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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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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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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. The Pandas Primer 4. Statistics and Linear Algebra 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

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 Octave version used at the time of writing was 4.2.0. The scipy.io.savemat() function saves an array in a file compliant to the Octave and Matlab format. The function accepts the name of the file and a dictionary with a name for the array and the values as parameters. Refer to the ch-11.ipynb 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 sunspot data in a file called...

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