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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

Exchanging information with MATLAB/Octave

MATLAB and its open source alternative Octave are popular numerical programs and programming languages. Octave and MATLAB have syntax very similar to Python's. In fact, you can find websites that compare their syntax (for instance, see http://wiki.scipy.org/NumPy_for_Matlab_Users).

The most recent Octave version at the time of writing was 3.8.0. The scipy.io.savemat() function saves an array in a file compliant to the Octave and MATLAB format. The function accepts as parameters the name of the file and a dictionary with a name for the array and the values. Refer to the octave_demo.py file in this book's code bundle:

import statsmodels.api as sm
from scipy.io import savemat

data_loader = sm.datasets.sunspots.load_pandas()
df = data_loader.data
savemat("sunspots", {"sunspots": df.values})

The preceding code stores sunspots data in a file called...

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