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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 2. NumPy Arrays FREE CHAPTER 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 Cython

The Cython programming language acts as glue between Python and C/C++. With the Cython tools, we can compile plain Python code, which is closer to the machine level. The following command will install Cython:

$ pip install cython

The cytoolz package contains utilities created by Cythonizing the handy Python toolz package. Install cytoolz as follows:

$ pip install cytoolz
$ pip freeze|grep cytoolz
cytoolz==0.7.0

Just as in cooking shows, we will show the results of Cythonizing before going through the process involved (deferred to the next section). The timeit Python module measures time. We will use this module to measure different functions. Define the following function, which accepts as arguments a short code snippet, a function call, and the number of times the code will run:

def time(code, n):
    times = min(timeit.Timer(code, setup=setup).repeat(3, n))

    return round(1000* np.array(times)/n, 3)

We predefine a large setup string containing all the code. The code is...

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