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Practical Data Analysis

You're reading from   Practical Data Analysis For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Length 360 pages
Edition 1st Edition
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Author (1):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
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Table of Contents (24) Chapters Close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Installing and running NumPy


According to the official website http://www.numpy.org/, NumPy is the fundamental package for scientific computing with Python. It contains amongst other things:

  • A powerful N-dimensional array object

  • Sophisticated (broadcasting) functions

  • Tools for integrating C/C++ and Fortran code

  • Useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of the generic data. Arbitrary datatypes can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of datasets.

Installing and running NumPy on Ubuntu

To install numpy, simply open a command prompt and run.

$ sudo apt-get install python3-numpy

To check whether everything is installed correctly, just execute the Python Shell shown as follows:

$ idle3

Then execute the following commands:

>>> import numpy
>>> numpy.test()

Tip

We need to use the nose library (that extends the test loading and running features of unit test) when using numpy.test().

In order to install it, we just need to open a command line and run the following command:

$ sudo apt-get install python3-nose

Or you can also execute:

$ pip install nose

For more information about the nose library, visit https://pypi.python.org/pypi/nose/1.1.2.

Installing and running NumPy on Windows

First, download the NumPy 1.7 from the official website http://sourceforge.net/projects/numpy/files/NumPy/1.7.0/.

The Windows version is provided as an .exe package. To install it manually, just double click on the /numpy-1.7.0-win32-superpack-python3.2.exe file.

To check whether everything is installed correctly, just navigate to Start | All Programs | Python 3.2 | IDLE (Python GUI).

Then execute the following commands:

>>> import numpy
>>> numpy.test()

Tip

We need to use the nose library (that extends the test loading and running features of unit test) when using numpy.test().

In order to install it, you just need to open a Windows command line (CMD) and run the following command:

C:\> pip install nose

For more information about nose, visit https://pypi.python.org/pypi/nose/1.1.2.

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