python

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

In Post #77 and Post #78, we learned the four main set operations: union, intersection, difference, and symmetric difference. While the code for these operations is straightforward, the concepts themselves can sometimes feel a bit abstract. In this post, we’ll take a step back from the code and use a simple visual tool to build … Read more

In Post #77, we learned how to combine sets with union (|) and find commonalities with intersection (&). These are powerful tools, but what if we want to find the elements that are not shared between sets? In this post, we’ll cover the two remaining key set operations: difference, to find elements unique to one … Read more

In Post #75, we learned that sets are collections of unique, unordered items. While their ability to automatically handle duplicates is useful on its own, their true power comes from the ability to perform high-speed mathematical set operations. In this post, we’ll explore the two most fundamental set operations: union, for combining sets, and intersection, … Read more

In Post #75, we were introduced to sets as unordered collections of unique items. We saw one way to create them: using the set() constructor on a list to filter out duplicates. There is another, more direct way to create a set using syntax that might look familiar. In this post, we’ll cover the two … Read more

So far in Part 3, we have explored lists, tuples, and dictionaries. Each has a specific purpose: lists for ordered, mutable collections; tuples for ordered, immutable collections; and dictionaries for key-value mappings. Now, we’ll introduce the fourth and final major built-in data structure in Python: the set. Sets are a specialized data structure designed for … Read more