7 ms·
I actually use R mostly because of its data.table package. It is much faster and more concise than pandas, which is a nightmare to work with. Sure, you can get
by CapmCrackaWaka 7y ago
I actually use R mostly because of its data.table package. It is much faster and more concise than pandas, which is a nightmare to work with. Sure, you can get the job done in pandas, but you often have to wait ~10x longer for your commands to run and sometimes, I simply cannot use pandas at all because I run out of memory.
https://h2oai.github.io/db-benchmark/ https://h2oai.github.io/db-benchmark/
People are usually pretty surprised when I take the stance that R is faster than Python for the things most people actually care about, which is data manipulation and model building. Python has its datatable library which is approaching data.tables speed however it is very much a work in progress, and does not have very useful features yet.
- qwhelan 7y agoFWIW it looks like pandas is slow/OOM-ing because the benchmarks solely use Categoricals, which aren't as heavily used by pandas users compared to R. In particular, I suspect the benchmark sizing is forcing falling back from numpy's int64 to Python ints as categorical labels, which easily could explain a 10x or more differential.