Book Image

Hands-On Data Science with the Command Line

By : Jason Morris, Chris McCubbin, Raymond Page
Book Image

Hands-On Data Science with the Command Line

By: Jason Morris, Chris McCubbin, Raymond Page

Overview of this book

The Command Line has been in existence on UNIX-based OSes in the form of Bash shell for over 3 decades. However, very little is known to developers as to how command-line tools can be OSEMN (pronounced as awesome and standing for Obtaining, Scrubbing, Exploring, Modeling, and iNterpreting data) for carrying out simple-to-advanced data science tasks at speed. This book will start with the requisite concepts and installation steps for carrying out data science tasks using the command line. You will learn to create a data pipeline to solve the problem of working with small-to medium-sized files on a single machine. You will understand the power of the command line, learn how to edit files using a text-based and an. You will not only learn how to automate jobs and scripts, but also learn how to visualize data using the command line. By the end of this book, you will learn how to speed up the process and perform automated tasks using command-line tools.
Table of Contents (8 chapters)

Getting set up on OS X

OS X already has a full command-line system installed using bash as the default shell. To access this shell, click the magnifying glass in the upper-right corner and type terminal in the dialog box:

This will open a bash Terminal:

As in other bash shells, this Terminal doesn't have everything installed, so type the following commands to install the requisite installers and command-line tools that we'll be using in this book:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
brew install jq sqlite gnuplot python netcat bc
pip3 install pandas

On OS X, this script installs a few installation tools, including pip and homebrew. It then uses these tools to install the commands that we use in this book that aren't natively installed, namely jq, gnuplot, sqlite, and pandas.

One thing to look out for in OS X is that certain standard tools are built a little differently than the ones that come with Debian-based systems like the rest of the systems we talk about in this chapter. In some circumstances, OS X tools work slightly differently or have different options. Where this is the case we have noted it in the text.