R vs. Python for Data Science: Summary of Modern Advances

Re:R vs. Python for Data Science: Summary of Modern Advances by vii on Wednesday December 07, 2016 @10:05PM (#13128120)

Both R and Python have gained libraries for SQL syntax queries on their datasets. These are powered by sqllite. As much of the data that gets into R or Python is obtained from a SQL query (or SparkQL etc.) it's awesome to be able to query, aggregate and even join internal datasets in the same way! https://github.com/ggrothendieck/sqldf is game changing.

The wealth of statistical packages available for R is astounding. Packages like GLMNet are made available by the top researchers in the field. In comparison, Python libraries tend to be relatively unsophisticated and less robust.

[ Parent | on HN ]

Re:R vs. Python for Data Science: Summary of Modern Advances by rz2k on Thursday December 08, 2016 @01:06AM (#13128719)

Sqldf is very tempting, but at least in the past it was so much, much slower than dplyr that the time to learn dplyr's syntax could pay off in a single project, or possibly even a single large operation.

[ Parent | on HN ]

Re:R vs. Python for Data Science: Summary of Modern Advances by bertha08 on Wednesday December 07, 2016 @11:10PM (#13128354)

Agreed. The ability to query with SQL syntax has been great.

[ Parent | on HN ]