Most data is allocated to a period or to some point in time. We can gain a lot of insight by analysing 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.
Pandas is a powerful framework for working with time series data and can make your life a lot easier.
This talks will feature:
- how to analyse periodical data with pandas
- read and write data in various formats
- how to mangle, reshape and pivot
- gain insights with statsmodels (e.g. seasonality)
- caveats when working with timed data
- visualize your data on the fly