Python与JavaScript打造动态数据可视化实战指南

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《数据可视化：Python与JavaScript实战》是一本由Kyran Dale编著的书籍，它针对日益增长的数据驱动需求，教你如何利用Python和JavaScript的强大功能将原始数据转化为动态、互动的网络可视化。本书适合开发者学习，特别是那些需要处理不规范或格式不一致的数据，并将其转化为可视化内容的人员。该书的主要内容包括以下几个方面： 1. **数据获取**：Kyran Dale教授如何使用Python的网络爬虫工具或Web APIs，程序化地获取数据，确保数据来源的多样性和实时性。 2. **数据清洗与处理**：书中深入讲解了如何利用Python中的重量级数据处理库（如Pandas和NumPy）对数据进行清洗、预处理和分析，确保数据质量。 3. **轻量级服务器搭建**：通过Flask框架，作者展示了如何在Python中创建一个轻量级服务器，以便向浏览器推送处理后的数据，实现前后端分离。 4. **Web可视化技术**：重点介绍了如何使用D3.js（一个强大的JavaScript库）来创建交互式图形，同时还会涉及Canvas和WebGL，展示不同层次的视觉效果和性能优化。 5. **实践与案例研究**：书中提供了丰富的实例和最佳实践，帮助读者逐步掌握数据采集、处理和呈现的完整流程，通过实际操作提升技能。 6. **版本更新**：由于是早期发布电子书，读者可以及时获取作者在写作过程中的更新，包括新章节和最终版的整合，确保始终掌握最新技术动态。《数据可视化与Python和JavaScript》是一本紧跟行业趋势的实用指南，不仅适合初学者入门，也对有经验的开发人员提供深入的学习材料，帮助他们在数据驱动的世界中打造引人入胜的网络可视化体验。版权信息表明，此书享有2016年Kyran Dale的版权，由O'Reilly Media出版，读者可以通过多种渠道获取，如在线书店或者联系公司销售部门获取更多信息。

The Choice of Libraries

I had three things in mind while choosing the libraries used in the

book.

1. Open-source and free as in beer - you shouldn’t have to invest

any extra money to learn with this book.

Longevity - generally well-established, community-driven and

popular.

3. Best-of-breed, allowing for 2. - at the sweet-spot between popu‐

larity and utility.

The skills you learn here should be relevant for a long time. Gener‐

ally the obvious candidates have been chosen, libraries that write

their own ticket as it were. Where appropriate I will highlight the

alternative choices and give a rationale for my selection.

Preliminaries

A few preliminary chapters are needed before beginning the trans‐

formative journey of our Nobel data-set through the toolchain.

These aim to cover the basic skills necessary to make the rest tool‐

chain chapters run more fluidly. The first few chapters cover the fol‐

lowing:

• Building a Language bridge between Python and JavaScript.

• Covering the Basic Web-dev needed by the book.

• How to pass data around with Python, through various file-

formats and databases.

These chapters are part-tutorial, part-reference and it’s fine to skip

straight to the beginning of the toolchain, dipping back where

needed.

The Dataviz Toolchain

The main part of the book demonstrates the data-visualisation tool‐

chain, which follows the journey of a data-set (of Nobel-prize win‐

ners) from raw, freshly scraped data to engaging, interactive

JavaScript visualisation. During the process of collection, refinement

and transformation a number of big libraries are demonstrated,

summarised in Figure P-2. These libraries are the industrial lathes of

xiv | Introduction

2. Cleaning data with Pandas

The dirty secret of data-viz is that pretty much all data is dirty and

turning it into something you can use may well occupy a lot more

time than anticipated. This is an unglamourous process which can

easily steal over half your time. Which is all the more reason to get

good at it and use the right tools.

Pandas is a huge player in the Python data-processing ecosystem. It’s

a Python data-analysis library whose chief component is the Data‐

Frame, essentially a programmatic spreadsheet. Pandas extends

Numpy, Python’s powerful numeric library, into the realm of hetero‐

geneous data-sets, the kind of categorical, temporal, ordinal etc.

information that data-visualisers have to deal with. As well as being

great for interactively exploring your data (using its built-in Mat‐

plotlib plots), Pandas is well-suited to the drudge-work of cleaning

data, making it easy to locate duplicate records, fix dodgy date-

strings, find missing fields etc..

3. Exploring data with Pandas and Matplotlib

Before beginning the transformation of your data into a visualisa‐

tion you need to understand it. The patterns, trends, anomalies etc.

hidden in the data will inform the stories you are trying to tell with

it, whether that’s explaining a recent rise in year-on-year widget sales

or demonstrating global climate change.

In conjunction with IPython, the Python interpreter on steroids,

Pandas and Matplolib (with additions such as Seaborn) provide a

great way to explore your data interactively, generating rich, inline

plots from the command-line, slicing and dicing your data to reveal

interesting patterns. The results of these explorations can then be

easily saved to file or database to be passed on to your JavaScript vis‐

ualisation.

4. Delivering your data with Flask

Once you’ve explored and refined your data you’ll need to serve it to

the web-browser, where a JavaScript library like D3 can transform it.

One of the great strengths of using a general purpose language like

Python is that it’s as comfortable rolling a web-server in a few lines

of code as it is crunching through large datasets with special-

xvi | Introduction

• SQLAlchemy: the best Python SQL Toolkit and Object Relational

Mapper (ORM) there is. It’s feature rich and makes working

with the various SQL-based databases a relative breeze.

• Seaborn: a great addition to Python’s plotting powerhouse Mat‐

plotlib, adding some very useful plot-types including some stat‐

istical ones of particular use to data-visualisers. It also adds

arguably superior aesthetics, over-riding the Matplotlib defaults.

•

crossfilter: although JavaScript’s data-processing libraries are

a work-in-progress, a few really useful ones have emerged

recently with crossfilter being a stand-out. It enables very

fast filtering of row-columnar datasets and is ideally suited to

data-viz work, unsurprising as one of it’s creators is Mike

Bostock, the father of D3.

A Little Bit of Context

This is a practical book and assumes that the reader has a pretty

good idea what he or she wants to visualise, how that visualization

should look and feel and a desire to get cracking on, unencumbered

by too much theory. Nevertheless, drawing on the history of data-

visualisation can both clarify the central themes of the book and add

valuable context. It can also help explain why now is such an excit‐

ing time to be entering the field, as technological innovation is driv‐

ing novel data-viz forms and people are grappling with the problem

of presenting the increasing amount of multi-dimensional data gen‐

erated by the internet.

Data visualisation has an impressive body of theory behind it and

there are some great books out there that I would recommend you

read (see ??? on page 21 for a little selection). The practical benefit of

understanding the way humans visually harvest information cannot

be overstated. It can be easily demonstrated, for example, that a pie-

chart is almost always a bad way of presenting comparative data and

a simple bar-chart far preferable. By conducting psychometric

experiments, we now have a pretty good idea how to trick the

human visual system and make the relationships in the data harder

to grasp. Conversely we can show that some visual forms are close to

optimal for amplifying contrast. The literature, at its very least, pro‐

vides some useful rules of thumb which suggest good candidates for

any particular data narrative.

xviii | Introduction

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