By Jeroen Janssens
This hands-on consultant demonstrates how the flexibleness of the command line might help turn into a extra effective and effective information scientist. You’ll methods to mix small, but strong, command-line instruments to quick receive, scrub, discover, and version your data.
To get you started—whether you’re on home windows, OS X, or Linux—author Jeroen Janssens introduces the information technology Toolbox, an easy-to-install digital surroundings full of over eighty command-line tools.
Discover why the command line is an agile, scalable, and extensible know-how. no matter if you’re already cozy processing information with, say, Python or R, you’ll tremendously increase your information technological know-how workflow via additionally leveraging the facility of the command line.
receive facts from web content, APIs, databases, and spreadsheets
practice scrub operations on undeniable textual content, CSV, HTML/XML, and JSON
discover facts, compute descriptive facts, and create visualizations
deal with your information technological know-how workflow utilizing Drake
Create reusable instruments from one-liners and latest Python or R code
Parallelize and distribute data-intensive pipelines utilizing GNU Parallel
version info with dimensionality aid, clustering, regression, and category algorithms
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Additional info for Data Science at the Command Line
After all, without any data, there is not much data science that we can do. We assume that the data that is needed to solve the data science problem at hand already exists at some location in some form. Our goal is to get this data onto your computer (or into your Data Science Toolbox) in a form that we can work with. According to the Unix philosophy, text is a universal interface. Almost every command-line tool takes text as input, produces text as output, or both. This is the main reason why command-line tools can work so well together.
The most common way of combining command-line tools is through a so-called pipe. The out‐ put from the first tool is passed to the second tool. There are virtually no limits to this. Consider, for example, the command-line tool seq, which generates a sequence of numbers. Let’s generate a sequence of five numbers: $ seq 5 1 2 3 4 5 The output of a command-line tool is by default passed on to the terminal, which dis‐ plays it on our screen. We can pipe the ouput of seq to a second tool, called grep, which can be used to filter lines.
O’Reilly Media. , & Schutt, R. (2013). Doing Data Science. O’Reilly Media. • Shron, M. (2014). Thinking with Data. O’Reilly Media. 12 | Chapter 1: Introduction CHAPTER 2 Getting Started In this chapter, we are going to make sure that you have all the prerequisites for doing data science at the command line. The prerequisites fall into two parts: (1) having a proper environment with all the command-line tools that we employ in this book, and (2) understanding the essential concepts that come into play when using the command line.
Data Science at the Command Line by Jeroen Janssens