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Practical Data Wrangling

You're reading from   Practical Data Wrangling Expert techniques for transforming your raw data into a valuable source for analytics

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286139
Length 204 pages
Edition 1st Edition
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Author (1):
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Allan Visochek Allan Visochek
Author Profile Icon Allan Visochek
Allan Visochek
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Table of Contents (10) Chapters Close

Preface 1. Programming with Data FREE CHAPTER 2. Introduction to Programming in Python 3. Reading, Exploring, and Modifying Data - Part I 4. Reading, Exploring, and Modifying Data - Part II 5. Manipulating Text Data - An Introduction to Regular Expressions 6. Cleaning Numerical Data - An Introduction to R and RStudio 7. Simplifying Data Manipulation with dplyr 8. Getting Data from the Web 9. Working with Large Datasets

Introducing MongoDB


MongoDB is what is referred to as a NoSQL database, which refers to a data model that is not tabular, as opposed to relational databases which are tabular. The structure of data in MongoDB is analogous to JSON, with each of the documents consisting of key-value pairs.

Once you have MongoDB set up on your computer and you have the MongoDB server running, you can import your data into a database using the mongoimport terminal command. The mongoimport command will take data from a static file, parse the data, and place the data into a database. The documentation for mongoimport is available at the following link: https://docs.mongodb.com/manual/reference/program/mongoimport/.

There are a few parameters that need to be specified along with the mongoimport command. The first of these is the name of the input file which should be written after the --file parameter. The command should be run in a terminal from the directory containing fake_weather_data.csv, so the filename is...

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