NumPy Practice Questions Tutorial

Create a NumPy Array of Zeros with Shape (3, 4)
Create a NumPy Array of Ones with Shape (2, 5)
Create a NumPy Array of Random Values with Shape (4, 4)
Create a NumPy Array of Integers Between 5 and 15 with Shape (3, 5)
Create a NumPy Array of Floating-Point Numbers Between 0 and 1 with Shape (2, 3, 4)

Reshape a NumPy Array from Shape (3, 4) to Shape (4, 3)
Find the Maximum Value in a NumPy Array
Find the Minimum Value in a NumPy Array
Find the Sum of All Values in a NumPy Array
Find the Mean Value of a NumPy Array
Find the Median Value of a NumPy Array
Find the Standard Deviation of a NumPy Array

Multiply Two NumPy Arrays Element-wise
Add Two NumPy Arrays Element-wise
Subtract Two NumPy Arrays Element-wise
Divide Two NumPy Arrays Element-wise

Compute the Dot Product of Two NumPy Arrays
Compute the Cross Product of Two NumPy Arrays
Compute the Inverse of a NumPy Array
Compute the Determinant of a NumPy Array
Compute the Transpose of a NumPy Array
Compute the Rank of a NumPy Array
Compute the Eigenvalues of a NumPy Array
Compute the Eigenvectors of a NumPy Array

Concatenate Two NumPy Arrays Horizontally
Concatenate Two NumPy Arrays Vertically
Split a NumPy Array Horizontally
Split a NumPy Array Vertically

Flatten a NumPy Array
Sort a NumPy Array in Ascending Order
Sort a NumPy Array in Descending Order
Find Unique Elements in a NumPy Array

Replace Even Numbers in NumPy Array with -1
Round Elements of NumPy Array to Nearest Integer
Compute Absolute Values of NumPy Array
Create Boolean Mask for Elements Greater Than 5
Count True Values in a Boolean Mask in NumPy

Element-wise Product with Transpose in NumPy
Element-wise Sum with Transpose in NumPy
Element-wise Difference with Transpose in NumPy
Element-wise Quotient with Transpose in NumPy

Index of Maximum Value in NumPy Array
Index of Minimum Value in NumPy Array
Compute Norm of a NumPy Array

Create 3D NumPy Array of Random Values
Extract Slice from 3D NumPy Array
Compute Sum of Each Slice in 3D NumPy Array
Reshape 3D NumPy Array to 2D Array
Compute Dot Product of 2D and 1D NumPy Arrays
Apply Function to Each Element in NumPy Array

Compute the Eigenvalues of a NumPy Array

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A Compute the Eigenvalues of a NumPy Array

Short Description of the Program:
This program calculates the eigenvalues of a square matrix using NumPy’s np.linalg.eig() function.

Explanation:

np.array()
Creates a square matrix (same number of rows and columns).
np.linalg.eig(array)
Returns two outputs:
1. Eigenvalues
2. Eigenvectors (not used in this case, so we use _ to ignore it)
print()
Displays the original matrix and the computed eigenvalues.

Program:

import numpy as np

array = np.array([[4, -2],

                  [1, 1]])

eigenvalues, _ = np.linalg.eig(array)

print("Original Array:\n", array)

print("\nEigenvalues of the Array:", eigenvalues)

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NumPy Practice Questions Tutorial

NumPy Practice Questions Tutorial

Compute the Eigenvalues of a NumPy Array

np.array()
Creates a square matrix (same number of rows and columns).

np.linalg.eig(array)
Returns two outputs:

Eigenvalues

Eigenvectors (not used in this case, so we use _ to ignore it)

print()
Displays the original matrix and the computed eigenvalues.

Important Links

NumPy Practice Questions Tutorial

NumPy Practice Questions Tutorial

Basics of Array Creation

Array Reshaping & Structure

Element-wise Operations

Matrix Operations

Concatenation & Splitting

Array Manipulation

Boolean & Masking

Transpose Operations

Advanced Indexing & Norms

3D Array Operations

Compute the Eigenvalues of a NumPy Array

np.array() Creates a square matrix (same number of rows and columns).

np.linalg.eig(array) Returns two outputs:

Eigenvalues

Eigenvectors (not used in this case, so we use _ to ignore it)

print() Displays the original matrix and the computed eigenvalues.

Important Links

np.array()
Creates a square matrix (same number of rows and columns).

np.linalg.eig(array)
Returns two outputs:

print()
Displays the original matrix and the computed eigenvalues.