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
We have spent the last several posts exploring Python’s powerful dictionary data structure. Now it’s time to put all that theory into practice. In this mini-project, we will build a functional, interactive, command-line contact book. Our program will be able to add new contacts, view a list of all contacts, look up the details for … Read more
Dictionaries are one of the most used data structures in Python, prized for their ability to store and retrieve data with a key. One of their most amazing features is that they are incredibly fast. Looking up a key in a dictionary with ten million items is, on average, just as fast as looking it … Read more
So far, we’ve learned to build dictionaries by manually defining them or by adding key-value pairs one by one. These methods work perfectly, but as you become more advanced in Python, you’ll discover more concise and powerful ways to accomplish common tasks. In this post, we’ll get a glimpse of a more advanced topic known … Read more
Just as we learned with lists, being able to add data to a dictionary is only half the story. A key feature of these mutable data structures is the ability to remove data as well. Whether you’re removing a user’s setting, processing an item from a queue, or resetting a data collection, knowing how to … Read more
So far, our lists have contained simple items like numbers and strings, and our dictionary values have been simple as well. But what happens when the data we need to store is more complex? What if a user has a list of interests, or we have a list of users, where each user is a … Read more