In Post #118, we saw how the * operator could be used on the left side of an assignment to collect multiple items into a list (e.g., first, *rest = my_list). This unpacking concept has a powerful counterpart when used on the right side of an expression, inside list or dictionary literals. In this post, … Read more
In Post #60, we learned about sequence unpacking, an elegant way to assign items from a list or tuple to a set of variables, like a, b = [10, 20]. We also learned its strict rule: the number of variables on the left must exactly match the number of items in the sequence on the … Read more
When writing conditional logic, a very common task is to check if a value falls within a certain range. For example, is a number between 1 and 10? Is a user’s age between 18 and 65? Using the tools we already know, we can solve this with the logical and operator. But Python, in its … Read more
A very common task in programming is to take a list of strings and combine them into a single string, often with a separator between each item. For example, you might have a list of tags that you want to display as a single, comma-separated string. While you could do this with a for loop … Read more
In Post #18, we introduced f-strings as the modern, Pythonic way to embed variables into strings. We learned that a simple {variable} is a huge improvement over manual concatenation with the + operator. However, the power of f-strings goes far beyond simple variable insertion. In this post, we’ll explore the format specifier, a mini-language inside … Read more
In Post #113, we introduced the idea of “Pythonic” code and used a loop as our primary example. We briefly contrasted a verbose, manual loop with a clean, elegant one that is more idiomatic to the Python language. Now, it’s time to formalize that comparison. In this post, we’ll take a closer look at the … Read more
Welcome to Part 5 of our series: The Pythonic Way! In the first four parts, we’ve focused on the syntax and rules of the Python language—the fundamentals that make our code work. Now, we’re going to shift our focus from code that simply works to code that is beautiful, elegant, and idiomatic. In this post, … Read more
Welcome to the final post of Part 4, where we’ve learned the essential skill of organizing code with functions and modules. By embracing the DRY principle, you now have the tools to write code that is clean, reusable, and far easier to maintain. To celebrate and solidify these concepts, we will first briefly recap the … Read more
In our posts on modules (Post #109, Post #110), we learned how to separate our code into multiple files. We saw that when you import a module, Python executes that file’s code from top to bottom. This leads to an important question: what if a file contains code that should only run when you execute … Read more
In Post #109, we created our first module, my_math_functions.py, and used the import my_math_functions statement to use its code in our main.py script. This is the most basic way to import, but Python offers several other ways to bring code into your project, each with its own advantages and disadvantages. In this post, we’ll explore … Read more