I'm not exactly sure what you mean, but perhaps the following is a useful example:
>>> import numpy as np
>>> x = np.arange(1,100)
>>> m = (sin(x)+1).mean()
>>> s = (sin(x)+1).std()
>>> print m, s
1.00383024876 0.710743876537
[edit after some further clarification]
If, however, you want the average per x-point of the various functions, something like this would work:
>>> y = np.array([sin(x), 3*sin(x), sin(x)+1])
>>> m = y.mean(axis=0)
>>> s = y.std(axis=0)
which would give you 100 means and 100 stddevs.
If you want the average of the combined function, you're essentially back to the first example:
>>> m = (sin(x) + 3*sin(x) + sin(x)+1).mean()
>>> s = (sin(x) + 3*sin(x) + sin(x)+1).std()
>>> print m, s
1.01915124381 3.55371938269
Which option is the one applicable for you depends on the context of your question; I have no clue about that.