Looping In Python And R

Dmitry Kisler has a quick comparison of looping speed in Python and R:

This post is about R versus Python in terms of the time they require to loop and generate pseudo-random numbers. To accomplish the task, the following steps were performed in Python and R (1) loop 100k times (ii is the loop index) (2) generate a random integer number out of the array of integers from 1 to the current loop index ii (ii+1 for Python) (3) output elapsed time at the probe loop steps: ii (ii+1 for Python) in [10, 100, 1000, 5000, 10000, 25000, 50000, 75000, 100000]

The findings were mostly unsurprising to me, though there was one unexpected twist.

Related Posts


John Mount explains the vtreat package that he and Nina Zumel have put together: When attempting predictive modeling with real-world data you quicklyrun into difficulties beyond what is typically emphasized in machine learning coursework: Missing, invalid, or out of range values. Categorical variables with large sets of possible levels. Novel categorical levels discovered during test, cross-validation, or […]

Read More

R 3.4.4 Now Available

David Smith notes that R 3.4.4 is now generally available: R 3.4.4 has been released, and binaries for Windows, Mac, Linux and now available for download on CRAN. This update (codenamed “Someone to Lean On” — likely a Peanuts reference, though I couldn’t find which one with a quick search) is a minor bugfix release, and shouldn’t cause […]

Read More

Leave a Reply

Your email address will not be published. Required fields are marked *


February 2018
« Jan Mar »