard library.

You might consider using a virtual environment to make sure your tutorial’s setup doesn’t interfere with anything in your existing Python environment.

Using a Jupyter Notebook within JupyterLab to run your code instead of a Python REPL is another useful option. It allows you to experiment and document your findings, as well as quickly view and edit files. The downloadable version of the code and exercise solutions are presented in Jupyter Notebook format.

The commands for setting things up on the common platforms are shown below:

Fire up a Windows PowerShell(Admin) or Terminal(Admin) prompt, depending on the version of Windows that you’re using. Now type in the following commands:

Windows PowerShell
      
PS> python -m venv venv\
PS> venv\Scripts\activate
(venv) PS> python -m pip install numpy matplotlib jupyterlab
(venv) PS> jupyter lab

Here you create a virtual environment named venv\, which you then activate. If the activation is successful, then the virtual environment’s name will precede your Powershell prompt. Next, you install numpy and matplotlib into this virtual environment, followed by the optional jupyterlab. Finally, you start JupyterLab.

Note: When you activate your virtual environment, you may receive an error stating that your system can’t run the script. Modern versions of Windows don’t allow you to run scripts downloaded from the Internet as a security feature.

To fix this, you need to type the command Set-ExecutionPolicy RemoteSigned, then answer Y to the question. Your computer will now run scripts that Microsoft has verified. Once you’ve done this, the venv\Scripts\activate command should work.

Fire up a terminal and type in the following commands:

Shell
      
$ python -m venv venv/
$ source venv/bin/activate
(venv) $ python -m pip install numpy matplotlib jupyterlab
(venv) $ jupyter lab

Here you create a virtual environment named venv/, which you then activate. If the activation is successful, then the virtual environment’s name will precede your command prompt. Next, you install numpy and matplotlib into this virtual environment, followed by the optional jupyterlab. Finally, you start JupyterLab.

You’ll notice that your prompt is preceded by (venv). This means that anything you do from this point forward will stay in this environment and remain separate from other Python work you have elsewhere.

Now that you have everything set up, it’s time to begin the main part of your learning journey.

NumPy Example 1: Creating Multidimensional Arrays From Files

When you create a NumPy array, you create a highly-optimized data structure. One of the reasons for this is that a NumPy array stores all of its elements in a contiguous area of memory. This memory management technique means that the data is stored in the same memory region, making access times fast. This is, of course, highly desirable, but an issue occurs when you need to expand your array.

Suppose you need to import multiple files into a multidimensional array. You could read them into separate arrays and then combine them using np.concatenate(). However, this would create a copy of your original array before expanding the copy with the additional data. The copying is necessary to ensure the updated array will still exist contiguously in memory since the original array may have had non-related content adjacent to it.

Constantly copying arrays each time you add new data from a file can make processing slow and is wasteful of your system’s memory. The problem becomes worse the more data you add to your array. Although this copying process is built into NumPy, you can minimize its effects with these two steps:

When setting up your initial array, determine how large it needs to be before populating it. You may even consider over-estimating its size to support any future data additions. Once you know these sizes, you can create your array upfront.
The second step is to populate it with the source data. This data will be slotted into your existing array without any need for it to be expanded.

Next, you’ll explore how to populate a three-dimensional NumPy array.

Populating Arrays With File Data

In this first example, you’ll use the data from three files to populate a three-dimensional array. The content of each file is shown below, and you’ll also find these files in the downloadable materials:

The first file has two rows and three columns with the following content:

CSVfile1.csv
      
    

1.1, 1.2, 1.3
1.4, 1.5, 1.6

Read the full article at https://realpython.com/numpy-example/ »

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Quiz: NumPy Practical Examples: Useful Techniques

2024-11-20T12:00:00+00:00

In this quiz, you’ll test your understanding of the techniques covered in the tutorial NumPy Practical Examples: Useful Techniques.

By working through the questions, you’ll review your understanding of NumPy arrays 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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Working With TOML and Python

2024-11-19T14:00:00+00:00

TOML—Tom’s Obvious Minimal Language—is a reasonably new configuration file format that the Python community has embraced over the last couple of years. TOML plays an essential part in the Python ecosystem. Many of your favorite tools rely on TOML for configuration, and you’ll use pyproject.toml when you build and distribute your own packages.

In this video course, you’ll learn more about TOML and how you can use it. In particular, you’ll:

Learn and understand the syntax of TOML
Use tomli and tomllib to parse TOML documents
Use tomli_w to write data structures as TOML
Use tomlkit when you need more control over your TOML 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 ]

Interacting With Python

2024-11-18T14:00:00+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 tutorial, 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 the Jupyter Notebook and online interpreters

Before working through this tutorial, make sure that you have a functioning Python installation at hand. Once you’re set up with that, it’s time to write some Python code!

Get Your Code: Click here to get the free sample code that you’ll use to learn about interacting with Python.

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

Interactive Quiz

Interacting With Python

In this quiz, you'll test your understanding of the different ways of interacting with Python. By working through this quiz, you'll revisit key concepts related to Python interaction in interactive mode using the REPL, through Python script files, and within IDEs and code editors.

Hello, World!

There’s a long-standing custom in computer programming that the first code written in a newly installed language is a short program that displays the text Hello, World! to the console.

In Python, running a “Hello, World!” program only takes a single line of code:

Python
      
print("Hello, World!")

Here, print() will display the text Hello, World! in quotes to your screen. In this tutorial, you’ll explore several ways to execute this code.

Running Python in Interactive Mode

The quickest way to start interacting with Python is in a Read-Eval-Print Loop (REPL) environment. This means starting up the interpreter and typing commands to it directly.

When you interact with Python in this way, the interpreter will:

Read the command you enter
Evaluate and execute the command
Print the output (if any) to the console
Loop back and repeat the process

The interactive session continues like this until you instruct the interpreter to stop. Using Python in this interactive mode is a great way to test short snippets of Python code and get more familiar with the language.

When you install Python using an installer, the Start menu shows a program group labeled Python 3.x. The label may vary depending on the particular installation you chose. Click on that item to start the Python interpreter.

Alternatively, you can open your Command Prompt or PowerShell application and type the py command to launch it:

Windows PowerShell

PS> py

To start the Python interpreter, open your Terminal application and type python3 to launch it from the command line:

Shell

$ python3

If you’re unfamiliar with this application, then you can use your operating system’s search function to find it.

After pressing Enter, you should see a response from the Python interpreter similar to the one below:

Python
      
Python 3.13.0 (main, Oct 14 2024, 10:34:31) [Clang 15.0.0 (clang-1500.3.9.4)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>

If you’re not seeing the >>> prompt, then you’re not talking to the Python interpreter. This could be because Python is either not installed or not in the path of your terminal window session.

Note: If you need additional help to get to this point, then you can check out the How to Install Python on Your System: A Guide tutorial.

If you’re seeing the prompt, then you’re off and running! With these next steps, you’ll execute the statement that displays "Hello, World!" to the console:

Ensure that Python displays the >>> prompt, and that you position your cursor after it.
Type the command print("Hello, World!") exactly as shown.
Press the Enter key.

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

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Using the Python zip() Function for Parallel Iteration

2024-11-17T14:00:00+00:00

Python’s zip() function combines elements from multiple iterables. Calling zip() generates an iterator that yields tuples, each containing elements from the input iterables. This function is essential for tasks like parallel iteration and dictionary creation, offering an efficient way to handle multiple sequences in Python programming.

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

zip() in Python aggregates elements from multiple iterables into tuples, facilitating parallel iteration.
dict(zip()) creates dictionaries by pairing keys and values from two sequences.
zip() is lazy in Python, meaning it returns an iterator instead of a list.
There’s no unzip() function in Python, but the same zip() function can reverse the process using the unpacking operator *.
Alternatives to zip() include itertools.zip_longest() for handling iterables of unequal lengths.

In this tutorial, you’ll explore how to use zip() for parallel iteration. You’ll also learn how to handle iterables of unequal lengths and discover the convenience of using zip() with dictionaries. Whether you’re working with lists, tuples, or other data structures, understanding zip() will enhance your coding skills and streamline your Python projects.

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Understanding the Python `zip()` Function

zip() is available in the built-in namespace. If you use dir() to inspect __builtins__, then you’ll see zip() at the end of the list:

Python
      
>>> dir(__builtins__)
['ArithmeticError', 'AssertionError', 'AttributeError', ..., 'zip']

You can see that 'zip' is the last entry in the list of available objects.

According to the official documentation, Python’s zip() function behaves as follows:

Returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables. The iterator stops when the shortest input iterable is exhausted. With a single iterable argument, it returns an iterator of 1-tuples. With no arguments, it returns an empty iterator. (Source)

You’ll unpack this definition throughout the rest of the tutorial. As you work through the code examples, you’ll see that Python zip operations work just like the physical zipper on a bag or pair of jeans. Interlocking pairs of teeth on both sides of the zipper are pulled together to close an opening. In fact, this visual analogy is perfect for understanding zip(), since the function was named after physical zippers!

Using `zip()` in Python

The signature of Python’s zip() function is zip(*iterables, strict=False). You’ll learn more about strict later. The function takes in iterables as arguments and returns an iterator. This iterator generates a series of tuples containing elements from each iterable. zip() can accept any type of iterable, such as files, lists, tuples, dictionaries, sets, and so on.

Passing `n` Arguments

If you use zip() with n arguments, then the function will return an iterator that generates tuples of length n. To see this in action, take a look at the following code block:

Python
      
        
      
    
>>> numbers = [1, 2, 3]
>>> letters = ["a", "b", "c"]
>>> zipped = zip(numbers, letters)
>>> zipped  # Holds an iterator object
<zip object at 0x7fa4831153c8>

>>> type(zipped)
<class 'zip'>

>>> list(zipped)
[(1, 'a'), (2, 'b'), (3, 'c')]

Here, you use zip(numbers, letters) to create an iterator that produces tuples of the form (x, y). In this case, the x values are taken from numbers and the y values are taken from letters. Notice how the Python zip() function returns an iterator. To retrieve the final list object, you need to use list() to consume the iterator.

If you’re working with sequences like lists, tuples, or strings, then your iterables are guaranteed to be evaluated from left to right. This means that the resulting list of tuples will take the form [(numbers[0], letters[0]), (numbers[1], letters[1]),..., (numbers[n], letters[n])]. However, for other types of iterables (like sets), you might see some weird results:

Python
      
>>> s1 = {2, 3, 1}
>>> s2 = {"b", "a", "c"}
>>> list(zip(s1, s2))
[(1, 'a'), (2, 'c'), (3, 'b')]

In this example, s1 and s2 are set objects, which don’t keep their elements in any particular order. This means that the tuples returned by zip() will have elements that are paired up randomly. If you’re going to use the Python zip() function with unordered iterables like sets, then this is something to keep in mind.

Passing No Arguments

You can call zip() with no arguments as well. In this case, you’ll simply get an empty iterator:

Python
      
>>> zipped = zip()
>>> zipped
<zip object at 0x7f196294a488>

>>> list(zipped)
[]

Here, you call zip() with no arguments, so your zipped variable holds an empty iterator. If you consume the iterator with list(), then you’ll see an empty list as well.

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

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Using the len() Function in Python

2024-11-16T14:00:00+00:00

The len() function in Python is a powerful and efficient tool used to determine the number of items in objects, such as sequences or collections. You can use len() with various data types, including strings, lists, dictionaries, and third-party types like NumPy arrays and pandas DataFrames. Understanding how len() works with different data types helps you write more efficient and concise Python code.

Using len() in Python is straightforward for built-in types, but you can extend it to your custom classes by implementing the .__len__() method. This allows you to customize what length means for your objects. For example, with pandas DataFrames, len() returns the number of rows. Mastering len() not only enhances your grasp of Python’s data structures but also empowers you to craft more robust and adaptable programs.

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

The len() function in Python returns the number of items in an object, such as strings, lists, or dictionaries.
To get the length of a string in Python, you use len() with the string as an argument, like len("example").
To find the length of a list in Python, you pass the list to len(), like len([1, 2, 3]).
The len() function operates in constant time, O(1), as it accesses a length attribute in most cases.

In this tutorial, you’ll learn when to use the len() Python function and how to use it effectively. You’ll discover which built-in data types are valid arguments for len() and which ones you can’t use. You’ll also learn how to use len() with third-party types like ndarray in NumPy and DataFrame in pandas, and with your own classes.

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

Getting Started With Python’s `len()`

The function len() is one of Python’s built-in functions. It returns the length of an object. For example, it can return the number of items in a list. You can use the function with many different data types. However, not all data types are valid arguments for len().

You can start by looking at the help for this function:

Python
      
>>> help(len)
Help on built-in function len in module builtins:
len(obj, /)
    Return the number of items in a container.

The function takes an object as an argument and returns the length of that object. The documentation for len() goes a bit further:

Return the length (the number of items) of an object. The argument may be a sequence (such as a string, bytes, tuple, list, or range) or a collection (such as a dictionary, set, or frozen set). (Source)

When you use built-in data types and many third-party types with len(), the function doesn’t need to iterate through the data structure. The length of a container object is stored as an attribute of the object. The value of this attribute is modified each time items are added to or removed from the data structure, and len() returns the value of the length attribute. This ensures that len() works efficiently.

In the following sections, you’ll learn about how to use len() with sequences and collections. You’ll also learn about some data types that you cannot use as arguments for the len() Python function.

Using `len()` With Built-in Sequences

A sequence is a container with ordered items. Lists, tuples, and strings are three of the basic built-in sequences in Python. You can find the length of a sequence by calling len():

Python
      
        
      
    
>>> greeting = "Good Day!"
>>> len(greeting)
9

>>> office_days = ["Tuesday", "Thursday", "Friday"]
>>> len(office_days)
3

>>> london_coordinates = (51.50722, -0.1275)
>>> len(london_coordinates)
2

When finding the length of the string greeting, the list office_days, and the tuple london_coordinates, you use len() in the same manner. All three data types are valid arguments for len().

The function len() always returns an integer as it’s counting the number of items in the object that you pass to it. The function returns 0 if the argument is an empty sequence:

Python
      
        
      
    
>>> len("")
0
>>> len([])
0
>>> len(())
0

In the examples above, you find the length of an empty string, an empty list, and an empty tuple. The function returns 0 in each case.

A range object is also a sequence that you can create using range(). A range object doesn’t store all the values but generates them when they’re needed. However, you can still find the length of a range object using len():

Python
      
>>> len(range(1, 20, 2))
10

This range of numbers includes the integers from 1 to 19 with increments of 2. The length of a range object can be determined from the start, stop, and step values.

In this section, you’ve used the len() Python function with strings, lists, tuples, and range objects. However, you can also use the function with any other built-in sequence.

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

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The Real Python Podcast – Episode #228: Maintaining the Foundations of Python & Cautionary Tales

2024-11-15T12:00:00+00:00

How do you build a sustainable open-source project and community? What lessons can be learned from Python's history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation.

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Quiz: Namespaces and Scope in Python

2024-11-14T12:00:00+00:00

In this quiz, you’ll test your understanding of Python Namespaces and Scope.

You’ll revisit how Python organizes symbolic names and objects in namespaces, when Python creates a new namespace, how namespaces are implemented, and how variable scope determines symbolic name visibility.

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Python Dictionary Comprehensions: How and When to Use Them

2024-11-13T14:00:00+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 tutorial, 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

To get the most out of this tutorial, you should be familiar with basic Python concepts, such as for loops, iterables, and dictionaries, as well as list comprehensions.

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

Take the Quiz: Test your knowledge with our interactive “Python Dictionary Comprehensions: How and When to Use Them” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python Dictionary Comprehensions: How and When to Use Them

In this quiz, you'll test your understanding of Python dictionary comprehensions. Dictionary comprehensions are a concise and quick way to create, transform, and filter dictionaries in Python, and can significantly enhance your code's conciseness and readability.

Creating and Transforming Dictionaries in Python

In Python programming, you’ll often need to create, populate, and transform dictionaries. To do this, you can use dictionary literals, the dict() constructor, and for loops. In the following sections, you’ll take a quick look at how to use these tools. You’ll also learn about dictionary comprehensions, which are a powerful way to manipulate dictionaries in Python.

Creating Dictionaries With Literals and `dict()`

To create new dictionaries, you can use literals. A dictionary literal is a series of key-value pairs enclosed in curly braces. The syntax of a dictionary literal is shown below:

Python Syntax
      
{key_1: value_1, key_2: value_2,..., key_N: value_N}

The keys must be hashable objects and are commonly strings. The values can be any Python object, including other dictionaries. Here’s a quick example of a dictionary:

Python
      
>>> likes = {"color": "blue", "fruit": "apple", "pet": "dog"}
>>> likes
{'color': 'blue', 'fruit': 'apple', 'pet': 'dog'}

>>> likes["hobby"] = "guitar"
>>> likes
{'color': 'blue', 'fruit': 'apple', 'pet': 'dog', 'hobby': 'guitar'}

In this example, you create dictionary key-value pairs that describe things people often like. The keys and values of your dictionary are string objects. You can add new pairs to the dictionary using the dict[key] = value syntax.

Note: To learn more about dictionaries, check out the Dictionaries in Python tutorial.

You can also create new dictionaries using the dict() constructor:

Python
      
>>> dict(apple=0.40, orange=0.35, banana=0.25)
{'apple': 0.4, 'orange': 0.35, 'banana': 0.25}

In this example, you create a new dictionary using dict() with keyword arguments. In this case, the keys are strings and the values are floating-point numbers. It’s important to note that the dict() constructor is only suitable for those cases where the dictionary keys can be strings that are valid Python identifiers.

Using `for` Loops to Populate Dictionaries

Sometimes, you need to start with an empty dictionary and populate it with key-value pairs dynamically. To do this, you can use a for loop. For example, say that you want to create a dictionary in which keys are integer numbers and values are powers of 2.

Here’s how you can do this with a for loop:

Python
      
>>> powers_of_two = {}

>>> for integer in range(1, 10):
...     powers_of_two[integer] = 2**integer
...

>>> powers_of_two
{1: 2, 2: 4, 3: 8, 4: 16, 5: 32, 6: 64, 7: 128, 8: 256, 9: 512}

In this example, you create an empty dictionary using an empty pair of curly braces. Then, you run a loop over a range of integer numbers from 1 to 9. Inside the loop, you populate the dictionary with the integer numbers as keys and powers of two as values.

The loop in this example is readable and clear. However, you can also use dictionary comprehension to create and populate a dictionary like the one shown above.

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

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Quiz: Basic Input and Output in Python

2024-11-13T12:00:00+00:00

In this quiz, you’ll test your understanding of how to use Python’s built-in functions input() and print() for basic input and output operations.

You’ll also revisit how to use readline to improve the user experience when collecting input, and how to format output using the sep and end keyword arguments of print().

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Quiz: Python Dictionary Comprehensions: How and When to Use Them

2024-11-13T12:00:00+00:00

In this quiz, you’ll test your understanding of Python Dictionary Comprehensions: How and When to Use Them.

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

[ 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 ]

Formatting Floats Inside Python F-Strings

2024-11-12T14:00:00+00:00

You’ll often need to format and round a Python float to display the results of your calculations neatly within strings. In earlier versions of Python, this was a messy thing to do because you needed to round your numbers first and then use either string concatenation or the old string formatting technique to do this for you.

Since Python 3.6, the literal string interpolation, more commonly known as a formatted string literal or f-string, allows you to customize the content of your strings in a more readable way.

An f-string is a literal string prefixed with a lowercase or uppercase letter f and contains zero or more replacement fields enclosed within a pair of curly braces {...}. Each field contains an expression that produces a value. You can calculate the field’s content, but you can also use function calls or even variables.

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Python News Roundup: November 2024

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

The latest Python developments all point to the same thing—Python is currently thriving. The recent GitHub Octoverse 2024 report has revealed that Python is now the most used language on GitHub. Also, last month saw the release of Python 3.13, which is already laying the groundwork for some exciting future improvements.

While Python core developers have been busy exploring the language’s features as they tinker with upcoming enhancements, it’s good to know that working on Python’s source code isn’t the only way you can contribute to Python’s future. Another way to shape the focus of upcoming releases is to join the Python Developers Survey 2024.

And with the end of the year in sight, you may want to venture a look at next year’s calendar and mark some dates, such as the PyCon US conference in May or the Python 3.14 release in October 2025.

Now that you know the highlights, it’s time to dive into the most important Python news for November.

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

Python’s Popularity Shines in GitHub’s Octoverse 2024

The latest Octoverse report for 2024 shows that Python remains one of the most widely used languages on GitHub, securing its place as a core language in open-source and professional development. Python ranked among the top three most-used languages, demonstrating its continued appeal across industries and experience levels:

As GitHub’s annual report illustrates, Python’s popularity is fueled by its solid role in developing machine learning and artificial intelligence frameworks.

Another takeaway from the Octoverse survey is Python’s strong community engagement. Python developers are not only active in contributing code but also in participating in discussions, filing issues, and reviewing pull requests.

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

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The Real Python Podcast – Episode #227: New PEPs: Template Strings & External Wheel Hosting

2024-11-08T12:00:00+00:00

Have you wanted the flexibility of f-strings but need safety checks in place? What if you could have deferred evaluation for logging or avoiding injection attacks? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Introduction to Web Scraping With Python

2024-11-05T14:00:00+00:00

Web scraping is the process of collecting and parsing raw data from the Web, and the Python community has come up with some pretty powerful web scraping tools.

The Internet hosts perhaps the greatest source of information on the planet. Many disciplines, such as data science, business intelligence, and investigative reporting, can benefit enormously from collecting and analyzing data from websites.

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

Parse website data using string methods and regular expressions
Parse website data using an HTML parser
Interact with forms and other website components

[ 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: Variables in Python: Usage and Best Practices

2024-11-05T12:00:00+00:00

In this quiz, you’ll test your understanding of Variables in Python: Usage and Best Practices.

By working through this quiz, you’ll revisit how to create and assign values to variables, change a variable’s data type dynamically, use variables to create expressions, counters, accumulators, and Boolean flags, follow best practices for naming variables, and create, access, and use variables in their scopes.

[ 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 #226: PySheets: Spreadsheets in the Browser Using PyScript

2024-11-01T12:00:00+00:00

What goes into building a spreadsheet application in Python that runs in the browser? How do you make it launch quickly, and where do you store the cells of data? This week on the show, we speak with Chris Laffra about his project, PySheets, and his book "Communication for Engineers."

[ 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's Magic Methods in Classes

2024-10-29T14:00:00+00:00

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

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

In this video course, you’ll:

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

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

Quiz: Using .repr() vs .str() in Python

2024-10-29T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s .__repr__() and .__str__() special methods. These methods allow you to control how a program displays an object, making your classes more readable and easier to debug and maintain.

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Quiz: Beautiful Soup: Build a Web Scraper With Python

2024-10-28T12:00:00+00:00

In this quiz, you’ll test your understanding of web scraping with Python, Requests, and Beautiful Soup.

By working through this quiz, you’ll revisit how to inspect the HTML structure of your target site with your browser’s developer tools, decipher data encoded in URLs, use Requests and Beautiful Soup for scraping and parsing internet data, and gain an understanding of what a web scraping pipeline looks like.

[ 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 #225: Python Getting Faster and Leaner & Ideas for Django Projects

2024-10-25T12:00:00+00:00

What changes are happening under the hood in the latest versions of Python? How are these updates laying the groundwork for a faster Python in the coming years? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Quiz: How to Reset a pandas DataFrame Index

2024-10-25T12:00:00+00:00

In this quiz, you’ll test your understanding of how to reset a pandas DataFrame index.

By working through the questions, you’ll review your knowledge of indexing 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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Understanding Python's Global Interpreter Lock (GIL)

2024-10-22T14:00:00+00:00

The Python Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter.

This means that only one thread can be in a state of execution at any point in time. The impact of the GIL isn’t visible to developers who execute single-threaded programs, but it can be a performance bottleneck in CPU-bound and multi-threaded code.

Since the GIL allows only one thread to execute at a time even in a multi-threaded architecture with more than one CPU core, the GIL has gained a reputation as an “infamous” feature of Python.

In this video course you’ll learn how the GIL affects the performance of your Python programs, and how you can mitigate the impact it might have on your code.

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The Real Python Podcast – Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries

2024-10-18T12:00:00+00:00

How does a Python tool support all types of DataFrames and their various features? Could a lightweight library be used to add compatibility for newer formats like Polars or PyArrow? This week on the show, we speak with Marco Gorelli about his project, Narwhals.

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Using Type Hints for Multiple Return Types in Python

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

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

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

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

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The Real Python Podcast – Episode #223: Exploring the New Features of Python 3.13

2024-10-11T12:00:00+00:00

Python 3.13 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. This year, Geir Arne coordinated a series of preview articles with members of the Real Python team and a showcase tutorial, "Python 3.13: Cool New Features for You to Try." Christopher's video course "What's New in Python 3.13" covers the topics from the article and shows the new features in action.

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What's New in Python 3.13

2024-10-08T14:00:00+00:00

Python 3.13 was published on October 7, 2024. This new version is a major step forward for the language, although several of the biggest changes are happening under the hood and won’t be immediately visible to you.

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

In this video course, you’ll learn about some of the improvements in the new version, including:

Improvements made to the interactive interpreter (REPL)
Clearer error messages that can help you fix common mistakes
Advancements done in removing the global interpreter lock (GIL) and making Python free-threaded
The implementation of an experimental Just-In-Time (JIT) compiler
A host of minor upgrades to Python’s static type system

In this video course, you’ll explore these changes and see how this new version of Python can work for you.

If you want to try any of the examples in this video course, then you’ll need to use Python 3.13. The Python 3 Installation & Setup Guide and How Can You Install a Pre-Release Version of Python? walk you through several options for adding a new version of Python to your system.

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Differences Between Python's Mutable and Immutable Types

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

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

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

In this video course, you’ll:

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

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

The Real Python Podcast – Episode #222: Using Virtual Environments in Docker & Comparing Python Dev Tools

2024-09-27T12:00:00+00:00

Should you use a Python virtual environment in a Docker container? What are the advantages of using the same development practices locally and inside a container? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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

Advanced Python import Techniques

2024-09-24T14:00:00+00:00

In Python, you use the import keyword to make code in one module available in another. Imports in Python are important for structuring your code effectively. Using imports properly will make you more productive, allowing you to reuse code while keeping your projects maintainable.

This video course provides a comprehensive overview of Python’s import statement and how it works. The import system is powerful, and this course will teach you how to harness this power. While you’ll cover many of the concepts behind Python’s import system, this video course is mostly example driven, so you’ll learn from the numerous code examples shared throughout.

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

Use modules, packages, and namespace packages
Manage namespaces and avoid shadowing
Avoid circular imports
Import modules dynamically at runtime
Customize Python’s import system

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

The Real Python Podcast – Episode #221: Thriving as a Developer With ADHD

2024-09-20T12:00:00+00:00

What are strategies for being a productive developer with ADHD? How can you help your team members with ADHD to succeed and complete projects? This week on the show, we speak with Chris Ferdinandi about his website and podcast "ADHD For the Win!"

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

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

2024-09-13T12:00:00+00:00

How do you take advantage of Git pre-commit hooks? How do you build custom software checks and rules that run every time you commit your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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

Real Python

Managing Dependencies With Python Poetry

Speed Up Your Python Program With Concurrency

Exploring Concurrency in Python

What Is Concurrency?

What Is Parallelism?

Python range(): Represent Numerical Ranges

Construct Numerical Ranges

Count From Zero

Count From Start to Stop

Efficient String Concatenation in Python

Doing String Concatenation With Python’s Plus Operator (+)

How to Iterate Through a Dictionary in Python

Getting Started With Python Dictionaries

Understanding How to Iterate Through a Dictionary in Python

The Real Python Podcast – Episode #229: The Joy of Tinkering & Python Free-Threading Performance

Quiz: Expression vs Statement in Python: What's the Difference?

Quiz: Interacting With Python

NumPy Practical Examples: Useful Techniques

Setting Up Your Working Environment

NumPy Example 1: Creating Multidimensional Arrays From Files

Populating Arrays With File Data

Quiz: NumPy Practical Examples: Useful Techniques

Working With TOML and Python

Interacting With Python

Hello, World!

Running Python in Interactive Mode

Using the Python zip() Function for Parallel Iteration

Understanding the Python zip() Function

Using zip() in Python

Passing n Arguments

Passing No Arguments

Using the len() Function in Python

Getting Started With Python’s len()

Using len() With Built-in Sequences

The Real Python Podcast – Episode #228: Maintaining the Foundations of Python & Cautionary Tales

Quiz: Namespaces and Scope in Python

Python Dictionary Comprehensions: How and When to Use Them

Creating and Transforming Dictionaries in Python

Creating Dictionaries With Literals and dict()

Using for Loops to Populate Dictionaries

Quiz: Basic Input and Output in Python

Quiz: Python Dictionary Comprehensions: How and When to Use Them

Formatting Floats Inside Python F-Strings

Python News Roundup: November 2024

Python’s Popularity Shines in GitHub’s Octoverse 2024

The Real Python Podcast – Episode #227: New PEPs: Template Strings & External Wheel Hosting

Introduction to Web Scraping With Python

Quiz: Variables in Python: Usage and Best Practices

The Real Python Podcast – Episode #226: PySheets: Spreadsheets in the Browser Using PyScript

Python's Magic Methods in Classes

Quiz: Using .__repr__() vs .__str__() in Python

Quiz: Beautiful Soup: Build a Web Scraper With Python

The Real Python Podcast – Episode #225: Python Getting Faster and Leaner & Ideas for Django Projects

Quiz: How to Reset a pandas DataFrame Index

Understanding Python's Global Interpreter Lock (GIL)

The Real Python Podcast – Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries

Using Type Hints for Multiple Return Types in Python

The Real Python Podcast – Episode #223: Exploring the New Features of Python 3.13

What's New in Python 3.13

Differences Between Python's Mutable and Immutable Types

The Real Python Podcast – Episode #222: Using Virtual Environments in Docker & Comparing Python Dev Tools

Advanced Python import Techniques

The Real Python Podcast – Episode #221: Thriving as a Developer With ADHD

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

Doing String Concatenation With Python’s Plus Operator (`+`)

Understanding the Python `zip()` Function

Using `zip()` in Python

Passing `n` Arguments

Getting Started With Python’s `len()`

Using `len()` With Built-in Sequences

Creating Dictionaries With Literals and `dict()`

Using `for` Loops to Populate Dictionaries

Quiz: Using .repr() vs .str() in Python