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Building Rich, High Performance Tools for Practical Data Analysis

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Published on Apr 19, 2013

Recorded from a Live Webcast

This talk is presented by Wes McKinney author of Python for Data Analysis and will be a somewhat advanced, technical talk connecting computer science concepts like data structure design and algorithms with the details of building intuitive, high performance, and flexible tools for data analysis. It is an accumulation of lessons learned and experience gained building pandas, a widely used, battle-tested data analysis toolkit for Python. Wes will give a number of short code demonstrations as a means of illustrating the various points.

In this webcast we will cover: Missing data handling Simple and hierarchical indexing Efficient serialization Pivoting and reshaping Grouped data aggregation and transformation Time series-specific computations Merge and join algorithms

Wes will also discuss structuring data for visualization and output to other tools such as JavaScript visualization toolkits like D3.js. Don't miss this exclusive event.

About Wes McKinney

Wes McKinney is CTO and Cofounder of Lambda Foundry, Inc. From 2010 to 2012, he served as a Python consultant to hedge funds and banks while developing pandas, a widely used Python data analysis library. From 2007 to 2010, he researched global macro and credit trading strategies at AQR Capital Management. He graduated from MIT with an S.B. in Mathematics. He is on leave from the Duke University Ph.D program in Statistics.

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