Is it me, or are the print options only respected when we want to print arrays? Here's another example of "bad" behavior with scalars:

>>> import numpy as np
>>> x = np.float64(0.12345678901234567890123456789)
>>> m = np.array([x])
>>>
>>> str(x) # default precision is 8 decimal places
'0.123456789012' # oops
>>> str(m) # printing an array containing 'x' respects the precision
'[ 0.12345679]'

Is anyone working on this issue?
If not i would like to give it a go

I don't know of anyone who is. Generally, IIUC the etiquette is that unless someone is really talking about it or has already opened a PR to fix the issue, it is completely up for grabs by anyone. So go for it! Good luck!

Please consider fixing #6136 at the same time :)

It seems the printoptions are set in numpy/core/arrayprint.py. It has functions defined for printing an array but not single numpy values. Is this behavior intended? I mean is printoptions set to work only for arrays? Here is link to an answered question on stackoverflow. Please tell me if I am missing something

Judging from the labeling by @rgommers , I suspect not. I think you may very well have to add a good amount of new code in order to enforce printing for numbers.

I think the float values are also printed by array2string function defined in numpy/core/arrayprint.py.
This is where I think it happens

You still have to cast a as an array object. It's just one without a shape, so the function still does not apply to pure literals like a = 5 for example.

I just ran into this issue. printoptions should be respected by all three:

In [21]: np.set_printoptions(precision=3)

In [22]: print(np.ones(()) * (0.123456789123))
0.123456789123

In [23]: print(np.float32(0.123456789123))
0.123457

In [24]: print(np.ones((1,)) * (0.123456789123))
[ 0.123]

Closed by #9941