Learning IPython for Interactive Computing and Data Visualization - Second Editi电子书

Learning IPython for Interactive Computing and Data Visualization Second Edition

Table of Contents

Learning IPython for Interactive Computing and Data Visualization Second Edition

Credits

About the Author

About the Reviewers

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Getting Started with IPython

What are Python, IPython, and Jupyter?

Jupyter and IPython

What this book covers

References

Installing Python with Anaconda

Downloading Anaconda

Installing Anaconda

Before you get started...

Opening a terminal

Finding your home directory

Manipulating your system path

Testing your installation

Managing environments

Common conda commands

References

Downloading the notebooks

Introducing the Notebook

Launching the IPython console

Launching the Jupyter Notebook

The Notebook dashboard

The Notebook user interface

Structure of a notebook cell

Markdown cells

Code cells

The Notebook modal interface

Keyboard shortcuts available in both modes

Keyboard shortcuts available in the edit mode

Keyboard shortcuts available in the command mode

References

A crash course on Python

Hello world

Variables

String escaping

Lists

Loops

Indentation

Conditional branches

Functions

Positional and keyword arguments

Passage by assignment

Errors

Object-oriented programming

Functional programming

Python 2 and 3

Going beyond the basics

Ten Jupyter/IPython essentials

Using IPython as an extended shell

Learning magic commands

Mastering tab completion

Writing interactive documents in the Notebook with Markdown

Creating interactive widgets in the Notebook

Running Python scripts from IPython

Introspecting Python objects

Debugging Python code

Benchmarking Python code

Profiling Python code

Summary

2. Interactive Data Analysis with pandas

Exploring a dataset in the Notebook

Provenance of the data

Downloading and loading a dataset

Making plots with matplotlib

Descriptive statistics with pandas and seaborn

Manipulating data

Selecting data

Selecting columns

Selecting rows

Filtering with boolean indexing

Computing with numbers

Working with text

Working with dates and times

Handling missing data

Complex operations

Group-by

Joins

Summary

3. Numerical Computing with NumPy

A primer to vector computing

Multidimensional arrays

The ndarray

Vector operations on ndarrays

How fast are vector computations in NumPy?

How an ndarray is stored in memory

Why operations on ndarrays are fast

Creating and loading arrays

Creating arrays

Loading arrays from files

Basic array manipulations

Computing with NumPy arrays

Selection and indexing

Boolean operations on arrays

Mathematical operations on arrays

A density map with NumPy

Other topics

Summary

4. Interactive Plotting and Graphical Interfaces

Choosing a plotting backend

Inline plots

Exported figures

GUI toolkits

Dynamic inline plots

Web-based visualization

matplotlib and seaborn essentials

Common plots with matplotlib

Customizing matplotlib figures

Interacting with matplotlib figures in the Notebook

High-level plotting with seaborn

Image processing

Further plotting and visualization libraries

High-level plotting

Bokeh

Vincent and Vega

Plotly

Maps and geometry

The matplotlib Basemap toolkit

GeoPandas

Leaflet wrappers: folium and mplleaflet

3D visualization

Mayavi

VisPy

Summary

5. High-Performance and Parallel Computing

Accelerating Python code with Numba

Random walk

Universal functions

Writing C in Python with Cython

Installing Cython and a C compiler for Python

Implementing the Eratosthenes Sieve in Python and Cython

Distributing tasks on several cores with IPython.parallel

Direct interface

Load-balanced interface

Further high-performance computing techniques

MPI

Distributed computing

C/C++ with Python

GPU computing

PyPy

Julia

Summary

6. Customizing IPython

Creating a custom magic command in an IPython extension

Writing a new Jupyter kernel

Displaying rich HTML elements in the Notebook

Displaying SVG in the Notebook

JavaScript and D3 in the Notebook

Customizing the Notebook interface with JavaScript

Summary

Index