I work in pandas 95% of my day doing data automation tasks, manipulating sql queries, and data-munging for machine learning. It is literally life changing for someone like me.
I used to program solely in R, but after discovering pandas I really have no need to go back to R. My project workflow consists of several IPython notebooks+pandas+sklearn.
Works extremely well in production, as in, on a flask web server, as well.
For the particular tasks of "data automation tasks, manipulating sql queries, and data-munging for machine learning," Python is indeed better than R, especially with sklearn
For other applications (especially charting and data manipulation with ggplot2 and dplyr respectively), R has an edge.
I've used that library, and I really don't like it at all. They tried to bring the R syntax to python, which ends up looking awful and is missing the point of the Graphical Grammar. In the same way that every language has it's own way of expressing control flow, every language should have it's own way to express the Graphical Grammar. We don't need R's GGplot2 in python, we need a pythonic way to express the Graphical Grammar.
If I had stronger python-fu I would love to build "GGPy".
Maybe. The plots look a lot like GGplot2 plots, any the syntax looks like python, but I haven't dug in to it to see if it builds plots using the Graphical Grammar under the hood.
we've shifted to plot.ly for visualization and moved away from expensive BI tools. We set up python/pandas scripts on CRON to output realtime data to our local plot.ly web server, which makes a local copy of the data and updates the chart. You simply embed the chart as an iframe where ever want internally, and BOOM, you've got a real time chart (beautiful I may add).
For those cases where I have to dip into R for specific functionality, I'll use Rpy2. Pandas has great support for translating Pandas data frames to R data frames!
I used to program solely in R, but after discovering pandas I really have no need to go back to R. My project workflow consists of several IPython notebooks+pandas+sklearn.
Works extremely well in production, as in, on a flask web server, as well.