[This article was first published on Coastal Econometrician Views, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
When we TATVA AI visit our clients, often both data scientists and higher management ask us, how we deal with both Python and R simultaneously for client requests; as there is no universal preference among clients.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Though solution is not straight forward, however, I suggest to exploit common libraries for quick deployments, such as, dfply (python) and dplyr (R). Below is a quick example:
< !-- HTML -->## R Code
library(dplyr)
testdata %>% filter(col.1==10)
## Python Code
from dfply import *
testdata >> filter_by(X.col.1==10)
Those who have not yet experience this can try out now and let me know your experiences at either info@tatvaai.com or mavuluri.pradeep@gmail.com
Happy R and Python Programming!
To leave a comment for the author, please follow the link and comment on their blog: Coastal Econometrician Views.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.