On Tue, May 6, 2008 at 9:53 AM, Keith Goodman <[EMAIL PROTECTED]> wrote:

> On Tue, May 6, 2008 at 9:45 AM, Anne Archibald > <[EMAIL PROTECTED]> wrote: > > In fact, if you want to use empty() down the road, it may > > make sense to initialize your array to zeros()/0., so that if you ever > > use the values, the NaNs will propagate and become obvious. > > Numpy has ones and zeros. Could we add a nans? > > I often initialize using x = nan * ones((n ,m)). But if it's in a > loop, I'll avoid one copy by doing > > x = np.ones((n, m)) > x *= np.nan > > To many on the list using nans for missing values is like chewing gum > you found on the sidewalk. But I use it all the time so I'd use a > nans. Why don't you just roll your own? >>> def nans(shape, dtype=float): ... a = np.empty(shape, dtype) ... a.fill(np.nan) ... return a ... >>> nans([3,4]) array([[ NaN, NaN, NaN, NaN], [ NaN, NaN, NaN, NaN], [ NaN, NaN, NaN, NaN]]) -- . __ . |-\ . . [EMAIL PROTECTED]

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