Aggregation and data grouping of Dataframes is accomplished in Python Pandas using “groupby()” and “agg()” functions. In this post, we’ll look at every aspect of grouping by single or multiple columns, applying aggregation functions such as max, min, count, and naming the resulting Dataframes and Pandas Series.
Threading in Python is simple. It allows you to manage concurrent threads doing work at the same time. The library is called “threading”, you create “Thread” objects, and they run target functions for you. You can start potentially hundreds of threads that will operate in parallel. Speed up long running tasks by parallelising and threading computation where you can.
FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post… SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in …