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

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22人正在读 | 0人评论 9.8

作       者:Cyrille Rossant

出  版  社:Packt Publishing

出版时间:2015-10-21

字       数:147.2万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This BookLearn the basics of Python in the Jupyter NotebookAnalyze and visualize data with pandas, NumPy, matplotlib, and seabornPerform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.What You Will LearnInstall Anaconda and code in Python in the Jupyter NotebookLoad and explore datasets interactivelyPerform complex data manipulations effectively with pandasCreate engaging data visualizations with matplotlib and seabornSimulate mathematical models with NumPyVisualize and process images interactively in the Jupyter Notebook with scikit-imageAccelerate your code with Numba, Cython, and IPython.parallelExtend the Notebook interface with HTML, JavaScript, and D3 In Detail Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.Style and approach This is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.
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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

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

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

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