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Please enable JavaScript in your browser settings or update your browser.Sorting 2D Arrays | Commonly used NumPy Functions
Ultimate NumPy
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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

bookSorting 2D Arrays

Here is an example of sorting a 2D array:

As you can see, simply passing our 2D array to the sort() function sorts each 1D array along the axis 1 (which is the default option in a 2D array). Setting axis=0 sorts each 1D array along the axis 0 (every column). Setting axis=None returns a contiguous sorted 1D array of all the elements of the 2D array.

Let’s now have a look at the code for our example:


              
12345678910

            
import numpy as np
array_2d = np.array([[2, 9, 3], [1, 6, 4], [5, 7, 8]])
# Sorting a 2D array along axis 1
print(np.sort(array_2d))
print('-' * 20)
# Sorting a 2D array along axis 0
print(np.sort(array_2d, axis=0))
print('-' * 20)
# Creating a 1D sorted array out of the elements of array_2d
print(np.sort(array_2d, axis=None))
copy

Sorting 2D Arrays in Descending Order

When sorting 2D arrays in descending order along a given axis, you need to use two slices: one full slice ([:]) and another with a negative step ([::-1]). The position of the slice with the negative step should correspond to the axis along which you are sorting.

Note

When sorting along axis 0, you can use only a slice with a negative step, as it already indexes along this axis.

Here is an example:

Let’s now have a look at the code for our example:


              
12345678910

            
import numpy as np
array_2d = np.array([[2, 9, 3], [1, 6, 4], [5, 7, 8]])
# Sorting a 2D array along axis 1 in descending order
print(np.sort(array_2d)[:, ::-1])
print('-' * 20)
# Sorting a 2D array along axis 0 in descending order
print(np.sort(array_2d, axis=0)[::-1])
print('-' * 20)
# Creating a 1D sorted array out of the elements of array_2d in descending order
print(np.sort(array_2d, axis=None)[::-1])
copy

You can always use NumPy documentation for reference: numpy.sort, ndarray.sort.

Task
test

Swipe to show code editor

You have a 2D array named exam_scores containing the scores for each exam from a certain subject. Each column represents a specific subject, and each row represents an individual student. Thus, a specific row displays the scores of that student for each exam. Your task is the following:

  1. Create a 2D NumPy array named top_scores_subject based on exam_scores where each column representing a certain subject should be sorted by scores in descending order:

    • use the appropriate NumPy function for sorting;
    • specify the correct array to sort as the first argument;
    • specify the second keyword argument to sort every column;
    • use the correct slices for descending order.

  2. Create a 1D NumPy array named sorted_scores based on exam_scores which contains all scores sorted in ascending order:

    • use the appropriate NumPy function for sorting;
    • specify the correct array to sort as the first argument;
    • specify the second keyword argument to create a contiguous sorted 1D array.

By doing so, we can easily see the highest score for each exam and the lowest scores out of all exams.

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 3. Chapter 2
toggle bottom row

bookSorting 2D Arrays

Here is an example of sorting a 2D array:

As you can see, simply passing our 2D array to the sort() function sorts each 1D array along the axis 1 (which is the default option in a 2D array). Setting axis=0 sorts each 1D array along the axis 0 (every column). Setting axis=None returns a contiguous sorted 1D array of all the elements of the 2D array.

Let’s now have a look at the code for our example:


              
12345678910

            
import numpy as np
array_2d = np.array([[2, 9, 3], [1, 6, 4], [5, 7, 8]])
# Sorting a 2D array along axis 1
print(np.sort(array_2d))
print('-' * 20)
# Sorting a 2D array along axis 0
print(np.sort(array_2d, axis=0))
print('-' * 20)
# Creating a 1D sorted array out of the elements of array_2d
print(np.sort(array_2d, axis=None))
copy

Sorting 2D Arrays in Descending Order

When sorting 2D arrays in descending order along a given axis, you need to use two slices: one full slice ([:]) and another with a negative step ([::-1]). The position of the slice with the negative step should correspond to the axis along which you are sorting.

Note

When sorting along axis 0, you can use only a slice with a negative step, as it already indexes along this axis.

Here is an example:

Let’s now have a look at the code for our example:


              
12345678910

            
import numpy as np
array_2d = np.array([[2, 9, 3], [1, 6, 4], [5, 7, 8]])
# Sorting a 2D array along axis 1 in descending order
print(np.sort(array_2d)[:, ::-1])
print('-' * 20)
# Sorting a 2D array along axis 0 in descending order
print(np.sort(array_2d, axis=0)[::-1])
print('-' * 20)
# Creating a 1D sorted array out of the elements of array_2d in descending order
print(np.sort(array_2d, axis=None)[::-1])
copy

You can always use NumPy documentation for reference: numpy.sort, ndarray.sort.

Task
test

Swipe to show code editor

You have a 2D array named exam_scores containing the scores for each exam from a certain subject. Each column represents a specific subject, and each row represents an individual student. Thus, a specific row displays the scores of that student for each exam. Your task is the following:

  1. Create a 2D NumPy array named top_scores_subject based on exam_scores where each column representing a certain subject should be sorted by scores in descending order:

    • use the appropriate NumPy function for sorting;
    • specify the correct array to sort as the first argument;
    • specify the second keyword argument to sort every column;
    • use the correct slices for descending order.

  2. Create a 1D NumPy array named sorted_scores based on exam_scores which contains all scores sorted in ascending order:

    • use the appropriate NumPy function for sorting;
    • specify the correct array to sort as the first argument;
    • specify the second keyword argument to create a contiguous sorted 1D array.

By doing so, we can easily see the highest score for each exam and the lowest scores out of all exams.

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 3. Chapter 2
toggle bottom row

bookSorting 2D Arrays

Here is an example of sorting a 2D array:

As you can see, simply passing our 2D array to the sort() function sorts each 1D array along the axis 1 (which is the default option in a 2D array). Setting axis=0 sorts each 1D array along the axis 0 (every column). Setting axis=None returns a contiguous sorted 1D array of all the elements of the 2D array.

Let’s now have a look at the code for our example:


              
12345678910

            
import numpy as np
array_2d = np.array([[2, 9, 3], [1, 6, 4], [5, 7, 8]])
# Sorting a 2D array along axis 1
print(np.sort(array_2d))
print('-' * 20)
# Sorting a 2D array along axis 0
print(np.sort(array_2d, axis=0))
print('-' * 20)
# Creating a 1D sorted array out of the elements of array_2d
print(np.sort(array_2d, axis=None))
copy

Sorting 2D Arrays in Descending Order

When sorting 2D arrays in descending order along a given axis, you need to use two slices: one full slice ([:]) and another with a negative step ([::-1]). The position of the slice with the negative step should correspond to the axis along which you are sorting.

Note

When sorting along axis 0, you can use only a slice with a negative step, as it already indexes along this axis.

Here is an example:

Let’s now have a look at the code for our example:


              
12345678910

            
import numpy as np
array_2d = np.array([[2, 9, 3], [1, 6, 4], [5, 7, 8]])
# Sorting a 2D array along axis 1 in descending order
print(np.sort(array_2d)[:, ::-1])
print('-' * 20)
# Sorting a 2D array along axis 0 in descending order
print(np.sort(array_2d, axis=0)[::-1])
print('-' * 20)
# Creating a 1D sorted array out of the elements of array_2d in descending order
print(np.sort(array_2d, axis=None)[::-1])
copy

You can always use NumPy documentation for reference: numpy.sort, ndarray.sort.

Task
test

Swipe to show code editor

You have a 2D array named exam_scores containing the scores for each exam from a certain subject. Each column represents a specific subject, and each row represents an individual student. Thus, a specific row displays the scores of that student for each exam. Your task is the following:

  1. Create a 2D NumPy array named top_scores_subject based on exam_scores where each column representing a certain subject should be sorted by scores in descending order:

    • use the appropriate NumPy function for sorting;
    • specify the correct array to sort as the first argument;
    • specify the second keyword argument to sort every column;
    • use the correct slices for descending order.

  2. Create a 1D NumPy array named sorted_scores based on exam_scores which contains all scores sorted in ascending order:

    • use the appropriate NumPy function for sorting;
    • specify the correct array to sort as the first argument;
    • specify the second keyword argument to create a contiguous sorted 1D array.

By doing so, we can easily see the highest score for each exam and the lowest scores out of all exams.

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!

Here is an example of sorting a 2D array:

As you can see, simply passing our 2D array to the sort() function sorts each 1D array along the axis 1 (which is the default option in a 2D array). Setting axis=0 sorts each 1D array along the axis 0 (every column). Setting axis=None returns a contiguous sorted 1D array of all the elements of the 2D array.

Let’s now have a look at the code for our example:


              
12345678910

            
import numpy as np
array_2d = np.array([[2, 9, 3], [1, 6, 4], [5, 7, 8]])
# Sorting a 2D array along axis 1
print(np.sort(array_2d))
print('-' * 20)
# Sorting a 2D array along axis 0
print(np.sort(array_2d, axis=0))
print('-' * 20)
# Creating a 1D sorted array out of the elements of array_2d
print(np.sort(array_2d, axis=None))
copy

Sorting 2D Arrays in Descending Order

When sorting 2D arrays in descending order along a given axis, you need to use two slices: one full slice ([:]) and another with a negative step ([::-1]). The position of the slice with the negative step should correspond to the axis along which you are sorting.

Note

When sorting along axis 0, you can use only a slice with a negative step, as it already indexes along this axis.

Here is an example:

Let’s now have a look at the code for our example:


              
12345678910

            
import numpy as np
array_2d = np.array([[2, 9, 3], [1, 6, 4], [5, 7, 8]])
# Sorting a 2D array along axis 1 in descending order
print(np.sort(array_2d)[:, ::-1])
print('-' * 20)
# Sorting a 2D array along axis 0 in descending order
print(np.sort(array_2d, axis=0)[::-1])
print('-' * 20)
# Creating a 1D sorted array out of the elements of array_2d in descending order
print(np.sort(array_2d, axis=None)[::-1])
copy

You can always use NumPy documentation for reference: numpy.sort, ndarray.sort.

Task
test

Swipe to show code editor

You have a 2D array named exam_scores containing the scores for each exam from a certain subject. Each column represents a specific subject, and each row represents an individual student. Thus, a specific row displays the scores of that student for each exam. Your task is the following:

  1. Create a 2D NumPy array named top_scores_subject based on exam_scores where each column representing a certain subject should be sorted by scores in descending order:

    • use the appropriate NumPy function for sorting;
    • specify the correct array to sort as the first argument;
    • specify the second keyword argument to sort every column;
    • use the correct slices for descending order.

  2. Create a 1D NumPy array named sorted_scores based on exam_scores which contains all scores sorted in ascending order:

    • use the appropriate NumPy function for sorting;
    • specify the correct array to sort as the first argument;
    • specify the second keyword argument to create a contiguous sorted 1D array.

By doing so, we can easily see the highest score for each exam and the lowest scores out of all exams.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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