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Hands-On Data Science with the Command Line
Hands-On Data Science with the Command Line

Hands-On Data Science with the Command Line: Automate everyday data science tasks using command-line tools

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Hands-On Data Science with the Command Line

Essential Commands

Now that we have the command line set up and installed, we will go over a list of everyday commands that are considered the basics. Having a fundamental understanding of the basic commands will be the building block on which we'll learn the advanced commands found later in this book.

In this chapter, we will cover the following topics:

  • Basic command-line navigation
  • Redirecting input and output
  • Where to get help if you're stuck
  • How to customize the shell to your liking

Essential commands

Woah... hold your horses, we need to cover some basics about commands. A command is a process run by a POSIX (Portable Operating System Interface) compliant OS (Operating System). OpenGroup maintains the standard in addition to it being ratified as an IEEE standard (http://pubs.opengroup.org/onlinepubs/9699919799/). In a POSIX environment, the process being run will have an environment, a current working directory, the command line (the path name that invoked the command and any arguments), and a series of file descriptors with stdin, stdout, and stderr (referred to by integer numbers 0, 1, and 2, respectively) being connected prior to handoff to your command.

Now with a little background and an installed command line, you are ready to go and we can actually start running commands. We will be going over some basic everyday commands. For those that are ready...

Navigating the command line

There's a couple of useful tricks for navigating the command line that, while optional, will improve your quality of life. This section has a selection of those tricks.

Bash, by default, saves the history of your commands. It will even save the history across sessions. This can be extremely useful because sometimes we make a small mistake and don't want to retype an entire command, or we want to repeat the same commands over and over. To see your history, type this command:

history

The following is what you should see on running the preceding command:

You can see that there is a numbered list of output commands. To repeat a numbered command, you can use the bang character, !. !<number> will repeat the number command verbatim:

!10

The following is what you should see on running the preceding command:

A double bang, !!, will repeat the...

Summary

As you can see, the command line is very powerful for everyday tasks. We learned how to do basic things, such as create files and directories, and navigate a system via the command line. We learned about manual pages, where to find help, and how to customize the shell.

In the next chapter, we'll take what you learned here and apply it against a real dataset. Feel free to come back to this chapter as it will be helpful throughout the rest of this book.

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Key benefits

  • Perform string processing, numerical computations, and more using CLI tools
  • Understand the essential components of data science development workflow
  • Automate data pipeline scripts and visualization with the command line

Description

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.

Who is this book for?

This book is for data scientists and data analysts with little to no knowledge of the command line but has an understanding of data science. Perform everyday data science tasks using the power of command line tools.

What you will learn

  • Understand how to set up the command line for data science
  • Use AWK programming language commands to search quickly in large datasets.
  • Work with files and APIs using the command line
  • Share and collect data with CLI tools
  • Perform visualization with commands and functions
  • Uncover machine-level programming practices with a modern approach to data science
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Publication date : Jan 31, 2019
Length: 124 pages
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Language : English
ISBN-13 : 9781789132984
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Tools :

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Table of Contents

7 Chapters
Data Science at the Command Line and Setting It Up Chevron down icon Chevron up icon
Essential Commands Chevron down icon Chevron up icon
Shell Workflows, and Data Acquisition and Massaging Chevron down icon Chevron up icon
Bash Functions and Data Visualization Chevron down icon Chevron up icon
Loops, Functions, and String Processing Chevron down icon Chevron up icon
SQL, Math, and Wrapping it up Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
Amazon Customer Nov 07, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great reference to explore the command line with the magic of data science. :D
Amazon Verified review Amazon
R. M. Mar 03, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Any data science work that can be done quickly + locally adds immediate value. This book provides a wide range of tools for everyday data science tasks, aimed mostly at the academic-turned-data-scientist and with a few nice surprises. Many useful exercises and examples. The writing also made me laugh, which helped
Amazon Verified review Amazon
Russell Geddes Feb 13, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Sometimes you need the command line to get exactly what you want. This book shows you how to automate tasks and I especially liked the tips on using AWK to search quickly in large datasets. This is a practical hands-on tool that you will be referring to again and again.
Amazon Verified review Amazon
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