you can use the 'converters' keyword in numpy.loadtxt. first define a function to convert a string in a float, that can handle your 'N/A' entries:

def converter(x): if x == 'N/A': return numpy.nan else: return float(x)

then use:

...

...

numpy.loadtxt('test.dat', converters={1:converter,2:converter})

array([[ 1., 2., NaN, 4.], [ 1., NaN, 3., 4.]])

where the file test.dat I used looks like this: 1 2 N/A 4 1 N/A 3 4

hth, L.

On Sun, Jul 13, 2008 at 09:50:24AM -0400, Bryan Fodness wrote:

I am using loadtxt and I have missing values that are show up as N/A.

I get a,

ValueError: invalid literal for float(): N/A

Is there a way to ignore these?

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