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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 and manipulating data with Pandas


Until now we have seen how to import and export data using mostly the tools provided in the Python standard library. Now, we'll see how to do some of the operations shown above in just few lines using the Pandas library. Pandas is an open source, BSD-licensed library that simplifies the process of data import and manipulation thus providing data structures and parsing functions.

We will demonstrate how to import, manipulate and export data using Pandas.

Getting ready

To be able to use the code in this section, we need to install Pandas.This can be done again using pip as shown here:

pip install pandas

How to do it...

Here, we will import again the data ch2-data.csv, add a new column to the original data and export the result in csv, as shown in the following code snippet:

data = pd.read_csv('ch02-data.csv')
data['amount_x_2'] = data['amount']*2
data.to_csv('ch02-data_more.csv)

How it works...

First, we import Pandas in our environment and then we use...

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