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Jupyter for Data Science

You're reading from Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

Product type Paperback

Published in Oct 2017

Publisher Packt

ISBN-13 9781785880070

Length 242 pages

Edition 1st Edition

Languages

Python

Tools

Jupyter

Concepts

Data Analysis

Author (1):

Dan Toomey

View More author details

Table of Contents (11) Chapters

Preface

1. Jupyter and Data Science FREE CHAPTER

2. Working with Analytical Data on Jupyter

3. Data Visualization and Prediction

4. Data Mining and SQL Queries

5. R with Jupyter

6. Data Wrangling

7. Jupyter Dashboards

8. Statistical Modeling

9. Machine Learning Using Jupyter

10. Optimizing Jupyter Notebooks

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...

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Authors (1)

Toomey

Toomey

Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

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