IPython interactive computing and visualization cookbook / Cyrille Rossant.

Format:

Book

Author/Creator:

Rossant, Cyrille, author.

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (1 v.) : ill.

Edition:

1st edition

Other Title:

Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science with Python

Place of Publication:

Birmingham, [England] : Packt Publishing, 2014.

System Details:

text file

Biography/History:

Rossant Cyrille: Cyrille Rossant, PhD, is a neuroscience researcher and software engineer at University College London. He is a graduate of Ecole Normale Superieure, Paris, where he studied mathematics and computer science. He has also worked at Princeton University and College de France. While working on data science and software engineering projects, he gained experience in numerical computing, parallel computing, and high-performance data visualization. He is the author of Learning IPython for Interactive Computing and Data Visualization, Second Edition, Packt Publishing.

Summary:

Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science with Python In Detail IPython is at the heart of the Python scientific stack. With its widely acclaimed web-based notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python. IPython Interactive Computing and Visualization Cookbook contains many ready-to-use focused recipes for high-performance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; high-performance computing through dynamic compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. What You Will Learn Code better by writing high-quality, readable, and well-tested programs; profiling and optimizing your code, and conducting reproducible interactive computing experiments Master all of the new features of the IPython notebook, including the interactive HTML/JavaScript widgets Analyze data with Bayesian and frequentist statistics (Pandas, PyMC, and R), and learn from data with machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Learn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many more

Contents:

Intro

IPython Interactive Computing and Visualization Cookbook

Table of Contents

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why Subscribe?

Free Access for Packt account holders

Preface

What this book is

What this book covers

Part 1 - Advanced High-Performance Interactive Computing

Part 2 - Standard Methods in Data Science and Applied Mathematics

What you need for this book

Installing Python

GitHub repositories

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images

Errata

Piracy

Questions

1. A Tour of Interactive Computing with IPython

Introduction

What is IPython?

A brief historical retrospective on Python as a scientific environment

What's new in IPython 2.0?

Roadmap for IPython 3.0 and 4.0

References

Introducing the IPython notebook

Getting ready

How to do it...

There's more...

See also

Getting started with exploratory data analysis in IPython

Introducing the multidimensional array in NumPy for fast array computations

How it works...

Creating an IPython extension with custom magic commands

The InteractiveShell class

Loading an extension

Mastering IPython's configuration system

Configurables

Magics

Creating a simple kernel for IPython

2. Best Practices in Interactive Computing

Introduction.

Choosing (or not) between Python 2 and Python 3

Main differences in Python 3 compared to Python 2

Python 2 or Python 3?

Supporting both Python 2 and Python 3

Using 2to3

Writing code that works in Python 2 and Python 3

Efficient interactive computing workflows with IPython

The IPython terminal

IPython and text editor

The IPython notebook

Integrated Development Environments

Learning the basics of the distributed version control system Git

How to do it…

Creating a local repository

Cloning a remote repository

How it works…

There's more…

A typical workflow with Git branching

Stashing

Ten tips for conducting reproducible interactive computing experiments

Writing high-quality Python code

Writing unit tests with nose

Test coverage

Workflows with unit testing

Unit testing and continuous integration

Debugging your code with IPython

The post-mortem mode

Step-by-step debugging

GUI debuggers

3. Mastering the Notebook

What is the notebook?

The notebook ecosystem

Architecture of the IPython notebook

Connecting multiple clients to one kernel

Security in notebooks

Teaching programming in the notebook with IPython blocks

Converting an IPython notebook to other formats with nbconvert

How to do it.

How it works...

Adding custom controls in the notebook toolbar

Customizing the CSS style in the notebook

Using interactive widgets - a piano in the notebook

Creating a custom JavaScript widget in the notebook - a spreadsheet editor for pandas

Processing webcam images in real time from the notebook

4. Profiling and Optimization

Evaluating the time taken by a statement in IPython

Profiling your code easily with cProfile and IPython

Premature optimization is the root of all evil

Profiling your code line-by-line with line_profiler

How do to it...

Tracing the step-by-step execution of a Python program

Profiling the memory usage of your code with memory_profiler

Using memory_profiler for standalone Python programs

Using the %memit magic command in IPython

Other tools

Understanding the internals of NumPy to avoid unnecessary array copying

Why are NumPy arrays efficient?

What is the difference between in-place and implicit-copy operations?

Why can't some arrays be reshaped without a copy?

What are NumPy broadcasting rules?

See also.

Using stride tricks with NumPy

Implementing an efficient rolling average algorithm with stride tricks

Making efficient array selections in NumPy

Processing huge NumPy arrays with memory mapping

Manipulating large arrays with HDF5 and PyTables

Manipulating large heterogeneous tables with HDF5 and PyTables

5. High-performance Computing

CPython and concurrent programming

Compiler-related installation instructions

Linux

Mac OS X

Windows

Python 32-bit

Python 64-bit

DLL hell

Accelerating pure Python code with Numba and just-in-time compilation

Accelerating array computations with Numexpr

Wrapping a C library in Python with ctypes

Accelerating Python code with Cython

Optimizing Cython code by writing less Python and more C

Releasing the GIL to take advantage of multicore processors with Cython and OpenMP

Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA

How it works….

There's more…

Writing massively parallel code for heterogeneous platforms with OpenCL

Distributing Python code across multiple cores with IPython

Dependent parallel tasks

Alternative parallel computing solutions

Interacting with asynchronous parallel tasks in IPython

Parallelizing code with MPI in IPython

Trying the Julia language in the notebook

6. Advanced Visualization

Making nicer matplotlib figures with prettyplotlib

Creating beautiful statistical plots with seaborn

Creating interactive web visualizations with Bokeh

Visualizing a NetworkX graph in the IPython notebook with D3.js

Converting matplotlib figures to D3.js visualizations with mpld3

Getting started with Vispy for high-performance interactive data visualizations

Vispy for scientific visualization

7. Statistical Data Analysis

What is statistical data analysis?

A bit of vocabulary

Exploration, inference, decision, and prediction

Univariate and multivariate methods

Frequentist and Bayesian methods.

Parametric and nonparametric inference methods.

Notes:

Includes index.

"Quick answers to common questions." --Cover.

Description based on online resource; title from PDF title page (ebrary, viewed October 06, 2014).

ISBN:

9781783284825

178328482X

OCLC:

894504558