Alexander Hendorf - Introduction to Time Series Analysis with Pandas

Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.

Pandas is a powerful framework for working with time series data and can make your life a lot easier.

This talks features:

quick intro to Pandas
how to analyze periodical data with pandas
read and write data in various formats
how to mangle, reshape and pivot
gain insights with stats-models (e.g. seasonality)
caveats when working with timed data
visualize your data on the fly