Book Image

Jupyter for Data Science

By : Dan Toomey
Book Image

Jupyter for Data Science

By: Dan Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Using SciPy in Jupyter


SciPy is an open source library for mathematics, science and, engineering. With such a wide scope, there are many areas we can explore using SciPy:

  • Integration
  • Optimization
  • Interpolation
  • Fourier transforms
  • Linear algebra
  • There are several other intense sets of functionality as well, such as signal processing

Using SciPy integration in Jupyter

A standard mathematical process is integrating an equation. SciPy accomplishes this using a callback function to iteratively calculate out the integration of your function. For example, suppose that we wanted to determine the integral of the following equation:

We would use a script like the following. We are using the definition of pi from the standard math package.

from scipy.integrate import quadimport mathdef integrand(x, a, b):    return a*math.pi + ba = 2b = 1quad(integrand, 0, 1, args=(a,b))

Again, this coding is very clean and simple, yet almost impossible to do in many languages. Running this script in Jupyter we see the results...