Read e-book online Data Science at the Command Line: Facing the Future with PDF

By Jeroen Janssens

ISBN-10: 1491947853

ISBN-13: 9781491947852

This hands-on advisor demonstrates how the pliability of the command line might help turn into a extra effective and efficient info scientist. You'll the way to mix small, but robust, command-line instruments to fast 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 choked with over eighty command-line tools.

Discover why the command line is an agile, scalable, and extensible know-how. whether you're already cozy processing facts with, say, Python or R, you'll vastly increase your facts technology workflow by means of additionally leveraging the facility of the command line.

  • Obtain facts from web pages, APIs, databases, and spreadsheets
  • Perform scrub operations on undeniable textual content, CSV, HTML/XML, and JSON
  • Explore information, compute descriptive data, and create visualizations
  • Manage your facts 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
  • Model facts with dimensionality relief, clustering, regression, and class algorithms

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Additional resources for Data Science at the Command Line: Facing the Future with Time-Tested Tools

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List of HTTP status codes. org/wiki/List_of_HTTP_status_codes. Further Reading | 39 CHAPTER 4 Creating Reusable Command-Line Tools Throughout the book, we use a lot of commands and pipelines that basically fit on one line (let’s call those one-liners). Being able to perform complex tasks with just a one-liner is what makes the command line powerful. It’s a very different experience from writing traditional programs. Some tasks you perform only once, and some you perform more often. Some tasks are very specific and others can be generalized.

In this book, when it comes to creating new command-line tools, we’ll focus mostly on the last three types: interpreted scripts, shell functions, and aliases. This is because these can easily be changed. The purpose of a command-line tool is to make your life on the command line easier, and to make you a more productive and more efficient data scientist. You can find out the type of a command-line tool with type (which is itself a shell builtin): $ type -a pwd pwd is a shell builtin pwd is /bin/pwd $ type -a cd cd is a shell builtin $ type -a fac fac is a function fac () { ( echo 1; seq $1 ) | paste -s -d\* | bc } $ type -a l l is aliased to `ls -1 --group-directories-first' As you can see, type returns two command-line tools for pwd.

2014). Vagrant. com. • Heddings, L. (2006). Keyboard Shortcuts for Bash. com/howto/ubuntu/keyboard-shortcuts-for-bash-commandshell-for-ubuntu-debian-suse-redhat-linux-etc. , & Loukides, M. (2002). ). O’Reilly Media. Further Reading | 27 CHAPTER 3 Obtaining Data This chapter deals with the first step of the OSEMN model: obtaining data. 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.

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Data Science at the Command Line: Facing the Future with Time-Tested Tools by Jeroen Janssens

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