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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

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 the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly Index

Importing data from Microsoft Excel files


Although Microsoft Excel supports some charting, sometimes you need more flexible and powerful visualization and need to export data from existing spreadsheets into Python for further use.

A common approach to importing data from Excel files is to export data from Excel into CSV-formatted files and use the tools described in the previous recipe to import data using Python from the CSV file. This is a fairly easy process if we have one or two files (and have Microsoft Excel or OpenOffice.org installed), but if we are automating a data pipe for many files (as part of an ongoing data processing effort), we are not in a position to manually convert every Excel file into CSV. So, we need a way to read any Excel file.

Python has decent support for reading and writing Excel files through the project www.python-excel.org. This support is available in the form of different modules for reading and writing and is platform-independent; in other words, we don't...

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