Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
Online Resources

Linear algebra with NumPy


Linear algebra is an important subdivision of mathematics. We can use linear algebra, for instance, to perform linear regression. The numpy.linalg subpackage holds linear algebra routines. With this subpackage, you can invert matrices, compute eigenvalues, solve linear equations, and find determinants, among other things. Matrices in NumPy are represented by a subclass of ndarray.

Inverting matrices with NumPy

The inverse of a square and invertible matrix A in linear algebra is the matrix A-1 , which, when multiplied with the original matrix, is equal to the identity matrix I. This can be written down as the following mathematical equation:

A A-1 = I 

The inv() function in the numpy.linalg subpackage can do this for us. Let's invert an example matrix. To invert matrices, follow these steps:

  1. Create the demonstration matrix with the mat() function:

            A = np.mat("2 4 6;4 2 6;10 -4 18")
             print("A\n", A)

    The A matrix is printed as follows:

             ...