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Ultimate NumPy
course content

Course Content

Ultimate NumPy

Ultimate NumPy

1. NumPy Basics
Introduction to NumPyBasic Array CreationCreating Higher Dimensional ArraysCreation Functions for 1D ArraysCreation Functions for 2D ArraysGeneral Array Creation FunctionsRandom Arrays
2. Indexing and Slicing
Basic IndexingMultidimensional IndexingSlicingSlicing in 2D ArraysInteger Array IndexingInteger Array Indexing in 2D ArraysBoolean IndexingMore about Comparisons Boolean Indexing in 2D ArraysAssigning Values to Indexed ElementsAssigning Values to Indexed Subarrays
3. Commonly used NumPy Functions
Sorting ArraysSorting 2D ArraysCopying ArraysReshaping ArraysFlattening ArraysArray Concatenation
4. Math with NumPy
BroadcastingBasic Mathematical OperationsStatistical OperationsBasic Linear Algebra with NumPy

bookInteger 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]])
copy

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
test

Swipe to show code editor

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.

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Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 6
toggle bottom row

bookInteger 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]])
copy

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
test

Swipe to show code editor

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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 6
toggle bottom row

bookInteger 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]])
copy

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
test

Swipe to show code editor

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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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]])
copy

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
test

Swipe to show code editor

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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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