Tim Hopper
Description: Interactive comparison of Python plotting libraries for exploratory data analysis. Examples of using Pandas plotting, plotnine, Seaborn, and Matplotlib. Includes comparison with ggplot2 for R.
The simple graph has brought more information to the data analyst's mind than any other device.
Plotting is an essential component of data analysis. As a data scientist, I spend a significant amount of my time making simple plots to understand complex data sets (exploratory data analysis) and help others understand them (presentations).
In particular, I make a lot of bar charts (including histograms), line plots (including time series), scatter plots, and density plots from data in Pandas data frames . I often want to facet these on various categorical variables and layer them on a common grid.
Tim Hopper