nan

If two Objects added together equal NaN (not a number), which is technically of type number , then why does getting the type of two Objects added together result in "string" ? I will express this via the REPL: > {} + {} > NaN ok. two objects added together creates NaN > typeof(NaN) > "number" ok. we know that NaN's type is "number" > typeof({} + {}) > "string" wait. shouldn't this have been "number" also? I'm aware that javascript has a less then desireable type system, but I'm confused as to what's happening here. Is the type being converted from number to string for some reason? Is number

I want to set specific values in a numpy array to NaN (to exclude them from a row-wise mean calculation). I tried import numpy x = numpy.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]]) cutoff = [5, 7] for i in range(len(x)): x[i][0:cutoff[i]:1] = numpy.nan Looking at x , I only see -9223372036854775808 where I expect NaN . I thought about an alternative: for i in range(len(x)): for k in range(cutoff[i]): x[i][k] = numpy.nan Nothing happens. What am I doing wrong? Divakar Vectorized approach to set appropriate elements as NaNs @unutbu's solution must get rid of the value

Possible Duplicate: Dealing with NaN’s in matlab functions Is there a one line command that allows you to take the elementwise average of a matrix (ignoring NaN 's) in Matlab? For example, >> A = [1 0 NaN; 0 3 4; 0 NaN 2] A = 1 0 NaN 0 3 4 0 NaN 2 So the mean(A) should equal (1+3+2+4+0+0+0)/7 = 1.4286 Also, I don't have access to the stats toolbox so I cannot use nanmean() You can use isnan() to filter out the unwanted elements: mean(A(~isnan(A))) nanmean Performs just like mean , but ignoring nans. For example: >> A = [1 0 NaN; 0 3 4; 0 NaN 2] A = 1 0 NaN 0 3 4 0 NaN 2 >> nanmean(A) ans = 0

I would like to have some kind of project-wide fail fast mechanism (maybe a RuntimeException ) for any code that causes assignment of NaN . In my project NaN is never a valid value. I realise I could add asserts (using isNaN ) or other tests throughout but I want to know if there is a more elegant way. Yes, you can use AspectJ (aspect oriented programming) to throw an error whenever a value is set to NaN. Essentially, you want to intercept whenever a value is set, and perform some other function. We've done similar things in our codebase ... but I can't give you much help outside of that. No -

I get some surprising results when trying to evaluate logical expressions on data that might contain nan values (as defined in numpy). I would like to understand why this results arise and how to implement the correct way. What I don't understand is why these expressions evaluate to the value they do: from numpy import nan nan and True >>> True # this is wrong.. I would expect to evaluate to nan True and nan >>> nan # OK nan and False >>> False # OK regardless the value of the first element # the expression should evaluate to False False and nan >>> False #ok Similarly for or : True or nan >>>

So, the R expression and its output is as follows: > (4-7)^1.3 [1] NaN Any ideas how to solve this in R? The answer is a complex number, so you need to give it a complex argument: > (4-7+0i)^1.3 [1] -2.451751-3.374545i but remember this is only one root... I quote from Wikipedia, especially the bold text (http://en.wikipedia.org/wiki/Exponentiation): The IEEE 754-2008 floating point standard is used in the design of most > floating point libraries. It recommends a number of different functions for computing a power:[19] pow treats 00 as 1. This is the oldest defined version. If the power is an

I want to delete all geospacial fields that are NaN so I can properly index my MongoDB. How do I find all documents that have this though? db.collection.find( { field: {$not: { $type: 1 } } }) won't work since NaN is of type Number. db.collection.find( { field: NaN }) actually works although I couldn't find any documentation on it Solution for PyMongo: # If you're alright with numpy as a dependency import numpy as np db.collection.find({ 'field': np.nan }) or db.collection.find({ 'field': float('nan') }) FYI: I ran into this issue because mongoexport (mongo 3.0.7) wrote NaN into the JSON files