In Post #138, we successfully built our first decorator by manually “wrapping” a function. We defined our original function, passed it to a decorator function, and then reassigned the returned wrapper back to the original function’s name. This manual process, say_whee = my_decorator(say_whee), works perfectly and clearly reveals the mechanics of how a decorator works. … Read more
In Post #137, we laid the groundwork for understanding decorators by confirming three key concepts: functions are objects, they can be passed as arguments, and they can be returned from other functions. Now, we’re ready to assemble these pieces to build our first manual decorator. In this post, we will walk through the step-by-step pattern … Read more
We are now about to begin our journey into one of Python’s most powerful and elegant features: decorators. Decorators can seem like magic at first, but they are a logical extension of concepts we’ve already learned. To understand them, we don’t need to learn any new syntax yet. We just need to put our existing … Read more
In our recent discussions on generators and functional tools like map and filter, a common theme has emerged: the power of “lazy” and memory-efficient iteration. Python takes this concept even further with a specialized module in its standard library that is designed for exactly this purpose. In this post, we’ll take a quick tour of … Read more
Imagine you have a list of conditions—say, a list of booleans representing whether a user has completed certain steps in a registration process. How would you check if they’ve completed any of the steps? Or if they’ve completed all of them? You could write a for loop to manually check a flag variable, but this … Read more
In Post #133, we learned how the zip() function can take multiple lists and combine them into a single iterator of tuples, effectively pairing up the elements. This is like turning columns of data into a series of rows. But what if we have the opposite problem? What if we start with a list of … Read more
So far, our for loops have iterated over one sequence at a time. But a common programming task is to work with multiple lists in parallel. For example, you might have a list of student names and a corresponding list of their grades, and you want to process them together, pairing the first student with … Read more
In Post #130 and Post #131, we learned about the functional programming tools map() and filter(). You might have noticed that the tasks they accomplish—transforming and selecting items from a list—are very similar to what we can do with the list comprehensions we learned about in Post #120. This leads to a natural question: which … Read more
In Post #130, we explored the map() function, a tool for applying a transformation to every item in a sequence. But what if we don’t want to transform the items, but rather select a subset of them that meets a certain condition? For this task, Python provides another core functional tool: the filter() function. In … Read more
In our discussions on comprehensions and generators, we’ve touched on ideas from a programming paradigm called “functional programming.” This style emphasizes transforming data by passing it through a series of functions. Python isn’t a purely functional language, but it includes several powerful built-in functions that support this clean and expressive style of coding. In this … Read more