Pandas? What’s that?
Pandas has become a hub of activity for the development of useful, (relatively) easy tools for doing all kinds of “data science.” This functionality is built around a central DataFrame structure (familiar to those of you coming from R), and includes support for oft-neglected stages of science like data cleaning and exploratory plotting.
The plan
We didn’t quite get through all the nice ways to use matplotlib last week, so we’ll be ready to have a quick look at:
- Seaborn, which makes it easier to plot using pandas and make things look nice.
- Vectorized string methods that make your data-cleaning life easier.
- Dav used stock data using the pandas io module for one example.
But we always welcome short talks from everyone! Particularly if you are haven’t spoken before!
If there’s interest, after the lightning talks, I (Dav) will lead a group on fixing a bug in the string methods linked above, and we’ll try submitting a pull request against pandas on github.