numpy and filtering (was: Fastest way to store ints and floats ondisk)

[Laszlo Nagy]

Attached there is an example program that only requires numpy. At the
end I have two numpy array:

rdims:

[[3 1 1]
[0 0 4]
[1 3 0]
[2 2 0]
[3 3 3]
[0 0 2]]
rmeas:

[[100000.0 254.0]
[40000.0 200.0]
[50000.0 185.0]
[5000.0 160.0]
[150000.0 260.0]
[20000.0 180.0]]
I would like to use numpy to create statistic, for example the mean
value of the prices:

>>rmeas[:,0] # Prices of cars

array([100000.0, 40000.0, 50000.0, 5000.0, 150000.0, 20000.0],
dtype=float96)

>>rmeas[:,0].mean() # Mean price

60833.3333333333333321

However, I only want to do this for 'color=yellow' or 'year=2003,
make=Ford' etc. I wonder if there a built-in numpy method that can
filter out rows using a set of values. E.g. create a view of the
original array or a new array that contains only the filtered rows. I
know how to do it from Python with iterators, but I wonder if there is a
better way to do it in numpy. (I'm new to numpy please forgive me if
this is a dumb question.)

Thanks,

Laszlo