![]() The np.sort function has 3 primary parameters: With that in mind, let’s talk about the parameters of numpy.sort. The function is fairly simple, but to really understand it, you need to understand the parameters. Then inside of the function, there are a set of parameters that enable you to control exactly how the function works. Again though, you can also refer to the function as numpy.sort() and it will work in a similar way. To initiate the function (assuming you’ve imported NumPy as I explained above), you can call the function as np.sort(). We’ll talk more about this in the examples section, but I want you to understand this before I start explaining the syntax. To set up that alias, you’ll need to “import” NumPy with the appropriate nickname by using the code import numpy as np. Having said that, this sort of aliasing only works if you set it up properly. So if you see the term np.sort(), that’s sort of a shorthand for numpy.sort(). Syntactically, np frequently operates as a “nickname” or alias of the NumPy package. When we write NumPy code, it’s very common to refer to NumPy as np. In this section, I’ll break down the syntax of np.sort.īefore I do that though, you need to be aware of some syntax conventions. Ok … so now that I’ve explained the NumPy sort technique at a high level, let’s take a look at the details of the syntax. To be clear, the NumPy sort function can actually sort arrays in more complex ways, but at a basic level, that’s all the function does. It sorted the array in ascending order, from low to high. Take a look at that image and notice what np.sort did. Essentially, numpy.sort will take an input array, and output a new array in sorted order. ![]() You can use NumPy sort to sort those values in ascending order. Imagine that you have a 1-dimensional NumPy array with five values that are in random order: That’s basically what NumPy sort does … it sorts NumPy arrays. For example, you can do things like calculate the mean of an array, calculate the median of an array, calculate the maximum, etc.Įssentially, NumPy is a broad toolkit for working with arrays of numbers.Īnd one of the things you can do with NumPy, is you can sort an array. We’ll create some NumPy arrays later in this tutorial, but you can think of them as row-and-column grids of numbers.Īnd again, the tools of NumPy can perform manipulations on these arrays. NumPy arrays are essentially arrays of numbers. Numpy functions work on NumPy arraysĪlthough the tools from NumPy can work on a variety of data structures, they are primarily designed to operate on NumPy arrays. That’s actually where the name comes from: More specifically, NumPy provides a set of tools and functions for working with arrays of numbers. NumPy is a toolkit for doing data manipulation in Python. Numpy is a data manipulation module for Python If you’re reading this blog post, you probably know what NumPy is.īut, just in case you don’t, I want to quickly review NumPy. A quick introduction to the NumPy sort function Let’s just start out by talking about the sort function and where it fits into the NumPy data manipulation system. You can click on either of those links and it will take you to the appropriate section in the tutorial.īut if you’re new to Python and NumPy, I suggest that you read the whole blog post. The blog post has two primary sections, a syntax explanation section and an examples section. So, this blog post will show you exactly how to use the technique to sort different kinds of arrays in Python. This tutorial will show you how to use the NumPy sort method, which is sometimes called np.sort or numpy.sort.Īs the name implies, the NumPy sort technique enables you to sort NumPy arrays.
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