Introduction to Pandas and Time Series Analysis

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:
• quick intro to Pandas
• how to analyse 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
• visualise your data on the fly

Alexander Hendorf
Partner, Königsweg

Pythonista & Data-Nerd
As senior consultant of German management consultancy Königsweg, Alexander is guiding enterprises and institutions through change processes of digitalisation and automation.
Alexander always loved data almost as much as music and so no wonder he’s organiser of local meet ups and one of the 25 mongoDB Community Masters.
He loves to share this expertise and engages in the global community as organiser and program chair of the EuroPython conference, speaker and trainer at multiple international conferences as mongoDB World, EuroPython, Cebit or PyData.