Book Image

Dancing with Python

By : Robert S. Sutor
Book Image

Dancing with Python

By: Robert S. Sutor

Overview of this book

Dancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files. The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning. By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way.
Table of Contents (29 chapters)
Part I: Getting to Know Python
PART II: Algorithms and Circuits
PART III: Advanced Features and Libraries
Other Books You May Enjoy
Appendix C: The Complete UniPoly Class
Appendix D: The Complete Guitar Class Hierarchy
Appendix F: Production Notes

15.4 Feature selection and reduction

Recall that our df DataFrame has 5 features/columns/dimensions. Do we need all these features for our analysis, or are any of them redundant?

In our df DataFrame, we do not need both the F and M columns. When a column entry in one is 0, the entry in the other is 1, and vice versa. This is easy to see, but other and more subtle relationships may exist among columns.

For example, if a, b, and c are floating-point numbers and X, Y, and Z are columns, we might have z = a x + b y + c for each value x in X, y in Y, and z in Z in the same row. In column notation, Z = a X + b Y + c.

Exercise 15.11

Show that F = – M + 1.

Exercise 15.12

Interpret this correlation coefficient matrix for the F and M columns:

df[['F', 'M']].corr(method="pearson")
        F       M