A native Python IDE built for data science
yhat.com
yhat.com
It's really cool to see. I like seeing science democratized, and Python is definitely a democratizing influence, and the fact that so much of it is open source is really fantastic. I've also noticed that a lot more domain experts are becoming programmer+domain experts through this evolution. It used to be that there were teams with a scientist to design it and one or more programmers to implement it, and that's becoming less of a requirement, which can accelerate the science-ing to a notable degree.
As a heads up, the setup workflow assumes you are on OS X, which may be a problem if it asks you to open a Terminal on Windows: http://i.imgur.com/nya50e4.png
Plus, for distributing binaries in Linux, instead of a zip file (tar.gz would be more common, too) it's better to support the main distros with a repository (PPA for Ubuntu, pacman for Arch, etc...) since it's way more user friendly every single time you want them to stay up to date.
thanks for trying it out!
matplotlib, however, fails to install completely with this method on Windows for subtle reasons. Filed: https://github.com/yhat/rodeo/issues/204
The documentation just points to a blog article on how to install matplotlib on Windows.
It's kind of weird to use but it works for the most part. You can clean up some data in python, then push the data over to a cell written in R to do some other evaluation, then push the results back over to python.
So here are some generalizations.
While in school, I noticed that the physics students were far more interested than the chemistry students, in math and computer stuff. Maybe we were computer science wannabees, or maybe we guessed (correctly in my case) that proficiency with computers would make us more employable. This was true in both undergrad and grad school.
And there's a long tradition of physicists stealing ideas from math and computation for solving physics problems. When I was in school, computation was considered to be a specialized branch of chemistry, but was at the forefront of physics.
Another difference is that the physics students were generally more interested in making our own tools. The current "maker" and "hacker" trends are old hat for small-lab experimental physicists.
Chemistry has always been a bigger field than physics, which I suspect has attracted more interest in making commercial equipment and software. I've noticed in an industrial setting, that managers are often looking for closed solutions that can't be modified by the user, either for regulatory reasons or adversarial labor-management attitudes. The industry wants your boss to think that letting you make your own tools is either dangerous, or a waste of your time.
In contrast, even in industry, physicists still have to make our own tools. And management already knows that we're freaks. ;-)
So the absence of FOSS tools for chemistry doesn't shock me.
It's interesting to consider in this context that workers owning the means of production is what links the GPL with Marxism.
http://openbabel.org/wiki/Main_Page
which has some python bindings built in. I set some of this up for myself during my PhD but it was occasionally kind of a pain sometimes to get it to work. Also at the time I was a bit of a noob so there's that :).
It has some nice features for handling chemical structures, I used it mostly for translating one format into another and computing fingerprints, but I think more can be done.
In general I'd agree with @analog31, biology has some good OSS tools, physics has some good OSS tools, but you get to the bridging discipline of chemistry and you find very few. My theory re. organic chemistry and biochemistry applications: it's way more profitable to be closed source. In contrast to the other two fields (gross generalization I know, but somewhat true) there's a very large market for commercial software in Pharma. If someone is willing to pay top dollar, especially an industry that is paranoid about IP and therefore tends to (rightly or wrongly) prefer closed, proprietary solutions, then that's where software will end up.
RStudio / Rodeo provides an interactive data analysis environment where multiple "views" are presented right in front of the user. A view could be a plot, a data frame or interactions between the code editor and the terminal. As a data analysis person it really helps to put the mental strain of code far away as possible and just explore the data.
Jupyter Notebook are nice but it can get overwhelming (too much scrolling) when things get complicated. Great teaching tool, however.
I think each of these tools have different use cases and it's great that Python is getting more user-friendly with the data science workflow.
After using it for 10 minutes, it feels identical to RStudio. That's a good thing.
Taking a look at the source (https://github.com/yhat/rodeo) it appears to be in all python.
I was under the (perhaps mistaken) impression that native referred to code which compiled to assembly.
I have this visceral reaction when I can tell something is based on Electron or IWebBrowser, 2.0.
I also tried setting the path the ~/.pyenv/shims/python, but that didn't work out either.
Also curious about the performance of data-frame viewer for large data sets.