Difference in precision in np.float64 and python float (64)

Question

Why does (or seems that) the python float has more precision than numpy.float64. Here I leave an example:

import numpy as np ann = 72.595895691 detections = np.array([0, 71.91000009999999], dtype=np.float64) group_det = [1] dist1 = [ann - detections[det] for det in group_det] dist = np.zeros(len(group_det), dtype=np.float64) for idx, det in enumerate(group_det): dist[idx] = ann - detections[det] print(f'dist1: {dist1}') print(f'dist {dist}')

The output of this code is:

dist1: [0.6858955910000049] dist [0.68589559]

My questions is why dist1 has more precision?

Note: The code seems like is doing things in a very complicated way, but I had to cut a little portion of my code with the same behaviour.

that's a display diffeeence

hpaulj
– hpaulj

2020-08-12 17:11:33 +00:00
Commented Aug 12, 2020 at 17:11 — hpaulj
– hpaulj, Commented Aug 12, 2020 at 17:11

Ehsan · Accepted Answer · 2020-08-12 17:25:00Z

As mentioned by @hpaulj in comments, it is not calculation precision. It is printing. Add this line to your code to see the precision you would like while printing in numpy:

np.set_printoptions(precision=16)

output of your code after above line:

dist1: [0.6858955910000049] dist [0.6858955910000049]

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