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Data Wrangling with JavaScript /
Data Wrangling with JavaScript promotes JavaScript to the center of the data analysis stage! With this hands-on guide, you'll create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. You'll also build interactive vi...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Manning Publications,
2018.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro
- Titlepage
- Copyright
- preface
- acknowledgments
- about this book
- Who should read this book
- How this book is organized: a roadmap
- About the code
- Book forum
- Other online resources
- about the author
- about the cover illustration
- Chapter 1: Getting started: establishing your data pipeline
- 1.1 Why data wrangling?
- 1.2 What's data wrangling?
- 1.3 Why a book on JavaScript data wrangling?
- 1.4 What will you get out of this book?
- 1.5 Why use JavaScript for data wrangling?
- 1.6 Is JavaScript appropriate for data analysis?
- 1.7 Navigating the JavaScript ecosystem
- 1.8 Assembling your toolkit
- 1.9 Establishing your data pipeline
- 1.9.1 Setting the stage
- 1.9.2 The data-wrangling process
- 1.9.3 Planning
- 1.9.4 Acquisition, storage, and retrieval
- 1.9.5 Exploratory coding
- 1.9.6 Clean and prepare
- 1.9.7 Analysis
- 1.9.8 Visualization
- 1.9.9 Getting to production
- Summary
- Chapter 2: Getting started with Node.js
- 2.1 Starting your toolkit
- 2.2 Building a simple reporting system
- 2.3 Getting the code and data
- 2.3.1 Viewing the code
- 2.3.2 Downloading the code
- 2.3.3 Installing Node.js
- 2.3.4 Installing dependencies
- 2.3.5 Running Node.js code
- 2.3.6 Running a web application
- 2.3.7 Getting the data
- 2.3.8 Getting the code for chapter 2
- 2.4 Installing Node.js
- 2.4.1 Checking your Node.js version
- 2.5 Working with Node.js
- 2.5.1 Creating a Node.js project
- 2.5.2 Creating a command-line application
- 2.5.3 Creating a code library
- 2.5.4 Creating a simple web server
- 2.6 Asynchronous coding
- 2.6.1 Loading a single file
- 2.6.2 Loading multiple files
- 2.6.3 Error handling
- 2.6.4 Asynchronous coding with promises
- 2.6.5 Wrapping asynchronous operations in promises
- 2.6.6 Async coding with "async" and "await"
- Summary.
- Chapter 3: Acquisition, storage, and retrieval
- 3.1 Building out your toolkit
- 3.2 Getting the code and data
- 3.3 The core data representation
- 3.3.1 The earthquakes website
- 3.3.2 Data formats covered
- 3.3.3 Power and flexibility
- 3.4 Importing data
- 3.4.1 Loading data from text files
- 3.4.2 Loading data from a REST API
- 3.4.3 Parsing JSON text data
- 3.4.4 Parsing CSV text data
- 3.4.5 Importing data from databases
- 3.4.6 Importing data from MongoDB
- 3.4.7 Importing data from MySQL
- 3.5 Exporting data
- 3.5.1 You need data to export!
- 3.5.2 Exporting data to text files
- 3.5.3 Exporting data to JSON text files
- 3.5.4 Exporting data to CSV text files
- 3.5.5 Exporting data to a database
- 3.5.6 Exporting data to MongoDB
- 3.5.7 Exporting data to MySQL
- 3.6 Building complete data conversions
- 3.7 Expanding the process
- Summary
- Chapter 4: Working with unusual data
- 4.1 Getting the code and data
- 4.2 Importing custom data from text files
- 4.3 Importing data by scraping web pages
- 4.3.1 Identifying the data to scrape
- 4.3.2 Scraping with Cheerio
- 4.4 Working with binary data
- 4.4.1 Unpacking a custom binary file
- 4.4.2 Packing a custom binary file
- 4.4.3 Replacing JSON with BSON
- 4.4.4 Converting JSON to BSON
- 4.4.5 Deserializing a BSON file
- Summary
- Chapter 5: Exploratory coding
- 5.1 Expanding your toolkit
- 5.2 Analyzing car accidents
- 5.3 Getting the code and data
- 5.4 Iteration and your feedback loop
- 5.5 A first pass at understanding your data
- 5.6 Working with a reduced data sample
- 5.7 Prototyping with Excel
- 5.8 Exploratory coding with Node.js
- 5.8.1 Using Nodemon
- 5.8.2 Exploring your data
- 5.8.3 Using Data-Forge
- 5.8.4 Computing the trend column
- 5.8.5 Outputting a new CSV file
- 5.9 Exploratory coding in the browser
- Putting it all together.
- 8.7.7 Filtering using queries
- 8.7.8 Discarding data with projection
- 8.7.9 Sorting large data sets
- 8.8 Achieving better data throughput
- 8.8.1 Optimize your code
- 8.8.2 Optimize your algorithm
- 8.8.3 Processing data in parallel
- Summary
- Chapter 9: Practical data analysis
- 9.1 Expanding your toolkit
- 9.2 Analyzing the weather data
- 9.3 Getting the code and data
- 9.4 Basic data summarization
- 9.4.1 Sum
- 9.4.2 Average
- 9.4.3 Standard deviation
- 9.5 Group and summarize
- 9.6 The frequency distribution of temperatures
- 9.7 Time series
- 9.7.1 Yearly average temperature
- 9.7.2 Rolling average
- 9.7.3 Rolling standard deviation
- 9.7.4 Linear regression
- 9.7.5 Comparing time series
- 9.7.6 Stacking time series operations
- 9.8 Understanding relationships
- 9.8.1 Detecting correlation with a scatter plot
- 9.8.2 Types of correlation
- 9.8.3 Determining the strength of the correlation
- 9.8.4 Computing the correlation coefficient
- Summary
- Chapter 10: Browser-based visualization
- 10.1 Expanding your toolkit
- 10.2 Getting the code and data
- 10.3 Choosing a chart type
- 10.4 Line chart for New York City temperature
- 10.4.1 The most basic C3 line chart
- 10.4.2 Adding real data
- 10.4.3 Parsing the static CSV file
- 10.4.4 Adding years as the X axis
- 10.4.5 Creating a custom Node.js web server
- 10.4.6 Adding another series to the chart
- 10.4.7 Adding a second Y axis to the chart
- 10.4.8 Rendering a time series chart
- 10.5 Other chart types with C3
- 10.5.1 Bar chart
- 10.5.2 Horizontal bar chart
- 10.5.3 Pie chart
- 10.5.4 Stacked bar chart
- 10.5.5 Scatter plot chart
- 10.6 Improving the look of our charts
- 10.7 Moving forward with your own projects
- Summary
- Chapter 11: Server-side visualization
- 11.1 Expanding your toolkit
- 11.2 Getting the code and data.
- 11.3 The headless browser
- 11.4 Using Nightmare for server-side visualization
- 11.4.1 Why Nightmare?
- 11.4.2 Nightmare and Electron
- 11.4.3 Our process: capturing visualizations with Nightmare
- 11.4.4 Prepare a visualization to render
- 11.4.5 Starting the web server
- 11.4.6 Procedurally start and stop the web server
- 11.4.7 Rendering the web page to an image
- 11.4.8 Before we move on . . .
- 11.4.9 Capturing the full visualization
- Feeding the chart with data
- Multipage reports
- Debugging code in the headless browser
- Making it work on a Linux server
- 11.5 You can do much more with a headless browser
- 11.5.1 Web scraping
- 11.5.2 Other uses
- Summary
- Chapter 12: Live data
- 12.1 We need an early warning system
- 12.2 Getting the code and data
- 12.3 Dealing with live data
- 12.4 Building a system for monitoring air quality
- 12.5 Set up for development
- 12.6 Live-streaming data
- 12.6.1 HTTP POST for infrequent data submission
- 12.6.2 Sockets for high-frequency data submission
- 12.7 Refactor for configuration
- 12.8 Data capture
- 12.9 An event-based architecture
- Code restructure for event handling
- 12.10.1 Triggering SMS alerts
- 12.10.2 Automatically generating a daily report
- Live data processing
- Live visualization
- Summary
- Chapter 13: Advanced visualization with D3
- 13.1 Advanced visualization
- 13.2 Getting the code and data
- 13.3 Visualizing space junk
- 13.4 What is D3?
- 13.5 The D3 data pipeline
- 13.6 Basic setup
- 13.7 SVG crash course
- 13.7.1 SVG circle
- 13.7.2 Styling
- 13.7.3 SVG text
- 13.7.4 SVG group
- 13.8 Building visualizations with D3
- 13.8.1 Element state
- 13.8.2 Selecting elements
- 13.8.3 Manually adding elements to our visualization
- 13.8.4 Scaling to fit
- 13.8.5 Procedural generation the D3 way
- 13.8.6 Loading a data file.