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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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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

Interfacing with R

R provides a datasets package that contains sample datasets. The morley dataset has data from measurements of the speed of light made in 1879. The speed of light is a fundamental physical constant and its value is currently known very precisely. The data is described at http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/morley.html. The speed of light value can be found in the scipy.constants module. The R data is stored in an R dataframe with three columns:

  • The experiment number from one to five
  • The run number with twenty runs per experiment, bringing the total measurements to 100
  • The measured speed of light in kilometers per second with 299,000 subtracted

The rpy2.robjects.r() function executes R code in a Python environment. Load the data as follows:

ro.r('data(morley)')

The pandas library provides an R interface via the pandas.rpy.common module. Load the data into a pandas DataFrame as follows:

df = com.load_data('morley')

Let's group the...

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