sirrice
Re:Using Altair for most of my visualization in Python
on Tuesday May 07, 2019 @08:47AM (#19848851)
> Sadly, in Python, we do not have a ggplot2.
https://github.com/sirrice/pygg provides the ggplot2 syntax in Python as a wrapper around Wickham's R implementation. It is useful if you 1) want the R syntax, 2) program in Python, 3) just want static plots.
Re:Plotnine: A grammar of graphics for Python
on Saturday May 27, 2017 @12:42PM (#14430779)
Recreating and keeping up with Hadley's hard work is challenging, particularly because ggplot2's layout and extensions are really nice and continue to evolve.
As an alternative that preserves the full power of Wickham's implementation, pygg[1] is a Python wrapper that provides R's ggplot2 syntax in Python and runs everything in R.
Re:Show HN: SpreadsheetDB – A database that you can query with spreads...
on Tuesday March 28, 2017 @10:54PM (#13982956)
There's some related research out of UIUC that's pretty neat.
http://dataspread.github.io/
Re:Queues and databases
on Monday July 14, 2014 @11:54AM (#8031479)
Relevant paper: Queues are databases, Jim Gray
Re:LevelDB Benchmarks
on Friday July 29, 2011 @03:11PM (#2823332)
It would be nice to see longer running experimentsto see how a) long term usage (compaction in LSM trees) and b) disk interactions (benefits of compression) impact performance.
For example, in the large values experiment, the experiment executes 1000 writes. However if the results report LevelDB as 1060 ops/sec. Assuming op == write, then the experiment ran for all of 1 second. This shows great instantaneous performance, but what if it kept running?
Additionally, it appears that no-compression is the way to go, which makes sense for small values and an in-memory experiment, but is that the case when disk comes into play?
