If you’re doing numeric calculations, NumPy is a lot faster than than plain Python—but sometimes that’s not enough. What should you do when your NumPy-based code is too slow? Your first thought might be parallelism, but that should probably be the last thing you consider. There are many speedups you can do before parallelism becomes helpful, from algorithmic improvements to working around NumPy’s architectural limitations. Let’s see why NumPy can be slow, and then some solutions to help speed up […]

In Python, range() is most commonly used in for loops to iterate over a known range of numbers. But that’s not all it can do! There are other interesting uses of range(). For example, you can pick out elements of a range or check whether a given number belongs to a range of numbers. The latter operation is known as a membership test. Python’s range() objects support most of the operations that you’ve come to expect from lists. For example, […]

Sometimes, when you’re working with data, you may have the data as a list of nested lists. A common operation is to flatten this data into a one-dimensional list in Python. Flattening a list involves converting a multidimensional list, such as a matrix, into a one-dimensional list. To better illustrate what it means to flatten a list, say that you have the following matrix of numeric values: >>> >>> matrix = [ … [9, 3, 8, 3], … [4, 5, […]

Have you ever found yourself lost in a big repository of Python code, struggling to keep track of the intended types of variables? Without the proper use of type hints and annotations, uncovering variable types can become a tedious and time-consuming task. Perhaps you’re an avid user of type hints but aren’t sure how to annotate methods that return self or other instances of the class itself. That’s the issue that you’ll tackle in this tutorial. First, though, you’ll need […]