Numpy - Replace a number with NaN

Question

I am looking to replace a number with NaN in numpy and am looking for a function like numpy.nan_to_num, except in reverse.

The number is likely to change as different arrays are processed because each can have a uniquely define NoDataValue. I have see people using dictionaries, but the arrays are large and filled with both positive and negative floats. I suspect that it is not efficient to try to load all of these into anything to create keys.

I tried using the following and numpy requiring that I use any() or all(). I realize that I need to iterate element wise, but hope that a built-in function can achieve this.

def replaceNoData(scanBlock, NDV):     for n, i in enumerate(array):         if i == NDV:             scanBlock[n] = numpy.nan 

NDV is GDAL's no data value and array is a numpy array.

Is a masked array the way to go perhaps?

Answer 1

A[A==NDV]=numpy.nan 

A==NDV will produce a boolean array that can be used as an index for A