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Python is pretty slow.

Use Numpy for data analysis. It runs on ATLAS, an open source BLAS implementation and is plenty fast.

Having a working Python implementation will also give you a better understanding of which parts are performance/memory sensitive. This may help guide you while rewriting in a different language, or you may find that you can achieve your goals just by hot spot optimizing your Python code using some of these fine tools:

http://www.numpy.org/ can give you compact arrays of unboxed types and fast operations over them.
http://cython.org/ is an amazing and versatile tool which allows you to compile your Python code, optionally add type information, optionally manage memory yourself, optionally interact with C/C++ code very easily.
OpenCL/CUDA if your work can take advantage of them.


If the standard Python runtime isn't simply too big for your project I can't recommend Cython highly enough. Only tackling the parts that need it is one of the keys to successful optimization and Cython lets you do just that even if it is just one loop in an otherwise pure Python file.

quietnet: Simple chat program using inaudible sounds

##

Simple chat program using near ultrasonic frequencies. Works without Wifi or Bluetooth and won't show up in a pcap.

Note: If you can clearly hear the send script working then your speakers may not be high quality enough to produce sounds in the near ultrasonic range.

https://github.com/Katee/quiet...

##

- Installation

Quietnet is dependant on pyaudio[1] and Numpy[2].

[1] http://people.csail.mit.edu/hu...
[2] http://www.numpy.org/

##

Better Projects

Quietnet is just a toy! Take a look at minimodem[3] or gnuradio[4] if you need something robust.

[3] http://www.whence.com/minimode...
[4] http://gnuradio.org/

quietnet: Simple chat program using inaudible sounds

##

Simple chat program using near ultrasonic frequencies. Works without Wifi or Bluetooth and won't show up in a pcap.

Note: If you can clearly hear the send script working then your speakers may not be high quality enough to produce sounds in the near ultrasonic range.

https://github.com/Katee/quiet...

##

- Installation

Quietnet is dependant on pyaudio[1] and Numpy[2].

[1] http://people.csail.mit.edu/hu...
[2] http://www.numpy.org/

##

Better Projects

Quietnet is just a toy! Take a look at minimodem[3] or gnuradio[4] if you need something robust.

[3] http://www.whence.com/minimode...
[4] http://gnuradio.org/

quietnet: Simple chat program using inaudible sounds

##

Simple chat program using near ultrasonic frequencies. Works without Wifi or Bluetooth and won't show up in a pcap.

Note: If you can clearly hear the send script working then your speakers may not be high quality enough to produce sounds in the near ultrasonic range.

https://github.com/Katee/quiet...

##

- Installation

Quietnet is dependant on pyaudio[1] and Numpy[2].

[1] http://people.csail.mit.edu/hu...
[2] http://www.numpy.org/

##

Better Projects

Quietnet is just a toy! Take a look at minimodem[3] or gnuradio[4] if you need something robust.

[3] http://www.whence.com/minimode...
[4] http://gnuradio.org/

Since you mention VBA, I suspect that your data is in Excel spreadsheets? If you want to try to speed this up with minimum effort, then consider using Python with Pyvot to access the data, and then numpy/scipy/pandas to do whatever processing you need. This should give you a significant perf boost without the need to significantly rearchitecture everything or change your workflow much.

In addition, using Python this way gives you the ability to use IPython to work with your data in interactive mode - it's kinda like a scientific Python REPL, with graphing etc.

If you want an IDE that can connect all these together, try Python Tools for Visual Studio. This will give you a good general IDE experience (editing with code completion, debugging, profiling etc), and also comes with an integrated IPython console. This way you can write your code in the full-fledged code editor, and then quickly send select pieces of it to the REPL for evaluation, to test it as you write it.

(Full disclosure: I am a developer on the PTVS team)

I second Python: just type in the pseudocode you'd write on a piece of paper, and there is a good chance that it will work just like that in Python.

2D gaming using SDL (and OpenGL 3D, but you have to do the hard work yourself): Pygame
3D drawing/animation/gaming: Blender 3D
(I started by gaming, because that's a fun way to learn a language quickly)

Web: Django
Co-routines: Stackless Python
Maths: NumPy and SciPy
Networking: Twisted

That just scratches the outside of it, but have a look at the above to get an idea of the language.

And Python's documentation is quite good: brief, but everything you need is there - you just need less than you would expect at first. Here are some good tutorials:

Official Python Tutorial
Dive into Python
How to think like a computer scientist?

Hmmm, looks like I've turned into a Python fanboi... Be careful if you try Python, you could fall for it.

Real scientist use the Python programming language with Numpy and MatPlotLib :D

Ha! Real scientists don't use Perl, Python, or any of those namby-pamby programming languages. Real scientists use slide rules and a No. 2 pencil.

No Perl is. Real scientist use the Python programming language with Numpy and MatPlotLib :D

In my experience, scientists tend to use Excel VBA and incredibly convoluted batch files.

Disclaimer: I mostly met mad scientists.

No Perl is. Real scientist use the Python programming language with Numpy and MatPlotLib :D

Lots of things to like about Python, but NumPy is one of the better ones, IMHO. There is a largish and growing community of numerical jocks coalescing around around Python in scientific computation. NumPy makes Python into an "array language" (like Matlab, S-Plus/R, APL, etc. etc.) where the crunching is heavily optimized C code. Links to LAPACK et al., and Fortran wrappers exist in f2py and PyFort, for that old still-running-after-all-these-years code.

(Just another enthusiast, dabbling in the religious-war du jour. Flamage to /dev/null ;-)

I switched language when I left uni for the real world (IDL is nice but bloody expensive) and settled for python.
Actually, more like python+numeric+c

I usually experiment in pure python+numeric first and then optimise the parts that need in c. I really love how easy it is to interface c and python (especially compared to IDL). No problems passing numeric arrays to and from python, etc, etc...

Don't forget to check biopython which is always a good place to start. Dunno if there's anything for processing microarrays, though.