Try numpy.clip:

>>> import numpy
>>> a = numpy.arange(-10, 10)
>>> a
array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,
         3,   4,   5,   6,   7,   8,   9])
>>> a.clip(0, 10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

You can clip only the bottom half with clip(0).

>>> a = numpy.array([1, 2, 3, -4, 5])
>>> a.clip(0)
array([1, 2, 3, 0, 5])

You can clip only the top half with clip(max=n). (This is much better than my previous suggestion, which involved passing NaN to the first parameter and using out to coerce the type.):

>>> a.clip(max=2)
array([ 1,  2,  2, -4,  2])

Another interesting approach is to use where:

>>> numpy.where(a <= 2, a, 2)
array([ 1,  2,  2, -4,  2])

Finally, consider aix's answer. I prefer clip for simple operations because it's self-documenting, but his answer is preferable for more complex operations.

You can also use np.where to replace a number with NaN.

arr = np.where(arr==NDV, np.nan, arr)

For example, the following result can be obtained via

arr = np.array([[1, 1, 2], [2, 0, 1]])
arr = np.where(arr==1, np.nan, arr)

This creates a new copy (unlike A[A==NDV]=np.nan) but in some cases that could be useful. For example, if the array was initially an int dtype, it will have to converted into a float array anyway (because replacing values with NaN won't work otherwise) and np.where can handle that.