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Wrangling person-data with pandas for analyses: processes, possibilities, tricks
Important notes: 1) I can do this Meetup in English or in German. 2) Even if you never work with person-related data, many of the processes I go through are also totally applicable to other fields. 3) Prior knowledge of Jupyter and pandas will help but isn't necessary. I'll make it as simple as possible so you can follow along. If you want to try some of the operations I'll present by yourself I'll include a Jupyter notebook with some code. Also I've listed very helpful resources at the end of the description. While working at Tripicchio, an E-Mail marketing company, I've had the opportunity to process a lot of data related to individuals (e.g. First and last name, country, city, Email...). Python and especially pandas and numpy, have been a huge help and great tools for this. I will show you the steps to work with the data, how to extract information from person-related data and even add information, I'll also show you some ways to identify or even take care of incorrect user-entries and much more. I will be using Tableau to present some visualizations. There are many great visualization libraries in python such as Matplotlib, Plotly, seaborn... that you could also use after the data have been processed. Tripicchio introduced me to Tableau and I've been using it since then for its versatility, ease of use and the speed with which I can build Dashboards with it. Tripicchio’s customers can also use it easily. I'm not planning on explaining how to do your own visualization with Tableau, since it would take a lot of time but I'd be happy to answer any general question regarding this Software. Useful links: # learning data analysis with pandas: Dataschool - https://www.youtube.com/watch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y Python for Data Analysis, 2nd Edition (important!) - you can buy the book or a pdf. It was written by the creator of pandas.

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