I have the code below and I would like to convert all zero's in the data to None's (as I do not want to plot the data here in matplotlib). However, the code is notworking and 0. is still being printed
sd_rel_track_sum=np.sum(sd_rel_track, axis=1)
for i in sd_rel_track_sum:
print i
if i==0:
i=None
return sd_rel_track_sum
Can anyone think of a solution to this. Or just an answer for how I can transfer all 0 to None. Or just not plot the zero values in Matplotlib.
Why not use numpy for this?
>>> values = np.array([3, 5, 0, 3, 5, 1, 4, 0, 9], dtype=np.double)
>>> values[ values==0 ] = np.nan
>>> values
array([ 3., 5., nan, 3., 5., 1., 4., nan, 9.])
It should be noted that values cannot be an integer type array.
Using numpy is of course the better choice, unless you have any good reasons not to use it ;) For that, see Daniel's answer.
If you want to have a bare Python solution, you might do something like this:
values = [3, 5, 0, 3, 5, 1, 4, 0, 9]
def zero_to_nan(values):
"""Replace every 0 with 'nan' and return a copy."""
return [float('nan') if x==0 else x for x in values]
print(zero_to_nan(values))
gives you:
[3, 5, nan, 3, 5, 1, 4, nan, 9]
Matplotlib won't plot nan (not a number) values.
float('nan')s, that should do the trick. ; )