IPython Interactive Computing and Visualization Cookbook

封面

版权页

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Preface

What this book is

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Chapter 1. A Tour of Interactive Computing with IPython

Introduction

Introducing the IPython notebook

Getting started with exploratory data analysis in IPython

Introducing the multidimensional array in NumPy for fast array computations

Creating an IPython extension with custom magic commands

Mastering IPython's configuration system

Creating a simple kernel for IPython

Chapter 2. Best Practices in Interactive Computing

Introduction

Choosing (or not) between Python 2 and Python 3

Efficient interactive computing workflows with IPython

Learning the basics of the distributed version control system Git

A typical workflow with Git branching

Ten tips for conducting reproducible interactive computing experiments

Writing high-quality Python code

Writing unit tests with nose

Debugging your code with IPython

Chapter 3. Mastering the Notebook

Introduction

Teaching programming in the notebook with IPython blocks

Converting an IPython notebook to other formats with nbconvert

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

Chapter 4. Profiling and Optimization

Introduction

Evaluating the time taken by a statement in IPython

Profiling your code easily with cProfile and IPython

Profiling your code line-by-line with line_profiler

Profiling the memory usage of your code with memory_profiler

Understanding the internals of NumPy to avoid unnecessary array copying

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

Chapter 5. High-performance Computing

Introduction

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

Writing massively parallel code for heterogeneous platforms with OpenCL

Distributing Python code across multiple cores with IPython

Interacting with asynchronous parallel tasks in IPython

Parallelizing code with MPI in IPython

Trying the Julia language in the notebook

Chapter 6. Advanced Visualization

Introduction

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

Chapter 7. Statistical Data Analysis

Introduction

Exploring a dataset with pandas and matplotlib

Getting started with statistical hypothesis testing – a simple z-test

Getting started with Bayesian methods

Estimating the correlation between two variables with a contingency table and a chi-squared test

Fitting a probability distribution to data with the maximum likelihood method

Estimating a probability distribution nonparametrically with a kernel density estimation

Fitting a Bayesian model by sampling from a posterior distribution with a Markov chain Monte Carlo method

Analyzing data with the R programming language in the IPython notebook

Chapter 8. Machine Learning

Introduction

Getting started with scikit-learn

Predicting who will survive on the Titanic with logistic regression

Learning to recognize handwritten digits with a K-nearest neighbors classifier

Learning from text – Naive Bayes for Natural Language Processing

Using support vector machines for classification tasks

Using a random forest to select important features for regression

Reducing the dimensionality of a dataset with a principal component analysis

Detecting hidden structures in a dataset with clustering

Chapter 9. Numerical Optimization

Introduction

Finding the root of a mathematical function