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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

NumPy and SciPy modules


First, let's take a look at the NumPy and SciPy module documentation. What will be described here is not a topic specific to data analysis, but more of a general Python item.

The following code prints the descriptions of subpackages for NumPy and SciPy:

import pkgutil as pu
import numpy as np
import matplotlib as mpl
import scipy as sp
import pydoc


print "NumPy version", np.__version__
print "SciPy version", sp.__version__
print "Matplotlib version", mpl.__version__

def clean(astr):
   s = astr
   # remove multiple spaces
   s = ' '.join(s.split())
   s = s.replace('=','')

   return s

def print_desc(prefix, pkg_path):
   for pkg in pu.iter_modules(path=pkg_path):
      name = prefix + "." + pkg[1]

      if pkg[2] == True:
         try:
            docstr = pydoc.plain(pydoc.render_doc(name))
            docstr = clean(docstr)
            start = docstr.find("DESCRIPTION")
            docstr = docstr[start: start + 140]
            print name, docstr
         except...
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