Real Python

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

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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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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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How to Reset a pandas DataFrame Index

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

In this tutorial, you’ll learn how to reset a pandas DataFrame index, the reasons why you might want to do this, and the problems that could occur if you don’t.

Before you start your learning journey, you should familiarize yourself with how to create a pandas DataFrame. Knowing the difference between a DataFrame and a pandas Series will also prove useful to you.

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

As a starting point, you’ll need some data. To begin with, you’ll use the band_members.csv file included in the downloadable materials that you can access by clicking the link below:

Get Your Code: Click here to download the free sample code you’ll use to learn how to reset a pandas DataFrame index.

The table below describes the data from band_members.csv that you’ll begin with:

Column Name	PyArrow Data Type	Description
`first_name`	`string`	First name of member
`last_name`	`string`	Last name of member
`instrument`	`string`	Main instrument played
`date_of_birth`	`string`	Member’s date of birth

As you’ll see, the data has details of the members of the rock band The Beach Boys. Each row contains information about its various members both past and present.

Note: In case you’ve never heard of The Beach Boys, they’re an American rock band formed in the early 1960s.

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

If you’re working at the command line, you can install both pandas and pyarrow using the single command python -m pip install pandas pyarrow. If you’re working in a Jupyter Notebook, you should use !python -m pip install pandas pyarrow. Regardless, you should do this within a virtual environment to avoid clashes with the libraries you use in your global environment.

Once you have the libraries in place, it’s time to read your data into a DataFrame:

Python
      
>>> import pandas as pd

>>> beach_boys = pd.read_csv(
...     "band_members.csv"
... ).convert_dtypes(dtype_backend="pyarrow")

First, you used import pandas to make the library available within your code. To construct the DataFrame and read it into the beach_boys variable, you used pandas’ read_csv() function, passing band_members.csv as the file to read. Finally, by passing dtype_backend="pyarrow" to .convert_dtypes() you convert all columns to pyarrow types.

If you want to verify that pyarrow data types are indeed being used, then beach_boys.dtypes will satisfy your curiosity:

Python
      
        
      
    
>>> beach_boys.dtypes
first_name            string[pyarrow]
last_name             string[pyarrow]
instrument            string[pyarrow]
date_of_birth         string[pyarrow]
dtype: object

As you can see, each data type contains [pyarrow] in its name.

If you wanted to analyze the date information thoroughly, then you would parse the date_of_birth column to make sure dates are read as a suitable pyarrow date type. This would allow you to analyze by specific days, months or years, and so on, as commonly found in pivot tables.

The date_of_birth column is not analyzed in this tutorial, so the string data type it’s being read as will do. Later on, you’ll get the chance to hone your skills with some exercises. The solutions include the date parsing code if you want to see how it’s done.

Now that the file has been loaded into a DataFrame, you’ll probably want to take a look at it:

Python
      
        
      
    
>>> beach_boys
  first_name last_name instrument date_of_birth
    Brian    Wilson       Bass   20-Jun-1942
     Mike      Love  Saxophone   15-Mar-1941
       Al   Jardine     Guitar   03-Sep-1942
    Bruce  Johnston       Bass   27-Jun-1942
     Carl    Wilson     Guitar   21-Dec-1946
   Dennis    Wilson      Drums   04-Dec-1944
    David     Marks     Guitar   22-Aug-1948
    Ricky    Fataar      Drums   05-Sep-1952
  Blondie   Chaplin     Guitar   07-Jul-1951

DataFrames are two-dimensional data structures similar to spreadsheets or database tables. A pandas DataFrame can be considered a set of columns, with each column being a pandas Series. Each column also has a heading, which is the name property of the Series, and each row has a label, which is referred to as an element of its associated index object.

The DataFrame’s index is shown to the left of the DataFrame. It’s not part of the original band_members.csv source file, but is added as part of the DataFrame creation process. It’s this index object you’re learning to reset.

The index of a DataFrame is an additional column of labels that helps you identify rows. When used in combination with column headings, it allows you to access specific data within your DataFrame. The default index labels are a sequence of integers, but you can use strings to make them more meaningful. You can actually use any hashable type for your index, but integers, strings, and timestamps are the most common.

Note: Although indexes are certainly useful in pandas, an alternative to pandas is the new high-performance Polars library, which eliminates them in favor of row numbers. This may come as a surprise, but aside from being used for selecting rows or columns, indexes aren’t often used when analyzing DataFrames. Also, row numbers always remain sequential when rows are added or removed in a Polars DataFrame. This isn’t the case with indexes in pandas.

Read the full article at https://realpython.com/pandas-reset-index/ »

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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

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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.

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Variables in Python: Usage and Best Practices

2024-11-04T14:00:00+00:00

In Python, variables are symbolic names that refer to objects or values stored in your computer’s memory. They allow you to assign descriptive names to data, making it easier to manipulate and reuse values throughout your code.

Understanding variables is key for Python developers because variables are essential building blocks for any Python program. Proper use of variables allows you to write clear, readable, and maintainable code.

In this tutorial, you’ll learn 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
Create, access, and use variables in their scopes

To get the most out of this tutorial, you should be familiar with Python’s basic data types and have a general understanding of programming concepts like loops and functions.

Don’t worry if you don’t have all this knowledge yet and you’re just getting started. You won’t need this knowledge to benefit from working through the early sections of this tutorial.

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

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

Interactive Quiz

Variables in Python: Usage and Best Practices

In this quiz, you'll test your understanding of variables in Python. Variables are symbolic names that refer to objects or values stored in your computer's memory, and they're essential building blocks for any Python program.

Getting to Know Variables in Python

In Python, variables are names associated with concrete objects or values stored in your computer’s memory. By associating a variable with a value, you can refer to the value using a descriptive name and reuse it as many times as needed in your code.

Variables behave as if they were the value they refer to. To use variables in your code, you first need to learn how to create them, which is pretty straightforward in Python.

Creating Variables With Assignments

The primary way to create a variable in Python is to assign it a value using the assignment operator and the following syntax:

Python Syntax
      
variable_name = value

In this syntax, you have the variable’s name on the left, then the assignment (=) operator, followed by the value you want to assign to the variable at hand. The value in this construct can be any Python object, including strings, numbers, lists, dictionaries, or even custom objects.

Note: To learn more about assignments, check out Python’s Assignment Operator: Write Robust Assignments.

Here are a few examples of variables:

Python
      
>>> word = "Python"

>>> number = 42

>>> coefficient = 2.87

>>> fruits = ["apple", "mango", "grape"]

>>> ordinals = {1: "first", 2: "second", 3: "third"}

>>> class SomeCustomClass: pass
>>> instance = SomeCustomClass()

In this code, you’ve defined several variables by assigning values to names. The first five examples include variables that refer to different built-in types. The last example shows that variables can also refer to custom objects like an instance of your SomeCustomClass class.

Setting and Changing a Variable’s Data Type

Apart from a variable’s value, it’s also important to consider the data type of the value. When you think about a variable’s type, you’re considering whether the variable refers to a string, integer, floating-point number, list, tuple, dictionary, custom object, or another data type.

Python is a dynamically typed language, which means that variable types are determined and checked at runtime rather than during compilation. Because of this, you don’t need to specify a variable’s type when you’re creating the variable. Python will infer a variable’s type from the assigned object.

Note: In Python, variables themselves don’t have data types. Instead, the objects that variables reference have types.

For example, consider the following variables:

Python
      
        
      
    
>>> name = "Jane Doe"
>>> age = 19
>>> subjects = ["Math", "English", "Physics", "Chemistry"]

>>> type(name)
<class 'str'>
>>> type(age)
<class 'int'>
>>> type(subjects)
<class 'list'>

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

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The Python Square Root Function

2024-11-03T14:00:00+00:00

The Python square root function, sqrt(), is part of the math module and is used to calculate the square root of a given number. To use it, you import the math module and call math.sqrt() with a non-negative number as an argument. For example, math.sqrt(9) returns 3.0.

This function works with both integers and floats and is essential for mathematical operations like solving equations and calculating geometric properties. In this tutorial, you’ll learn how to effectively use the square root function in Python.

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

Python’s sqrt() function calculates square roots using Python’s math.sqrt() for quick and accurate results in your programs.
math.sqrt() calculates the square root of positive numbers and zero but raises an error for negative inputs.
Python’s square root function can be used to solve real-world problems like calculating distances using the Pythagorean theorem.

Time to dive in!

Python Pit Stop: This tutorial is a quick and practical way to find the info you need, so you’ll be back to your project in no time!

Free Bonus: Click here to get our free Python Cheat Sheet that shows you the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

Square Roots in Mathematics

In algebra, a square, x, is the result of a number, n, multiplied by itself: x = n²

You can calculate squares using Python:

Python
      
>>> n = 5
>>> x = n**2
>>> x
25

The Python ** operator is used for calculating the power of a number. In this case, 5 squared, or 5 to the power of 2, is 25.

The square root, then, is the number n, which when multiplied by itself yields the square, x.

In this example, n, the square root of 25, is 5.

25 is an example of a perfect square. Perfect squares are the squares of integer values:

Python
      
>>> 1**2
1

>>> 2**2
4

>>> 3**2
9

You might have memorized some of these perfect squares when you learned your multiplication tables in an elementary algebra class.

If you’re given a small perfect square, it may be straightforward enough to calculate or memorize its square root. But for most other squares, this calculation can get a bit more tedious. Often, an estimation is good enough when you don’t have a calculator.

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

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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."

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Python Closures: Common Use Cases and Examples

2024-10-30T14:00:00+00:00

In Python, a closure is typically a function defined inside another function. This inner function grabs the objects defined in its enclosing scope and associates them with the inner function object itself. The resulting combination is called a closure.

Closures are a common feature in functional programming languages. In Python, closures can be pretty useful because they allow you to create function-based decorators, which are powerful tools.

In this tutorial, you’ll:

Learn what closures are and how they work in Python
Get to know common use cases of closures
Explore alternatives to closures

To get the most out of this tutorial, you should be familiar with several Python topics, including functions, inner functions, decorators, classes, and callable instances.

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

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

Interactive Quiz

Python Closures: Common Use Cases and Examples

In this quiz, you'll test your understanding of Python closures. Closures are a common feature in functional programming languages and are particularly popular in Python because they allow you to create function-based decorators.

Getting to Know Closures in Python

A closure is a function that retains access to its lexical scope, even when the function is executed outside that scope. When the enclosing function returns the inner function, then you get a function object with an extended scope.

In other words, closures are functions that capture the objects defined in their enclosing scope, allowing you to use them in their body. This feature allows you to use closures when you need to retain state information between consecutive calls.

Closures are common in programming languages that are focused on functional programming, and Python supports closures as part of its wide variety of features.

In Python, a closure is a function that you define in and return from another function. This inner function can retain the objects defined in the non-local scope right before the inner function’s definition.

To better understand closures in Python, you’ll first look at inner functions because closures are also inner functions.

Inner Functions

In Python, an inner function is a function that you define inside another function. This type of function can access and update names in their enclosing function, which is the non-local scope.

Here’s a quick example:

Python
      
        
      
    
>>> def outer_func():
...     name = "Pythonista"
...     def inner_func():
...         print(f"Hello, {name}!")
...     inner_func()
...

>>> outer_func()
Hello, Pythonista!

>>> greeter = outer_func()
>>> print(greeter)
None

In this example, you define outer_func() at the module level or global scope. Inside this function, you define the name local variable. Then, you define another function called inner_func(). Because this second function lives in the body of outer_func(), it’s an inner or nested function. Finally, you call the inner function, which uses the name variable defined in the enclosing function.

When you call outer_func(), inner_func() interpolates name into the greeting string and prints the result to your screen.

Note: To learn more about inner functions, check out the Python Inner Functions: What Are They Good For? tutorial.

In the above example, you defined an inner function that can use the names in the enclosing scope. However, when you call the outer function, you don’t get a reference to the inner function. The inner function and the local names won’t be available outside the outer function.

In the following section, you’ll learn how to turn an inner function into a closure, which makes the inner function and the retained variables available to you.

Function Closures

All closures are inner functions, but not all inner functions are closures. To turn an inner function into a closure, you must return the inner function object from the outer function. This may sound like a tongue twister, but here’s how you can make outer_func() return a closure object:

Python
      
        
      
    
>>> def outer_func():
...     name = "Pythonista"
...     def inner_func():
...         print(f"Hello, {name}!")
...     return inner_func
...

>>> outer_func()
<function outer_func.<locals>.inner_func at 0x1066d16c0>

>>> greeter = outer_func()

>>> greeter()
Hello, Pythonista!

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

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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

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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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Beautiful Soup: Build a Web Scraper With Python

2024-10-28T14:00:00+00:00

Web scraping is the automated process of extracting data from the internet. The Python libraries Requests and Beautiful Soup are powerful tools for the job. To effectively harvest the vast amount of data available online for your research, projects, or personal interests, you’ll need to become skilled at web scraping.

In this tutorial, you’ll learn 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 data from the internet
Step through a web scraping pipeline from start to finish
Build a script that fetches job offers from websites and displays relevant information in your console

If you like learning with hands-on examples and have a basic understanding of Python and HTML, then this tutorial is for you! Working through this project will give you the knowledge and tools you need to scrape any static website out there on the World Wide Web. You can download the project source code by clicking on the link below:

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

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

Interactive Quiz

Beautiful Soup: Build a Web Scraper With Python

In this quiz, you'll test your understanding of web scraping using Python. By working through this quiz, you'll revisit how to inspect the HTML structure of a target site, decipher data encoded in URLs, and use Requests and Beautiful Soup for scraping and parsing data from the Web.

What Is Web Scraping?

Web scraping is the process of gathering information from the internet. Even copying and pasting the lyrics of your favorite song can be considered a form of web scraping! However, the term “web scraping” usually refers to a process that involves automation. While some websites don’t like it when automatic scrapers gather their data, which can lead to legal issues, others don’t mind it.

If you’re scraping a page respectfully for educational purposes, then you’re unlikely to have any problems. Still, it’s a good idea to do some research on your own to make sure you’re not violating any Terms of Service before you start a large-scale web scraping project.

Reasons for Automated Web Scraping

Say that you like to surf—both in the ocean and online—and you’re looking for employment. It’s clear that you’re not interested in just any job. With a surfer’s mindset, you’re waiting for the perfect opportunity to roll your way!

You know about a job site that offers precisely the kinds of jobs you want. Unfortunately, a new position only pops up once in a blue moon, and the site doesn’t provide an email notification service. You consider checking up on it every day, but that doesn’t sound like the most fun and productive way to spend your time. You’d rather be outside surfing real-life waves!

Thankfully, Python offers a way to apply your surfer’s mindset. Instead of having to check the job site every day, you can use Python to help automate the repetitive parts of your job search. With automated web scraping, you can write the code once, and it’ll get the information that you need many times and from many pages.

Note: In contrast, when you try to get information manually, you might spend a lot of time clicking, scrolling, and searching, especially if you need large amounts of data from websites that are regularly updated with new content. Manual web scraping can take a lot of time and be highly repetitive and error-prone.

There’s so much information on the internet, with new information constantly being added. You’ll probably be interested in some of that data, and much of it is out there for the taking. Whether you’re actually on the job hunt or just want to automatically download all the lyrics of your favorite artist, automated web scraping can help you accomplish your goals.

Challenges of Web Scraping

The internet has grown organically out of many sources. It combines many different technologies, styles, and personalities, and it continues to grow every day. In other words, the internet is a hot mess! Because of this, you’ll run into some challenges when scraping the web:

Variety: Every website is different. While you’ll encounter general structures that repeat themselves, each website is unique and will need personal treatment if you want to extract the relevant information.
Durability: Websites constantly change. Say you’ve built a shiny new web scraper that automatically cherry-picks what you want from your resource of interest. The first time you run your script, it works flawlessly. But when you run the same script a while later, you run into a discouraging and lengthy stack of tracebacks!

Unstable scripts are a realistic scenario because many websites are in active development. If a site’s structure changes, then your scraper might not be able to navigate the sitemap correctly or find the relevant information. The good news is that changes to websites are often small and incremental, so you’ll likely be able to update your scraper with minimal adjustments.

Still, keep in mind that the internet is dynamic and keeps on changing. Therefore, the scrapers you build will probably require maintenance. You can set up continuous integration to run scraping tests periodically to ensure that your main script doesn’t break without your knowledge.

An Alternative to Web Scraping: APIs

Some website providers offer application programming interfaces (APIs) that allow you to access their data in a predefined manner. With APIs, you can avoid parsing HTML. Instead, you can access the data directly using formats like JSON and XML. HTML is primarily a way to visually present content to users.

When you use an API, the data collection process is generally more stable than it is through web scraping. That’s because developers create APIs to be consumed by programs rather than by human eyes.

The front-end presentation of a site might change often, but a change in the website’s design doesn’t affect its API structure. The structure of an API is usually more permanent, which means it’s a more reliable source of the site’s data.

However, APIs can change as well. The challenges of both variety and durability apply to APIs just as they do to websites. Additionally, it’s much harder to inspect the structure of an API by yourself if the provided documentation lacks quality.

Read the full article at https://realpython.com/beautiful-soup-web-scraper-python/ »

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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 data from the Web, 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.

[ 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 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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Quiz: The Python Standard REPL: Try Out Code and Ideas Quickly

2024-10-24T12:00:00+00:00

In this quiz, you’ll test your understanding of The Python Standard REPL: Try Out Code and Ideas Quickly.

The Python REPL allows you to run Python code interactively, which is useful for testing new ideas, exploring libraries, refactoring and debugging code, and trying out examples.

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

Quiz: Python Class Constructors: Control Your Object Instantiation

2024-10-23T12:00:00+00:00

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

By working through this quiz, you’ll revisit the internal instantiation process, object initialization using .__init__(), and fine-tuning object creation by overriding .__new__().

[ 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 ]

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.

[ 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: Defining Your Own Python Function

2024-10-22T12:00:00+00:00

In this quiz, you’ll test your understanding of how to define your own Python function.

You’ll revisit theoretical knowledge about passing values to functions, when to divide your program into separate user-defined functions, and all the tools you’ll need to define complex and powerful functions in Python.

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Quiz: Pydantic: Simplifying Data Validation in Python

2024-10-20T12:00:00+00:00

In this quiz, you’ll test your understanding of Pydantic. Pydantic is a powerful data validation library for Python. You can also use a related library, pydantic-settings, for settings management.

By working through this quiz, you’ll revisit how to work with data schemas with Pydantic’s BaseModel, write custom validators for complex use cases, validate function arguments with Pydantic’s @validate_call, and manage settings and configure applications with pydantic-settings.

[ 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 #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.

[ 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 #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.

[ 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 ]

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.

[ 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 ]

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 ]

Customizing VS Code Through Color Themes

2024-09-17T14:00:00+00:00

A well-designed coding environment not only enhances your focus and productivity but also makes coding sessions more enjoyable. In this Code Conversation, your instructor Philipp Acsany will guide you step-by-step through the process of finding, installing, and adjusting color themes in VS Code. You’ll explore the various options available in VS Code and learn how to make fine adjustments to create a setup that suits your personal preferences.

In this video course, you’ll:

Learn about Themes in VS Code
Find a VS Code Color Theme
Select a Theme
Install Your Theme
Make Additional Adjustments

By the end of the course, you’ll have a coding environment that not only looks great but also enhances your overall coding experience.

[ 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 ]

The Real Python Podcast – Episode #219: Astrophysics and Astronomy With Python & PyCon Africa 2024

2024-09-06T12:00:00+00:00

Are you interested in practicing your Python skills while learning how to solve astrophysics and astronomy problems? 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

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

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

How to Reset a pandas DataFrame Index

Introduction to Web Scraping With Python

Quiz: Variables in Python: Usage and Best Practices

Variables in Python: Usage and Best Practices

Getting to Know Variables in Python

Creating Variables With Assignments

Setting and Changing a Variable’s Data Type

The Python Square Root Function

Square Roots in Mathematics

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

Python Closures: Common Use Cases and Examples

Getting to Know Closures in Python

Inner Functions

Function Closures

Python's Magic Methods in Classes

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

Beautiful Soup: Build a Web Scraper With Python

What Is Web Scraping?

Reasons for Automated Web Scraping

Challenges of Web Scraping

An Alternative to Web Scraping: APIs

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

Quiz: The Python Standard REPL: Try Out Code and Ideas Quickly

Quiz: Python Class Constructors: Control Your Object Instantiation

Understanding Python's Global Interpreter Lock (GIL)

Quiz: Defining Your Own Python Function

Quiz: Pydantic: Simplifying Data Validation in Python

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

Customizing VS Code Through Color Themes

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

The Real Python Podcast – Episode #219: Astrophysics and Astronomy With Python & PyCon Africa 2024

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