Unleash the Power of Copying Arrays in Python
When working with arrays in Python, creating a copy of an existing array can be a crucial step in many applications. But did you know that there’s more to copying arrays than just using the assignment operator? In this article, we’ll explore the copy() method, its syntax, and its optional arguments, and show you how to create independent copies of arrays.
The Basics of Copying Arrays
The copy() method returns an array copy of the given object. Its syntax is straightforward: copy(). However, it takes three optional arguments: array, subok, and order. The array argument specifies the input data, while subok determines whether to subclass the output array if the data type is changed or to return a base-class array. The order argument, on the other hand, specifies the order in which the elements are filled in the copied array.
Creating Arrays with Copy()
Let’s start with a simple example. We can create an array using the copy() method, like this:
array0 = np.array([1, 2, 3])
array1 = array0.copy()
The output will be a new array array1 with the same values as array0.
The Difference Between Shallow and Deep Copies
But what happens if we modify array1? Will it affect array0 as well? The answer is yes, because when we use the assignment operator (=), we’re creating a shallow copy of the array. This means that both arrays refer to the same underlying data. To create an independent copy of array0, we need to use the np.copy() function, like this:
array0 = np.array([1, 2, 3])
array1 = np.copy(array0)
Now, modifying array1 won’t affect array0.
Using the Subok Argument
The subok parameter in the copy() function determines whether the copy should be a subclass of the original array’s class (True) or a basic ndarray (False). Let’s see an example:
array0 = np.array([1, 2, 3], dtype=np.float64)
array1 = array0.copy(subok=True)
In this case, array1 will be a subclass of the original array’s class.
Using the Optional Order Argument
The order argument specifies the order in which the copies are filled. We can choose from four options: 'C' for row-wise, 'F' for column-wise, 'A' to preserve the original array’s order, and 'K' to copy the elements in the order they occur in memory. Note that the order argument only affects the memory layout of the copied array and does not change the values or shape of the array.
By mastering the copy() method and its optional arguments, you’ll be able to create independent copies of arrays with ease, and unlock new possibilities in your Python applications.