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Python Data Visualization Cookbook

You're reading from   Python Data Visualization Cookbook As a developer with knowledge of Python you are already in a great position to start using data visualization. This superb cookbook shows you how in plain language and practical recipes, culminating with 3D animations.

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Product type Paperback
Published in Nov 2013
Publisher Packt
ISBN-13 9781782163367
Length 280 pages
Edition 1st Edition
Languages
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Author (1):
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Igor Milovanovic Igor Milovanovic
Author Profile Icon Igor Milovanovic
Igor Milovanovic
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Table of Contents (15) Chapters Close

Python Data Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using Right Plots to Understand Data 8. More on matplotlib Gems Index

Importing data from tab-delimited files


Another very common format of flat datafile is the tab-delimited file. This can also come from an Excel export but can be the output of some custom software we must get our input from.

The good thing is that usually this format can be read in almost the same way as CSV files, as the Python module csv supports so-called dialects that enable us to use the same principles to read variations of similar file formats—one of them being the tab delimited format.

Getting ready

We are already able to read CSV files. If not, please refer the Importing data from CSV recipe first.

How to do it...

We will re-use the code from the Importing data from CSV recipe, where all we need to change is the dialect we are using.

import csv

filename = 'ch02-data.tab'

data = []
try:
    with open(filename) as f:
        reader = csv.reader(f, dialect=csv.excel_tab)
    header = reader.next()
       data = [row for row in reader]
except csv.Error as e:
    print "Error reading CSV...
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