To do this, we’ll use the np.arrange function to generate the values, and Numpy reshape to reshape the array into the correct shape. Here, we’re going to create an array with the values from 1 to 6, arranged into an array with 2 rows and 3 columns. Test the copy (to show that it’s a proper copy).In this example, we’ll make a copy of a pre-existing Numpy array. Now that you’re learn how the syntax works, let’s look at an example of Numpy copy.īefore you run the example, make sure that you import Numpy correctly.ĮXAMPLE 1: Make a copy of an existing Numpy array Otherwise, the output array will be a base-class array.īy default, this parameter is set to subok = False. If subok = True, then any sub classes will be copied over to the output array. This parameter is somewhat rarely used, so you probably should’t worry about it too much. 'K' attempts to match the input array as closely as possibleīy default, this parameter is set to order = 'K'.'A' specifies that the array should be copied in Fortran order if the input array is Fortran continuous, otherwise copy in C order.'F' specifies that the array should be copied in Fortran order (column first).'C' specifies that the array should be copied in C order (row first).There are several possible arguments to this paramter: The order parameter controls the order of how the values are copied. This is the Numpy array that you want to copy.Īdditionally, you should note that instead of a Numpy array, the function will also operate similarly on array-like objects, such as lists and tuples. Let’s look at those parameters a little more closely. Then, there are two optional parameters that you can use to modify the function behavior.Īs noted above, there is only one primary argument, and two optional parameters to np.copy: Inside the parenthesis, you provide the name of the original Numpy array that you wan to copy as the first argument. This is the standard convention among Python programmers and data scientists.Īdditionally, to be able to use this prefix, you must import Numpy as follows:Įverything else going forward will assume that you’ve imported Numpy like this. When we use Numpy functions, we almost always call them with the prefix np. Ok, in this section, we’ll look at the syntax of Numpy copy. This is one of the simplest functions in Numpy.īut it’s really important, because there are other “bad” ways to make copies, as I’ll show you in the examples section.īefore we look at an example though, let’s look at the syntax. The primary data structure of the Numpy system is the Numpy array: a row-and-column structure that stores numbers.Īnd we use functions from Numpy to manipulate, aggregate, and analyze the data that we store in Numpy arrays. If you need something specific, you can click on any of the following links.Īs you probably know, Numpy is a toolkit for working with numeric data in Python. I’ll explain the syntax of np.copy and show you a clear example of how to use it. In this tutorial, I’ll explain the Numpy copy function.
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