Book Image

IPython Notebook Essentials

By : Martins
Book Image

IPython Notebook Essentials

By: Martins

Overview of this book

If you are a professional, student, or educator who wants to learn to use IPython Notebook as a tool for technical and scientific computing, visualization, and data analysis, this is the book for you. This book will prove valuable for anyone that needs to do computations in an agile environment.

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IPython Notebook Essentials
Martins
Nov, 2014 | 190 pages
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Table of Contents (15 chapters)
IPython Notebook Essentials
IPython Notebook Essentials
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
A Tour of the IPython Notebook
A Tour of the IPython Notebook
Getting started with Anaconda or Wakari
Running the notebook
Example – the coffee cooling problem
Exercises
Summary
2
The Notebook Interface
The Notebook Interface
Editing and navigating a notebook
IPython magics
Interacting with the operating system
Running scripts, loading data, and saving data
The rich display system
Summary
3
Graphics with matplotlib
Graphics with matplotlib
The plot function
Three-dimensional plots
Animations
Summary
4
Handling Data with pandas
Handling Data with pandas
The Series class
The DataFrame class
Computational and graphics tools
An example with a realistic dataset
Summary
5
Advanced Computing with SciPy, Numba, and NumbaPro
Advanced Computing with SciPy, Numba, and NumbaPro
Overview of SciPy
Advanced mathematical algorithms with SciPy
Accelerating computations with Numba and NumbaPro
Summary
IPython Notebook Reference Card
IPython Notebook Reference Card
Starting the notebook
Keyboard shortcuts
Importing modules
Getting help
A Brief Review of Python
A Brief Review of Python
Introduction
Basic types, expressions, and variables and their assignment
Sequence types
Dictionaries
Control structures
Summary
NumPy Arrays
NumPy Arrays
Introduction
Array creation and member access
Indexing and Slicing
Index
Index

Chapter 5. Advanced Computing with SciPy, Numba, and NumbaPro

In this chapter, the user will learn how to use SciPy to perform scientific computations. The Numba package will then be introduced as a way to accelerate computations. Finally, the NumbaPro capabilities of parallel execution in the GPU will be presented.

In this chapter, we will cover the following topics:

  • Overview of SciPy

  • Advanced mathematical algorithms with SciPy

  • Accelerating computations with Numba and NumbaPro

Before running the examples in this chapter, load pylab by running the following command in a computing cell:

%pylab inline
Previous Section
End of Section 1
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