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Mastering Eigenvalues and Eigenvectors with Numpy linalgeig

Numpy linalg.eig function: Understanding Eigenvalues and Eigenvectors

The world of linear algebra is vast, and there are numerous concepts and theorems to understand. Eigenvalues and Eigenvectors are two of the most crucial and widely used concepts in linear algebra.

These concepts have a significant role in data analysis, machine learning, physics, and engineering. In this article, we will provide a comprehensive guide to Numpy linalg.eig function and help you to understand Eigenvalues and Eigenvectors.

Eigenvalues and Eigenvectors: Definition and Significance

Eigenvalues and Eigenvectors are mainly associated with square matrices. In linear algebra, a matrix is a rectangular array of numbers.

A square matrix is a matrix

where the number of rows and columns is the same. An Eigenvalue is a scalar quantity that represents how a linear transformation changes in direction and magnitude.

An Eigenvector, on the other hand, is a non-zero vector that remains in the same direction when a linear transformation is applied to it. Eigenvalues and Eigenvectors are closely related and always exist in pairs.

An Eigenvector can be multiplied by an Eigenvalue, and the result is the transformed vector. Eigenvalues and Eigenvectors are essential because they describe how a system changes and how much variance is captured in the system’s data.

Eigenvalues help in finding solutions to linear equations, and Eigenvectors play a vital role in reducing dimensions in data analysis.

Use of Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors have various applications in different fields. In data analysis, they are used to reduce dimensions to simplify data sets and make data more manageable.

They are also used in image compression,

where the original image can be reconstructed from the compressed data through the use of Eigenvectors and Eigenvalues. In physics, Eigenvalues and Eigenvectors are used to calculate the angular momentum of particles and create quantum states.

In engineering, Eigenvalues and Eigenvectors are used to calculate the natural frequencies and mode shapes of structures.

Required Conditions for Calculating Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors can only be calculated for square matrices, i.e., matrices that have the same number of rows as columns. The size of the matrix, known as the dimension, is generally represented as nXn,

where n is the number of rows and columns.

Numpy linalg Library and the linalg.eig() function

The NumPy library is used for scientific computing in Python. It is widely used for data analysis, data manipulation, and numerical computing.

NumPy has a sub-library called ‘linalg’, which stands for linear algebra. The ‘linalg’ sub-library has several functions that can be used for computing Eigenvalues and Eigenvectors.

The linalg.eig() function is one of the most commonly used functions in the ‘linalg’ library for computing Eigenvalues and Eigenvectors. It takes an array as input and returns two outputs: an array of Eigenvalues and an array of Eigenvectors.

Syntax and Parameters of linalg.eig() function

The syntax of the linalg.eig() function is as follows:



a: Array or matrix to be computed for Eigenvalues and Eigenvectors. The function returns two values:

w: Eigenvalues,

where each Eigenvalue corresponds to the column index of the Eigenvector.

v: Eigenvectors,

where each column of the Eigenvector matrix corresponds to an Eigenvalue. The linalg.eig() function can take complex values as inputs, and it can also return normalized Eigenvectors if specified as a parameter in the function.

Example of how to use the linalg.eig() function

To illustrate the linalg.eig() function, let us consider an example using a pre-defined matrix. Suppose we have a matrix A, as shown below:

import numpy as np

A = np.array([[3, 1], [2, 2]])

w, v = np.linalg.eig(A)

The above code will return an array of Eigenvalues and an array of Eigenvectors. The Eigenvalues will be displayed as an array, while the Eigenvectors will be displayed as a matrix.

print(‘Eigenvalues:’, w)

print(‘Eigenvectors:’, v)

The output will be:

Eigenvalues: [ 4. 1.]

Eigenvectors: [[ 0.70710678 -0.4472136 ]

[ 0.70710678 0.89442719]]

We can also create a user-input matrix and compute the Eigenvalues and Eigenvectors using the linalg.eig() function.

import numpy as np

user_matrix = []

n = int(input(“Enter the dimension of the matrix: “))

for i in range(n):

row = [int(x) for x in input(f”Enter the values in row {i+1}: “).split()]


matrix = np.array(user_matrix)

w, v = np.linalg.eig(matrix)

print(‘Eigenvalues:’, w)

print(‘Eigenvectors:’, v)

The above code will take user input, create a matrix, and return the Eigenvalues and Eigenvectors of the matrix.


In conclusion, Eigenvalues and Eigenvectors are crucial concepts in linear algebra that have practical applications in various fields. The linalg.eig() function in the NumPy linalg library is a powerful tool for computing Eigenvalues and Eigenvectors.

With this comprehensive guide, you should feel confident in using the linalg.eig() function to solve complex problems that involve Eigenvalues and Eigenvectors.

3) Shortcomings of Eigenvalues and Eigenvectors

While Eigenvalues and Eigenvectors have numerous applications and are frequently used in many fields, including physics, engineering, and data analysis, they have some limitations in terms of their capabilities in handling complex problems.

Limitations of Eigenvalues and Eigenvectors

1. Limitations in Linear Transformations:

Eigenvalues are only applicable when calculating linear transformations.

This means that the effect on a given vector by a transformation is limited to a single dimension. However, most data sets are not linear, and non-linear approaches are required to represent the data accurately.

2. Time-Consuming Calculations:

Calculating Eigenvalues and Eigenvectors can be time-consuming, particularly for large or complex data sets.

The computational load can be overwhelming and may require more computing power, which could increase the overall cost of the analysis. 3.

Linear Dependence:

Eigenvalues and Eigenvectors can only be calculated for linearly independent vectors. When a data set has linearly dependent vectors, there will be more than one set of Eigenvalues and Eigenvectors that could represent the data set.

This scenario is referred to as the singularity problem and can make it challenging to accurately capture the true Eigenvalues and Eigenvectors of the data set. 4.

Algebraic and Geometric Multiplicity:

Eigenvalues and Eigenvectors have limitations when it comes to algebraic and geometric multiplicity. Algebraic multiplicity represents how many times an Eigenvalue appears in the spectrum of the data set’s transformation matrix.

In contrast, geometric multiplicity counts the number of Eigenvectors associated with a particular Eigenvalue. If the dimensions of the spectra don’t match, it can pose limitations when calculating Eigenvalues and Eigenvectors.



In conclusion, Eigenvalues and Eigenvectors are valuable mathematical concepts that are used extensively in various fields, including physics, engineering, and data analysis. However, they have limitations when it comes to handling complex problems.

While the Numpy linalg.eig function provides insight into the data’s structure and relationships, it is essential to consider the limitations when interpreting the results. Understanding these limitations and the impact they can have on the analysis can lead to more accurate and insightful evaluations of data.

Eigenvalues and Eigenvectors are critical concepts in linear algebra, and the NumPy linalg.eig function is a powerful tool that supports their computation. The article explained the definition and significance of Eigenvalues and Eigenvectors, their use in data analysis, physics, and engineering, and the necessary conditions for their calculation.

The article also discussed the limitations of Eigenvalues and Eigenvectors, including their limitations in linear transformations, the risk of time-consuming calculations, linear dependence, and algebraic and geometric multiplicity. It is essential to understand these limitations when interpreting their results, which can lead to data insight and more accurate evaluations.

By understanding Eigenvalues and Eigenvectors and how to use the linalg.eig function, a comprehensive approach to data analysis can be executed.

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