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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Tools
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Installing rpy2

The R programming language is popular among statisticians. It is written in C and Fortran and is available under the GNU General Public License. R has support for modeling, statistical tests, time-series analysis, classification, visualization, and clustering. The Comprehensive R Archive Network (CRAN) and other repository websites offer thousands of R packages for various tasks.

Note

Download R from http://www.r-project.org/.

The latest R version as of August 2014 was 3.1.1. The rpy2 package facilitates interfacing with R from Python. Install rpy2 as follows with pip:

$ pip install rpy2
$ pip freeze|grep rpy2
rpy2==2.4.2

Note

If you already have rpy2 installed, follow the instructions on http://rpy.sourceforge.net/rpy2/doc-dev/html/overview.html as upgrading is not a straightforward process.

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