Multidimensional Indexing

Now that you are able to access elements in 1D arrays, it's time to learn about indexing in higher-dimensional arrays.

2D Arrays Indexing

This is a 2x3 array, which means it consists of 2 1D arrays along axis 0, and each of these 1D arrays has 3 elements along axis 1.

The images below will clarify positive and negative indexing in 2D arrays (array values are shown in black, and indices are shown in green for positive indices and red for negative indices):

Accessing Elements in 2D Arrays

In 1D arrays, we accessed elements by specifying the index of the element in square brackets. If we do the same in 2D arrays, we retrieve a 1D array at the specified index, which may be exactly what we need.

However, if we want to retrieve a particular element of an inner 1D array, we should specify the index of the 1D array (along axis 0) and the index of its element (along axis 1), e.g., array[0, 1]. We could also write array[0][1] as we do with Python list, but this is less efficient since it performs the search twice for each index instead of once.

Note

If a specified index is out of bounds, an IndexError is thrown, so be cautious of that.


              12345678
            
import numpy as np
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
# Accessing the first element (1D array) with positive index
print(array_2d[0])
# Accessing the second element of the first 1D array with positive index
print(array_2d[0, 1])
# Accessing the last element of the last 1D array with negative index
print(array_2d[-1, -1])

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

Task

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stock_prices contains simulated stock prices over five days for five different companies. Each row corresponds to a particular company, and each column corresponds to a particular day. Consequently, each element in the matrix represents the closing price of a certain company's stock on a given day.

Retrieve all the stock prices of the first company over five days using positive indexing.
Retrieve the stock price of the third company on the second day using positive indiexing.
Retrieve the stock price of the last company on the last day using negative indexing.

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Section 2. Chapter 2

Multidimensional Indexing

Now that you are able to access elements in 1D arrays, it's time to learn about indexing in higher-dimensional arrays.

2D Arrays Indexing

This is a 2x3 array, which means it consists of 2 1D arrays along axis 0, and each of these 1D arrays has 3 elements along axis 1.

Accessing Elements in 2D Arrays

Note

If a specified index is out of bounds, an IndexError is thrown, so be cautious of that.


              12345678
            
import numpy as np
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
# Accessing the first element (1D array) with positive index
print(array_2d[0])
# Accessing the second element of the first 1D array with positive index
print(array_2d[0, 1])
# Accessing the last element of the last 1D array with negative index
print(array_2d[-1, -1])

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

Task

Swipe to show code editor

Retrieve all the stock prices of the first company over five days using positive indexing.
Retrieve the stock price of the third company on the second day using positive indiexing.
Retrieve the stock price of the last company on the last day using negative indexing.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 2

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