Books & Videos

Table of Contents

  1. Chapter 6 Development Setup

    1. The Accompanying Code

    2. Python

    3. JavaScript

    4. Databases

    5. Integrated Development Environments

    6. Summary

  2. Basic Toolkit

    1. Chapter 7 A Language-Learning Bridge Between Python and JavaScript

      1. Similarities and Differences
      2. Interacting with the Code
      3. Basic Bridge Work
      4. Differences in Practice
      5. A Cheat Sheet
      6. Summary
    2. Chapter 8 Reading and Writing Data with Python

      1. Easy Does It
      2. Passing Data Around
      3. Working with System Files
      4. CSV, TSV, and Row-Column Data Formats
      5. JSON
      6. SQL
      7. MongoDB
      8. Dealing with Dates, Times, and Complex Data
      9. Summary
    3. Chapter 9 Webdev 101

      1. The Big Picture
      2. Single-Page Apps
      3. Tooling Up
      4. Building a Web Page
      5. Chrome’s Developer Tools
      6. A Basic Page with Placeholders
      7. Scalable Vector Graphics
      8. Summary
  3. Getting Your Data

    1. Chapter 10 Getting Data off the Web with Python

      1. Getting Web Data with the requests Library
      2. Getting Data Files with requests
      3. Using Python to Consume Data from a Web API
      4. Using Libraries to Access Web APIs
      5. Scraping Data
      6. Getting the Soup
      7. Selecting Tags
      8. Summary
    2. Chapter 11 Heavyweight Scraping with Scrapy

      1. Setting Up Scrapy
      2. Establishing the Targets
      3. Targeting HTML with Xpaths
      4. A First Scrapy Spider
      5. Scraping the Individual Biography Pages
      6. Chaining Requests and Yielding Data
      7. Scrapy Pipelines
      8. Scraping Text and Images with a Pipeline
      9. Summary
  4. Cleaning and Exploring Data with Pandas

    1. Chapter 12 Introduction to NumPy

      1. The NumPy Array
      2. Creating Array Functions
      3. Summary
    2. Chapter 13 Introduction to Pandas

      1. Why Pandas Is Tailor-Made for Dataviz
      2. Why Pandas Was Developed
      3. Heterogeneous Data and Categorizing Measurements
      4. The DataFrame
      5. Creating and Saving DataFrames
      6. Series into DataFrames
      7. Panels
      8. Summary
    3. Chapter 14 Cleaning Data with Pandas

      1. Coming Clean About Dirty Data
      2. Inspecting the Data
      3. Indices and Pandas Data Selection
      4. Cleaning the Data
      5. The Full clean_data Function
      6. Saving the Cleaned Dataset
      7. Summary
    4. Chapter 15 Visualizing Data with Matplotlib

      1. Pyplot and Object-Oriented Matplotlib
      2. Starting an Interactive Session
      3. Interactive Plotting with Pyplot’s Global State
      4. Figures and Object-Oriented Matplotlib
      5. Plot Types
      6. Seaborn
      7. Summary
    5. Chapter 16 Exploring Data with Pandas

      1. Starting to Explore
      2. Plotting with Pandas
      3. Gender Disparities
      4. National Trends
      5. Age and Life Expectancy of Winners
      6. The Nobel Diaspora
      7. Summary
  5. Delivering the Data

    1. Chapter 17 Delivering the Data

      1. Serving the Data
      2. Delivering Static Files
      3. Dynamic Data with Flask
      4. Using Static or Dynamic Delivery
      5. Summary
    2. Chapter 18 RESTful Data with Flask

      1. A RESTful, MongoDB API with Eve
      2. Delivering Data to the Nobel Prize Visualization
      3. RESTful SQL with Flask-Restless
      4. Summary
  6. Visualizing Your Data with D3

    1. Chapter 19 Imagining a Nobel Visualization

      1. Who Is It For?
      2. Choosing Visual Elements
      3. Menu Bar
      4. Prizes by Year
      5. A Map Showing Selected Nobel Countries
      6. A Bar Chart Showing Number of Winners by Country
      7. A List of the Selected Winners
      8. The Complete Visualization
      9. Summary
    2. Chapter 20 Building a Visualization

      1. Preliminaries
      2. The HTML Skeleton
      3. CSS Styling
      4. The JavaScript Engine
      5. Running the Nobel Prize Visualization App
      6. Summary
    3. Chapter 21 Introducing D3—The Story of a Bar Chart

      1. Framing the Problem
      2. Working with Selections
      3. Adding DOM Elements
      4. Leveraging D3
      5. Measuring Up with D3’s Scales
      6. Unleashing the Power of D3 with Data Binding
      7. The enter Method
      8. Accessing the Bound Data
      9. The Update Pattern
      10. Axes and Labels
      11. Transitions
      12. Summary
    4. Chapter 22 Visualizing Individual Prizes

      1. Building the Framework
      2. Scales
      3. Axes
      4. Category Labels
      5. Nesting the Data
      6. Adding the Winners with a Nested Data-Join
      7. A Little Transitional Sparkle
      8. Summary
    5. Chapter 23 Mapping with D3

      1. Available Maps
      2. D3’s Mapping Data Formats
      3. D3 Geo, Projections, and Paths
      4. Putting the Elements Together
      5. Updating the Map
      6. Adding Value Indicators
      7. Our Completed Map
      8. Building a Simple Tooltip
      9. Summary
    6. Chapter 24 Visualizing Individual Winners

      1. Building the List
      2. Building the Bio-Box
      3. Summary
    7. Chapter 25 The Menu Bar

      1. Creating HTML Elements with D3
      2. Building the Menu Bar
      3. Summary
    8. Chapter 26 Conclusion

      1. Recap
      2. Future Progress
      3. Final Thoughts
    9. Appendix Moving from Development to Production

      1. The Starting Directory
      2. Configuration
      3. Authentication
      4. Testing Flask Apps
      5. Testing JavaScript Apps
      6. Deploying Flask Apps
      7. Logging and Error Handling