Course Content

Ultimate NumPy

Integer Array Indexing in 2D Arrays

Speaking of 2D and higher-dimensional arrays, integer array indexing works the same as in 1D arrays along each axis. If we use only one integer array for indexing, we index along only one axis (axis 0). If we use two arrays separated by a comma, we index along both axes (axis 0 and axis 1).

Indexing only along axis 0 using an array of integers returns a 2D array. When we access elements via such indexing, we group them into a new array. This new array consists of 1D arrays, and grouping them increases the dimensionality by one, resulting in a 2D array.

Indexing along axis 0 and axis 1 using two arrays of integers returns a 1D array.

Note
All integer arrays used for each of the axes must have the same shape.

Now it’s time for an example:


              1234567891011121314151617
            
import numpy as np

array_2d = np.array([
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
])
# Retrieving first and the third row
print(array_2d[[0, 2]])
print('-' * 10)
# Retrieving the main diagonal elements
print(array_2d[[0, 1, 2], [0, 1, 2]])
print('-' * 10)
# Retrieving the first and third element of the second row
print(array_2d[1, [0, 2]])
# IndexError is thrown, since index 3 along axis 0 is out of bounds
print(array_2d[[0, 3], [0, 1]])

Here is an illustration for clarification:

As you can see, we can also combine basic integer indexing and integer array indexing.

Note
Once again, if at least one of the indices is out of bounds, an IndexError is thrown.

Task

You are analyzing the performance metrics of three different products over two criteria, stored in a 2D NumPy array. Use integer array indexing (a Python list) to retrieve the first metric of the first product and the second metric of the third product from performance_metrics using only positive indices.

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 6

Integer Array Indexing in 2D Arrays

Indexing along axis 0 and axis 1 using two arrays of integers returns a 1D array.

Note
All integer arrays used for each of the axes must have the same shape.

Now it’s time for an example:


              1234567891011121314151617
            
import numpy as np

array_2d = np.array([
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
])
# Retrieving first and the third row
print(array_2d[[0, 2]])
print('-' * 10)
# Retrieving the main diagonal elements
print(array_2d[[0, 1, 2], [0, 1, 2]])
print('-' * 10)
# Retrieving the first and third element of the second row
print(array_2d[1, [0, 2]])
# IndexError is thrown, since index 3 along axis 0 is out of bounds
print(array_2d[[0, 3], [0, 1]])

Here is an illustration for clarification:

As you can see, we can also combine basic integer indexing and integer array indexing.

Note
Once again, if at least one of the indices is out of bounds, an IndexError is thrown.

Task

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 6

Integer Array Indexing in 2D Arrays

Indexing along axis 0 and axis 1 using two arrays of integers returns a 1D array.

Note
All integer arrays used for each of the axes must have the same shape.

Now it’s time for an example:


              1234567891011121314151617
            
import numpy as np

array_2d = np.array([
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
])
# Retrieving first and the third row
print(array_2d[[0, 2]])
print('-' * 10)
# Retrieving the main diagonal elements
print(array_2d[[0, 1, 2], [0, 1, 2]])
print('-' * 10)
# Retrieving the first and third element of the second row
print(array_2d[1, [0, 2]])
# IndexError is thrown, since index 3 along axis 0 is out of bounds
print(array_2d[[0, 3], [0, 1]])

Here is an illustration for clarification:

As you can see, we can also combine basic integer indexing and integer array indexing.

Note
Once again, if at least one of the indices is out of bounds, an IndexError is thrown.

Task

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

Everything was clear?

Thanks for your feedback!

Indexing along axis 0 and axis 1 using two arrays of integers returns a 1D array.

Note
All integer arrays used for each of the axes must have the same shape.

Now it’s time for an example:


              1234567891011121314151617
            
import numpy as np

array_2d = np.array([
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
])
# Retrieving first and the third row
print(array_2d[[0, 2]])
print('-' * 10)
# Retrieving the main diagonal elements
print(array_2d[[0, 1, 2], [0, 1, 2]])
print('-' * 10)
# Retrieving the first and third element of the second row
print(array_2d[1, [0, 2]])
# IndexError is thrown, since index 3 along axis 0 is out of bounds
print(array_2d[[0, 3], [0, 1]])

Here is an illustration for clarification:

As you can see, we can also combine basic integer indexing and integer array indexing.

Note
Once again, if at least one of the indices is out of bounds, an IndexError is thrown.

Task

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

Section 2. Chapter 6

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