Practical Pandas

Welcome back. As a reminder: In part 1 we got dataset with my cycling data from last year merged and stored in an HDF5 store In part 2 we did some cleaning and augmented the cycling data with data from http://forecast.io. You can find the full source code and data at this project’s GitHub repo. Today we’ll use pandas, seaborn, and matplotlib to do some exploratory data analysis. For fun, we’ll make some maps at the end using folium. ...

This is Part 2 in the Practical Pandas Series, where I work through a data analysis problem from start to finish. It’s a misconception that we can cleanly separate the data analysis pipeline into a linear sequence of steps from data acqusition data tidying exploratory analysis model building production As you work through a problem you’ll realize, “I need this other bit of data”, or “this would be easier if I stored the data this way”, or more commonly “strange, that’s not supposed to happen”. ...