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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

bookCreation Functions for 2D Arrays

Similarly to 1D arrays, NumPy has creation functions for 2D arrays. We will cover the most common one, the eye() function.

eye()

The numpy.eye() function creates a matrix in the format of a 2D array where the elements with equal row and column indices are 1, while all other elements are 0.

The two most important parameters are N and M, which specify the number of rows and columns respectively. The M parameter is optional, so you can specify only N to create a square NxN matrix.

Let’s take a look at an example:


              
1234567

            
import numpy as np
# Creating a 2x2 identity matrix
identity_matrix = np.eye(2)
print(f'2x2 identity matrix:\n{identity_matrix}')
# Creating a 4x3 matrix with np.eye()
rectangular_matrix = np.eye(4, 3, dtype=np.int8)
print(f'4x3 matrix:\n{rectangular_matrix}')
copy

In our example, we created an identity matrix by specifying only the N parameter and a rectangular matrix by specifying both N and M. We also set the dtype to np.int8 for the rectangular matrix, which can be useful when working with only integers (np.float64 is the default value for dtype).

The resulting 2D arrays look as follows:

Regarding applications, the eye() function is primarily used to create identity matrices for specific linear algebra operations and to initialize matrices in machine learning algorithms.

Note

Here is the documentation for this function: eye.

Task

  1. Use the correct function for matrix to create a matrix where the elements with equal row index and column index are 1, while all other elements are 0.
  2. Specify the first two arguments so that array_1 is a 5x2 matrix.
  3. Set the data type of array_1 elements to np.int8.

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Section 1. Chapter 5
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bookCreation Functions for 2D Arrays

Similarly to 1D arrays, NumPy has creation functions for 2D arrays. We will cover the most common one, the eye() function.

eye()

The numpy.eye() function creates a matrix in the format of a 2D array where the elements with equal row and column indices are 1, while all other elements are 0.

The two most important parameters are N and M, which specify the number of rows and columns respectively. The M parameter is optional, so you can specify only N to create a square NxN matrix.

Let’s take a look at an example:


              
1234567

            
import numpy as np
# Creating a 2x2 identity matrix
identity_matrix = np.eye(2)
print(f'2x2 identity matrix:\n{identity_matrix}')
# Creating a 4x3 matrix with np.eye()
rectangular_matrix = np.eye(4, 3, dtype=np.int8)
print(f'4x3 matrix:\n{rectangular_matrix}')
copy

In our example, we created an identity matrix by specifying only the N parameter and a rectangular matrix by specifying both N and M. We also set the dtype to np.int8 for the rectangular matrix, which can be useful when working with only integers (np.float64 is the default value for dtype).

The resulting 2D arrays look as follows:

Regarding applications, the eye() function is primarily used to create identity matrices for specific linear algebra operations and to initialize matrices in machine learning algorithms.

Note

Here is the documentation for this function: eye.

Task

  1. Use the correct function for matrix to create a matrix where the elements with equal row index and column index are 1, while all other elements are 0.
  2. Specify the first two arguments so that array_1 is a 5x2 matrix.
  3. Set the data type of array_1 elements to np.int8.

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 1. Chapter 5
toggle bottom row

bookCreation Functions for 2D Arrays

Similarly to 1D arrays, NumPy has creation functions for 2D arrays. We will cover the most common one, the eye() function.

eye()

The numpy.eye() function creates a matrix in the format of a 2D array where the elements with equal row and column indices are 1, while all other elements are 0.

The two most important parameters are N and M, which specify the number of rows and columns respectively. The M parameter is optional, so you can specify only N to create a square NxN matrix.

Let’s take a look at an example:


              
1234567

            
import numpy as np
# Creating a 2x2 identity matrix
identity_matrix = np.eye(2)
print(f'2x2 identity matrix:\n{identity_matrix}')
# Creating a 4x3 matrix with np.eye()
rectangular_matrix = np.eye(4, 3, dtype=np.int8)
print(f'4x3 matrix:\n{rectangular_matrix}')
copy

In our example, we created an identity matrix by specifying only the N parameter and a rectangular matrix by specifying both N and M. We also set the dtype to np.int8 for the rectangular matrix, which can be useful when working with only integers (np.float64 is the default value for dtype).

The resulting 2D arrays look as follows:

Regarding applications, the eye() function is primarily used to create identity matrices for specific linear algebra operations and to initialize matrices in machine learning algorithms.

Note

Here is the documentation for this function: eye.

Task

  1. Use the correct function for matrix to create a matrix where the elements with equal row index and column index are 1, while all other elements are 0.
  2. Specify the first two arguments so that array_1 is a 5x2 matrix.
  3. Set the data type of array_1 elements to np.int8.

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!

Similarly to 1D arrays, NumPy has creation functions for 2D arrays. We will cover the most common one, the eye() function.

eye()

The numpy.eye() function creates a matrix in the format of a 2D array where the elements with equal row and column indices are 1, while all other elements are 0.

The two most important parameters are N and M, which specify the number of rows and columns respectively. The M parameter is optional, so you can specify only N to create a square NxN matrix.

Let’s take a look at an example:


              
1234567

            
import numpy as np
# Creating a 2x2 identity matrix
identity_matrix = np.eye(2)
print(f'2x2 identity matrix:\n{identity_matrix}')
# Creating a 4x3 matrix with np.eye()
rectangular_matrix = np.eye(4, 3, dtype=np.int8)
print(f'4x3 matrix:\n{rectangular_matrix}')
copy

In our example, we created an identity matrix by specifying only the N parameter and a rectangular matrix by specifying both N and M. We also set the dtype to np.int8 for the rectangular matrix, which can be useful when working with only integers (np.float64 is the default value for dtype).

The resulting 2D arrays look as follows:

Regarding applications, the eye() function is primarily used to create identity matrices for specific linear algebra operations and to initialize matrices in machine learning algorithms.

Note

Here is the documentation for this function: eye.

Task

  1. Use the correct function for matrix to create a matrix where the elements with equal row index and column index are 1, while all other elements are 0.
  2. Specify the first two arguments so that array_1 is a 5x2 matrix.
  3. Set the data type of array_1 elements to np.int8.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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