In Post #87 and Post #88, we learned how to pass information to our functions using positional and keyword arguments. In all of our examples so far, every parameter we defined was required. If we defined a function with two parameters, we had to call it with two arguments, or the program would crash. But … Read more
In Post #87, we learned about positional arguments and saw their biggest weakness: they depend entirely on the correct order. If you have a function with several parameters, it’s easy to forget their sequence and introduce a bug, like in our example where we accidentally called describe_pet(‘Harry’, ‘hamster’) and got a nonsensical result. To solve … Read more
In Post #86, we learned the crucial difference between parameters (the placeholders in a function definition) and arguments (the actual values we pass in a function call). Now, we need to understand how Python knows which argument to assign to which parameter. The simplest and most common way Python does this is by position. In … Read more
In our previous posts, we created simple functions that performed the same task every time they were called. Our display_welcome_banner() function from Post #84 was useful, but it wasn’t very flexible—it couldn’t greet different users by name. To make functions truly powerful and reusable, we need a way to pass information into them. In this … Read more
In Post #84, we learned how to define a function with def and then use it by “calling” it. We established that defining a function is like writing a recipe, and calling it is like cooking the dish. This distinction is one of the most important concepts when working with functions. In this post, we’re … Read more
In Post #83, we learned about the “Don’t Repeat Yourself” (DRY) principle and why avoiding repeated code is so important for writing clean and maintainable programs. We established that the solution is to package our reusable logic into a block called a function. Now it’s time to learn how to do that. In this post, … Read more
Welcome to Part 4 of our series: Functions and Modularity! Until now, all of our programs have been written as single scripts that run from top to bottom. As our programs grow in complexity, this approach becomes difficult to manage. The key to writing clean, professional, and maintainable code is organization. In this post, before … Read more
Welcome to the final post of Part 3! Over this section, we have taken a deep dive into Python’s four essential built-in data structures, the containers that hold and organize all of our program’s data. To wrap up, we will first briefly recap the unique role of each data structure. Then, we’ll build a final … Read more
We’ve learned that sets are mutable collections, meaning we can add and remove items after they’re created. This behavior is similar to lists. But just as lists have an immutable counterpart in tuples (which we learned about in Post #58), sets also have an immutable version for situations where you need a guarantee that the … Read more
In the last few posts, we’ve explored the mathematical side of sets, learning about union, intersection, and difference (Post #77, Post #78). While these operations are powerful, the reality is that in your day-to-day programming, you will use sets for one task far more than any other. In this post, we’ll cover the most frequent … Read more