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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

Installing virtualenv and virtualenvwrapper


If you are working on many projects simultaneously, or even just switching between them frequently, you'll find that having everything installed system-wide is not the best option and can bring problems in future on different systems (production) where you want to run your software. This is not a good time to find out that you are missing a certain package or have versioning conflicts between packages that are already installed on production system; hence, virtualenv.

virtualenv is an open source project started by Ian Bicking that enables a developer to isolate working environments per project, for easier maintenance of different package versions.

For example, you inherited legacy Django website based on Django 1.1 and Python 2.3, but at the same time you are working on a new project that must be written in Python 2.6. This is my usual case—having more than one required Python version (and related packages) depending on the project I am working on.

virtualenv enables me to easily switch to different environments and have the same package easily reproduced if I need to switch to another machine or to deploy software to a production server (or to a client's workstation).

Getting ready

To install virtualenv, you must have workable installation of Python and pip. Pip is a tool for installing and managing Python packages, and it is a replacement for easy install. We will use pip through most of this book for package management. Pip is easily installed, as root executes the following line in your terminal:

# easy_install pip

virtualenv by itself is really useful, but with the help of virtualenvwrapper, all this becomes easy to do and also easy to organize many virtual environments. See all the features at http://virtualenvwrapper.readthedocs.org/en/latest/#features.

How to do it...

By performing the following steps you can install the virtualenv and virtualenvwrapper tools:

  1. Install virtualenv and virtualenvwrapper:

    $ sudo pip virtualenv
    $ sudo pip virtualenvwrapper
    # Create folder to hold all our virtual environments and export the path to it.
    $ export VIRTENV=~/.virtualenvs
    $ mkdir -p $VIRTENV
    # We source (ie. execute) shell script to activate the wrappers
    $ source /usr/local/bin/virtualenvwrapper.sh
    # And create our first virtual environment
    $ mkvirtualenv virt1
    
  2. You can now install our favorite package inside virt1:

    (virt1)user1:~$ pip install matplotlib
    
  3. You will probably want to add the following line to your ~/.bashrc file:

    source /usr/loca/bin/virtualenvwrapper.sh

Few useful and most frequently used commands are as follows:

  • mkvirtualenv ENV: This creates virtual environment with name ENV and activates it

  • workon ENV: This activates the previously created ENV

  • deactivate: This gets us out of the current virtual environment

You have been reading a chapter from
Python Data Visualization Cookbook
Published in: Nov 2013
Publisher: Packt
ISBN-13: 9781782163367
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