Copying Arrays

Often, you need to create a copy of an array to make changes without affecting the original array.

Simple Assignment

First, we'll discuss why we can't simply create another variable using array_2 = array_1, where array_1 is our original array.


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import numpy as np
array_1 = np.array([1, 2, 3])
array_2 = array_1
# Setting the first element of array_2 to 10
array_2[0] = 10
print(array_1)

We changed the value of the first element of array_2 to 10, but this assignment also changed the value of the first element of array_1 to 10.

Note

With array_2 = array_1, you are not creating a new array; instead, you are creating a reference to the same array in memory. As a result, any changes made to array_2 will also affect array_1.

To solve this problem, we could write array_2 = np.array([1, 2, 3]), but that would mean writing the same code twice. Remember the key principle in coding: Don't repeat yourself.

ndarray.copy() Method

Luckily, NumPy has an ndarray.copy() method as a solution to this problem.


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import numpy as np
array_1 = np.array([1, 2, 3])
# Copying the contents of array_1
array_2 = array_1.copy()
# Setting the first element of array_2 to 10
array_2[0] = 10
print(f'Initial array: {array_1}')
print(f'Modified copy: {array_2}')

Now, we have created a new array for array_2 with the same elements as array_1.

For 2D arrays, the copying procedure is exactly the same.

numpy.copy() Function

Instead of the .copy() method, we can also use the copy() function, which takes the array as its parameter: array_2 = np.copy(array_1).

Both the function and the method work the same; however, there is one nuance. They both have the order parameter, which specifies the memory layout of the array, but their default values are different.

The picture below shows the structure of the sales_data_2021 array used in the task:

Завдання

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You are analyzing the quarterly sales data for a company for the year 2021. The data is stored in a NumPy array named sales_data_2021, where each row represents a specific product, and each column represents the quarterly sales for that product.

Create a copy of sales_data_2021 using the appropriate method of a NumPy array and store it in sales_data_2022.
Update the last two elements of the first row (representing a product's quarterly sales) in sales_data_2022 to 390 and 370:
- Use a positive index to specify the row;
- Use a slice with only a negative start value to index the last two elements.

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