numpy solution:

Its pretty straightforward like this:

# convert frm to a numpy array:
frm = np.array(frm)
# create a copy of frm so you don't modify original array:
to = frm.copy()

# mask to, and insert your replacement values:
to[mask] = repl

Then to returns:

>>> to
array([  1, 200,   3, 400, 500])

pandas solution:

if your dataframe looks like:

>>> df
   column
0       1
1       2
2       3
3       4
4       5

Then you can use loc:

df.loc[mask,'column'] = repl

Then your dataframe looks like:

>>> df
   column
0       1
1     200
2       3
3     400
4     500