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
In Post #60, we learned about the art of sequence unpacking, a “Pythonic” way to assign items from a tuple or list to multiple variables at once. This elegant feature is more than just a convenient trick—it’s the key to one of the most powerful capabilities of Python functions. In this post, we will explore … Read more
In Post #58 and Post #59, we learned about tuples as immutable, ordered collections. While their unchangeable nature is useful for data integrity, tuples are also central to one of Python’s most elegant and convenient features. In this post, we will explore sequence unpacking (often called tuple unpacking). This is a “Pythonic” shortcut that allows … Read more