python

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

As we build more complex applications, we often find ourselves with data spread across multiple dictionaries. You might have a dictionary of default settings and another with a user’s custom settings, or a user’s basic profile and their contact information stored separately. The question then becomes: how do you efficiently merge them? In this post, … Read more

In Post #67, when we looped through our user_profile dictionary, you might have noticed that the items (name, age, city) were printed in the exact same order we defined them. This might seem obvious and natural, but for a long time in Python’s history, this was not the case at all. This post covers a … Read more

In Post #65 and Post #66, we learned how to access individual items in a dictionary using square brackets [] and the .get() method. But what if we want to process all the information in a dictionary? For that, we need to know how to loop through it. In this post, we’ll explore the three … Read more

In Post #65, we learned how to access dictionary values using square brackets, like my_dict[‘key’]. We also discovered a dangerous side effect: if the key doesn’t exist, our program crashes with a KeyError. This is not ideal for robust applications that need to handle optional or missing data. In this post, we’ll learn the best … Read more

In Post #64, we were introduced to Python’s dictionary, a powerful data structure for storing data as key-value pairs. Now that we know how to define a dictionary, it’s time to learn how to interact with it. This post covers the three fundamental operations you’ll perform on dictionaries every day: accessing a value using its … Read more

So far in Part 3, we’ve explored lists and tuples. Both are powerful sequence types that store collections of items in a specific order, which we access using integer indexes like 0, 1, and 2 (a concept we learned in Post #47). But what if we don’t want to access data by its numerical position? … Read more

In Post #62, we established a clear rule of thumb: use a list for collections that need to change and a tuple for collections that should remain fixed. The primary reason we discussed was intent—communicating to other developers (and your future self) that a piece of data isn’t meant to be modified. But what are … Read more

We’ve now been introduced to two of Python’s sequence types: the mutable list (Post #46) and the immutable tuple (Post #58). They seem very similar—both are ordered collections that can hold various data types. This often leads to a common question for new programmers: which one should I use, and when? This post is a … Read more