We can tell that they are organized as a tuple, because they are enclosed in parenthesis.īut you could also put those arrays together inside of a Python list. Notice that the arrays in the above code, array1 and array2, are organized inside of a tuple. So if you had two NumPy arrays, array1 and array2, you could write the code something like this: So you need to provide multiple NumPy arrays inside of some type of sequence.įurthermore, the documentation shows the arrays organized inside of a tuple. The input arrays can be organized inside of any Python sequenceĪccording to the documentation of NumPy vstack, the input should be a sequence of NumPy arrays. It will accept a variety of input types, and they can be presented inside of a few different structures. The collection of input arrays is the only thing you need to provide as an input (AKA, argument) to the function. Here, I’ll refer to the input arrays collectively as input-arrays input-arrays (required) The arguments of np.vstackĪs I mentioned, there’s only one real argument to the np.vstack function … the input arrays that you want to combine together. Having said that, there is a little bit of flexibility in terms of how we specify the inputs, so let’s talk about the input argument. In fact, the arrays that you want to combine together are the only argument to the function. You call the function with the code np.vstack(), and then inside of the function, we need to provide the names of the arrays that we want to combine. The syntax of np.vstackĪs I noted a couple of paragraphs ago, the syntax of np.vstack is extremely simple. Having said all of that, let’s take closer look at the syntax. This is a common convention, so I’ll use it here (although I will also refer to the function as numpy.hstack). If you import NumPy with the code import numpy as np, then you can refer to NumPy in your syntax with the alias np. Typically, we’ll call the function with the name np.vstack(), although exactly how you call it depends on how you import the NumPy module. The syntax of NumPy vstack is very simple. So now that you know what NumPy vstack does, let’s take a look at the syntax. NumPy hstack combines arrays horizontally and NumPy vstack combines together arrays vertically. So it’s sort of like the sibling of np.hstack. NumPy vstack is related to NumPy concatenate and NumPy hstack, except it enables you to combine together NumPy arrays vertically. And then there’s NumPy hstack, which enables you to combine together arrays horizontally. For example, NumPy concatenate is a very flexible tool for combining together NumPy arrays, either vertically or horizontally. Numpy vstack, Numpy hstack, and Numpy concatenate are all somewhat similar. Numpy vstack is actually one of several Numpy tools for combining Numpy arrays. It’s essentially a data manipulation tool in NumPy. NumPy vstack is a tool for combining together Numpy arrays. One of the common things that you will need to do with arrays is combine them together. For example, there are tools for reshaping arrays. NumPy provides tools for creating these arrays of numbers.Īnd it also has tools for manipulating these arrays of numbers. They can even have multiple dimensions (more than two). These NumPy arrays can be 1-dimensional, like this … These special arrays of numbers are called NumPy arrays. More specifically, it’s a toolkit for working with arrays of numbers. NumPy is just a toolkit for working with numbers in Python. That said, if you’re in a hurry and want to find a quick answer to something, you can click on any of the following links and it will take you to the appropriate place in the tutorial.įor people who are getting started with Python, and data science in Python, NumPy is a little confusing. It will give you a quick background to NumPy, explain the syntax of NumPy vstack, and it will show you clear, step-by-step examples. This is a very simple NumPy function, but in case you’re more of a beginner, this tutorial will explain almost everything you need to know. In this tutorial, I’ll explain how to use the NumPy vstack function, which is also called numpy.vstack or np.vstack.
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