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Coming from the 2012 PyData Workshop, Wes McKinney, CTO and cofounder of Lambda Foundry, gives us a tour of Pandas, a rich data manipulation tool built on top of NumPy. Frustrated with working in R, Wes started building Pandas in 2008 with a focus on fast, intuitive data structures and data manipulation capabilities. The Pandas project has seen huge growth in the last few years, and aims to be the ultimate data tool for Python.
pandas is a Python library providing fast, expressive data structures for working with structured or relational data sets. In addition to being used for general purpose data manipulation and data analysis, it has also been designed to enable Python to become a competitive statistical computing platform. In this talk, I will discuss the library's features and show a variety of topical examples.
The tutorial will give a hands-on introduction to manipulating and analyzing large and small structured data sets in Python using the pandas library. While the focus will be on learning the nuts and bolts of the library's features, I also aim to demonstrate a different way of thinking regarding structuring data in memory for manipulation and analysis.
In this talk I will give an overview on the pandas data analysis package for Python, its features, and plans for future development. I'll use various interesting data sets to illustrate the features and give motivation for how the tools can be applied in a diverse set of fields.