Wes McKinney is the author of pandas and vbench, and a contributor to statsmodels. Prior to starting Lambda Foundry, he worked in quantitative finance at AQR Capital Management. He's interested in data analysis, visualization, high performance computing, and testing, profiling, and performance monitoring tools.
In this tutorial, I'll give a brief overview of pandas basics for new users, then dive into the nuts of bolts of manipulating time series data in memory. This includes such common topics date arithmetic, alignment and join / merge methods, resampling and frequency conversion, time zone handling, moving window functions like moving mean and standard deviation. A strong focus will be placed on working with large time series efficiently using array manipulations. I'll also illustrate visualization tools for slicing and dicing time series to make informative plots. There will be several example data sets taken from finance, economics, ecology, web analytics, or other areas.
The target audience for the tutorial includes individuals who already work regularly with time series data and are looking to acquire additional skills and knowledge as well as users with an interest in data analysis who are new to time series. You will be expected to be comfortable with general purpose Python programming and have a modest amount of experience using NumPy. Prior experience with the basics of pandas's data structures will also be helpful.