Introducing Wakari by Paddy Mullen.
From the post:
We are proud to introduce Wakari, our hosted Python data analysis environment.
We believe that programmers, scientists, and analysts should spend their time writing code, not working to setup a system. Data should be shareable, and analysis should be repeatable. We built Wakari to achieve these goals.
Sample Use Cases
We think Wakari will be useful for many people in all types of industries. Here are just three of the many use cases that Wakari will help for.
Learning python
If you want to learn Python, Wakari is the perfect environment. Wakari makes it easy to start writing code immediately, without needing to install any software on your own computer. You will be able to show instructors your code and get feedback as to where you’re getting hung up.
Academia
If you’re an academic frustrated by setting up computing environments and annoyed that your colleagues can’t easily run your code, Wakari is made for you. Wakari handles all of the problems related to setting up a Python scientific computing environment. Because Wakari builds on Anaconda, useful libraries like SciKit, mpi4py and NumPy are right at your fingertips without compilation gymnastics.
Since you run code on our servers through a web browser, it is easy for your colleagues to re-run your code to repeat your analysis, or try out variations on their own. At Continuum, we understand that reproducibility is an important part of the scientific process that your results be consistent for reviewers and colleagues.
Finance
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For users who work in finance, Wakari lets you avoid the drudgery of emailing Excel files to share analysis, data, and visuals. Since data feeds are integrated into the Python environment, it is effortless to import financial data into your coding environment. When it is time to share results, you can email colleagues a URL that links to running code. Interactive charts are easy to create and share from Python. Since Wakari is built on top of Anaconda, great libraries like NumPy, Scipy, Matplotlib, and Pandas are already installed. Wakari includes support for Anaconda’s multiple environments, so you can easily change between versions of Python (including Python 3.3!) and versions of fundamental libraries.
Interesting in part because Wakari further blurs the distinction between “your” computer and the “host.”
If you are performing analysis on data (assuming a high speed connection), does it really matter if “your” computer is running the analysis or simply displaying the results from some remote host?
Not a completely new concept for those of you who remember desktops that booted from servers.
Interesting as well as a model for how authoring aids for topic maps could be delivered (or at least their results) to topic map authors.
Want a concordance of text at Y location? Enter the URI. Want other NLP routines? Choose from this list. Separate and apart from any authoring engine. (Its called modularity.)