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Mastering Python High Performance

You're reading from   Mastering Python High Performance Learn how to optimize your code and Python performance with this vital guide to Python performance profiling and benchmarking

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781783989300
Length 260 pages
Edition 1st Edition
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Author (1):
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Fernando Donglio Fernando Donglio
Author Profile Icon Fernando Donglio
Fernando Donglio
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The pandas tool


The second tool that we'll discuss in this chapter is called pandas (http://pandas.pydata.org/). It is an open source library that provides high-performance, easy-to-use data structures, and data-analysis tools for Python.

This tool was invented back in 2008 by developer Wes McKinney while needing a performant solution to perform quantitative analysis on financial data. The library has become one of the most popular and active projects in the Python community.

One thing to note regarding the performance of code written using pandas is that parts of its critical code paths were written using Cython (we covered Cython in Chapter 6, Generic Optimization Options).

Installing pandas

Given the popularity of pandas, there are many ways to install it onto your system. It all depends on the type of setup you have.

The recommended way is to directly install the Anaconda Python distribution (docs.continuum.io/anaconda/), which comes packed with pandas and the rest of the SciPy stack (such...

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