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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

Using the CSV module to read CSV data


In this first demonstration, you will read the artificial_roads_by_region.csv file and get an estimate of the total length of roads as of 2011.

The first step to using the CSV module is to import the module, as shown in the following example:

import csv

The next step, similar to the process use in Chapter 3Reading, Exploring, and Modifying Data - Part I, is to open the file containing the data. Recall that for this chapter, the code is one directory up from the base directory. This means that in the path to the data, you will first need to go back one directory using ../ in macOS and Linux or ..\\ in Windows. In the following demonstration, I've created a Python script called csv_intro.py in which the open() function is used to open artificial_roads_by_region.csv with read permission:

import csv

## open the file containing the data
fin = open("../data/input_data/artificial_roads_by_region.csv","r",newline="")

Note

You may have noticed that a new parameter...

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