Data Visualization with Python and JavaScript. 2nd Edition Kyran Dale

Spis treści książki

• Preface
  • Part I: Basic Toolkit
  • Part II: Getting Your Data
  • Part III: Cleaning and Exploring Data with pandas
  • Part IV: Delivering the Data
  • Part V: Visualizing Your Data with D3 and Plotly
  • The Second Edition
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Second Edition
• Introduction
  • Who This Book Is For
    • Minimal Requirements to Use This Book
  • Why Python and JavaScript?
    • Why Not Python in the Browser?
    • Why Python for Data Processing
      • Java
      • R
      • Others
    • Pythons Getting Better All the Time
  • What Youll Learn
    • The Choice of Libraries
    • Preliminaries
  • The Dataviz Toolchain
    • 1. Scraping Data with Scrapy
    • 2. Cleaning Data with pandas
    • 3. Exploring Data with pandas and Matplotlib
    • 4. Delivering Your Data with Flask
    • 5. Transforming Data into Interactive Visualizations with Plotly and D3
    • Smaller Libraries
  • Using the Book
  • A Little Bit of Context
  • Summary
  • Recommended Books
• I. Basic Toolkit
• 1. Development Setup
  • The Accompanying Code
  • Python
    • Anaconda
    • Installing Extra Libraries
    • Virtual Environments
  • JavaScript
    • Content Delivery Networks
    • Installing Libraries Locally
  • Databases
    • Getting MongoDB Up and Running
    • Easy MongoDB with Docker
  • Integrated Development Environments
  • Summary
• 2. A Language-Learning Bridge Between Python and JavaScript
  • Similarities and Differences
  • Interacting with the Code
    • Python
    • JavaScript
  • Basic Bridge Work
    • Style Guidelines, PEP 8, and use strict
    • CamelCase Versus Underscore
    • Importing Modules, Including Scripts
    • JavaScript Modules
    • Keeping Your Namespaces Clean
    • Outputting Hello World!
    • Simple Data Processing
    • String Construction
    • Significant Whitespace Versus Curly Brackets
    • Comments and Doc-Strings
    • Declaring Variables Using let or var
    • Strings and Numbers
    • Booleans
    • Data Containers: dicts, objects, lists, Arrays
    • Functions
    • Iterating: for Loops and Functional Alternatives
    • Conditionals: if, else, elif, switch
    • File Input and Output
    • Classes and Prototypes
  • Differences in Practice
    • Method Chaining
    • Enumerating a List
    • Tuple Unpacking
    • Collections
    • Underscore
    • Functional Array Methods and List Comprehensions
    • Map, Reduce, and Filter with Pythons Lambdas
    • JavaScript Closures and the Module Pattern
  • A Cheat Sheet
  • Summary
• 3. Reading and Writing Data with Python
  • Easy Does It
  • Passing Data Around
  • Working with System Files
  • CSV, TSV, and Row-Column Data Formats
  • JSON
    • Dealing with Dates and Times
  • SQL
    • Creating the Database Engine
    • Defining the Database Tables
    • Adding Instances with a Session
    • Querying the Database
    • Easier SQL with Dataset
  • MongoDB
  • Dealing with Dates, Times, and Complex Data
  • Summary
• 4. Webdev 101
  • The Big Picture
  • Single-Page Apps
  • Tooling Up
    • The Myth of IDEs, Frameworks, and Tools
    • A Text-Editing Workhorse
    • Browser with Development Tools
    • Terminal or Command Prompt
  • Building a Web Page
    • Serving Pages with HTTP
    • The DOM
    • The HTML Skeleton
    • Marking Up Content
    • CSS
    • JavaScript
    • Data
  • Chrome DevTools
    • The Elements Tab
    • The Sources Tab
    • Other Tools
  • A Basic Page with Placeholders
  • Positioning and Sizing Containers with Flex
    • Filling the Placeholders with Content
  • Scalable Vector Graphics
    • The <g> Element
    • Circles
    • Applying CSS Styles
    • Lines, Rectangles, and Polygons
    • Text
    • Paths
    • Scaling and Rotating
    • Working with Groups
    • Layering and Transparency
    • JavaScripted SVG
  • Summary
• II. Getting Your Data
• 5. Getting Data Off the Web with Python
  • Getting Web Data with the Requests Library
  • Getting Data Files with Requests
  • Using Python to Consume Data from a Web API
    • Consuming a RESTful Web API with Requests
    • Getting Country Data for the Nobel Dataviz
  • Using Libraries to Access Web APIs
    • Using Google Spreadsheets
    • Using the Twitter API with Tweepy
  • Scraping Data
    • Why We Need to Scrape
    • Beautiful Soup and lxml
    • A First Scraping Foray
  • Getting the Soup
  • Selecting Tags
    • Crafting Selection Patterns
    • Caching the Web Pages
    • Scraping the Winners Nationalities
  • Summary
• 6. Heavyweight Scraping with Scrapy
  • Setting Up Scrapy
  • Establishing the Targets
  • Targeting HTML with Xpaths
    • Testing Xpaths with the Scrapy Shell
    • Selecting with Relative Xpaths
  • A First Scrapy Spider
  • Scraping the Individual Biography Pages
  • Chaining Requests and Yielding Data
    • Caching Pages
    • Yielding Requests
  • Scrapy Pipelines
  • Scraping Text and Images with a Pipeline
    • Specifying Pipelines with Multiple Spiders
  • Summary
• III. Cleaning and Exploring Data with pandas
• 7. Introduction to NumPy
  • The NumPy Array
    • Creating Arrays
    • Array Indexing and Slicing
    • A Few Basic Operations
  • Creating Array Functions
    • Calculating a Moving Average
  • Summary
• 8. Introduction to pandas
  • Why pandas Is Tailor-Made for Dataviz
  • Why pandas Was Developed
  • Categorizing Data and Measurements
  • The DataFrame
    • Indices
    • Rows and Columns
    • Selecting Groups
  • Creating and Saving DataFrames
    • JSON
    • CSV
    • Excel Files
    • SQL
    • MongoDB
  • Series into DataFrames
  • Summary
• 9. Cleaning Data with pandas
  • Coming Clean About Dirty Data
  • Inspecting the Data
  • Indices and pandas Data Selection
    • Selecting Multiple Rows
  • Cleaning the Data
    • Finding Mixed Types
    • Replacing Strings
    • Removing Rows
    • Finding Duplicates
    • Sorting Data
    • Removing Duplicates
    • Dealing with Missing Fields
    • Dealing with Times and Dates
  • The Full clean_data Function
  • Adding the born_in column
    • Merging DataFrames
  • Saving the Cleaned Datasets
  • Summary
• 10. Visualizing Data with Matplotlib
  • pyplot and Object-Oriented Matplotlib
  • Starting an Interactive Session
  • Interactive Plotting with pyplots Global State
    • Configuring Matplotlib
    • Setting the Figures Size
    • Points, Not Pixels
    • Labels and Legends
    • Titles and Axes Labels
    • Saving Your Charts
  • Figures and Object-Oriented Matplotlib
    • Axes and Subplots
  • Plot Types
    • Bar Charts
    • Scatter Plots
      • Adding a regression line
  • seaborn
    • FacetGrids
    • PairGrids
  • Summary
• 11. Exploring Data with pandas
  • Starting to Explore
  • Plotting with pandas
  • Gender Disparities
    • Unstacking Groups
    • Historical Trends
  • National Trends
    • Prize Winners Per Capita
    • Prizes by Category
    • Historical Trends in Prize Distribution
  • Age and Life Expectancy of Winners
    • Age at Time of Award
    • Life Expectancy of Winners
    • Increasing Life Expectancies over Time
  • The Nobel Diaspora
  • Summary
• IV. Delivering the Data
• 12. Delivering the Data
  • Serving the Data
    • Organizing Your Flask Files
    • Serving Data with Flask
  • Delivering Data Files
  • Dynamic Data with Flask APIs
    • A Simple Data API with Flask
  • Using Static or Dynamic Delivery
  • Summary
• 13. RESTful Data with Flask
  • The Tools for a RESTful Job
  • Creating the Database
  • A Flask RESTful Data Server
    • Serializing with marshmallow
  • Adding our RESTful API Routes
    • Posting Data to the API
  • Extending the API with MethodViews
  • Paginating the Data Returns
  • Deploying the API Remotely with Heroku
    • CORS
    • Consuming the API Using JavaScript
  • Summary
• V. Visualizing Your Data with D3 and Plotly
• 14. Bringing Your Charts to the Web with Matplotlib and Plotly
  • Static Charts with Matplotlib
    • Adapting to Screen Sizes
    • Using Remote Images or Assets
  • Charting with Plotly
    • Basic Charts
    • Plotly Express
    • Plotly Graph-Objects
    • Mapping with Plotly
    • Adding Custom Controls with Plotly
  • From Notebook to Web with Plotly
  • Native JavaScript Charts with Plotly
    • Fetching JSON Files
  • User-Driven Plotly with JavaScript and HTML
  • Summary
• 15. Imagining a Nobel Visualization
  • Who Is It For?
  • Choosing Visual Elements
  • Menu Bar
  • Prizes by Year
  • A Map Showing Selected Nobel Countries
  • A Bar Chart Showing Number of Winners by Country
  • A List of the Selected Winners
    • A Mini-Biography Box with Picture
  • The Complete Visualization
  • Summary
• 16. Building a Visualization
  • Preliminaries
    • Core Components
    • Organizing Your Files
    • Serving the Data
  • The HTML Skeleton
  • CSS Styling
  • The JavaScript Engine
    • Importing the Scripts
    • Modular JS with Imports
    • Basic Data Flow
    • The Core Code
    • Initializing the Nobel Prize Visualization
    • Ready to Go
    • Data-Driven Updates
    • Filtering Data with Crossfilter
      • Creating the filter
  • Running the Nobel Prize Visualization App
  • Summary
• 17. Introducing D3The Story of a Bar Chart
  • Framing the Problem
  • Working with Selections
  • Adding DOM Elements
  • Leveraging D3
  • Measuring Up with D3s Scales
    • Quantitative Scales
    • Ordinal Scales
  • Unleashing the Power of D3 with Data Binding/Joining
  • Updating the DOM with Data
  • Putting the Bar Chart Together
  • Axes and Labels
  • Transitions
    • Updating the Bar Chart
  • Summary
• 18. Visualizing Individual Prizes
  • Building the Framework
  • Scales
  • Axes
  • Category Labels
  • Nesting the Data
  • Adding the Winners with a Nested Data-Join
  • A Little Transitional Sparkle
    • Updating the Bar Chart
  • Summary
• 19. Mapping with D3
  • Available Maps
  • D3s Mapping Data Formats
    • GeoJSON
    • TopoJSON
    • Converting Maps to TopoJSON
  • D3 Geo, Projections, and Paths
    • Projections
    • Paths
    • graticules
  • Putting the Elements Together
  • Updating the Map
  • Adding Value Indicators
  • Our Completed Map
  • Building a Simple Tooltip
    • Updating the Map
  • Summary
• 20. Visualizing Individual Winners
  • Building the List
  • Building the Bio-Box
    • Updating the Winners List
  • Summary
• 21. The Menu Bar
  • Creating HTML Elements with D3
  • Building the Menu Bar
    • Building the Category Selector
    • Adding the Gender Selector
    • Adding the Country Selector
    • Wiring Up the Metric Radio Button
  • Summary
• 22. Conclusion
  • Recap
    • Part I: Basic Toolkit
    • Part II: Getting Your Data
    • Part III: Cleaning and Exploring Data with pandas
    • Part IV: Delivering the Data
    • Part V: Visualizing Your Data with D3 and Plotly
  • Future Progress
    • Visualizing Social Media Networks
    • Machine-Learning Visualizations
  • Final Thoughts
• A. D3s enter/exit Pattern
  • The enter Method
  • Accessing the Bound Data
• Index

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