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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) |
MARC
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| 100 | 1 | |a Davis, Ashley, |e author. | |
| 245 | 1 | 0 | |a Data Wrangling with JavaScript / |c Davis, Ashley. |
| 250 | |a 1st edition. | ||
| 264 | 1 | |b Manning Publications, |c 2018. | |
| 300 | |a 1 online resource (432 pages) | ||
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| 520 | |a 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 visualizations and deploy your apps to production. Each valuable chapter provides a new component for your reusable data wrangling toolkit. | ||
| 542 | |f Copyright © Manning Publications 2018 |g 2018 | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
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| 650 | 0 | |a JavaScript (Computer program language) | |
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| 650 | 7 | |a JavaScript (Computer program language) |2 fast | |
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