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Fri Jun 10 11:07:18 EDT 2016 · https://mail.python.org/pipermail/python-dev/2016-June/145015.html

I started to work on visualisation. IMHO it helps to understand the problem.

Let's create a large dataset: 500 samples (100 processes x 5 samples):
---
$ python3 telco.py --json-file=telco.json -p 100 -n 5
---

Attached plot.py script creates an histogram:
---
avg: 26.7 ms +- 0.2 ms; min = 26.2 ms

26.1 ms:   1 #
26.2 ms:  12 #####
26.3 ms:  34 ############
26.4 ms:  44 ################
26.5 ms: 109 ######################################
26.6 ms: 117 ########################################
26.7 ms:  86 ##############################
26.8 ms:  50 ##################
26.9 ms:  32 ###########
27.0 ms:  10 ####
27.1 ms:   3 ##
27.2 ms:   1 #
27.3 ms:   1 #

minimum 26.1 ms: 0.2% (1) of 500 samples
---

Replace "if 1" with "if 0" to produce a graphical view, or just view
the attached distribution.png, the numpy+scipy histogram.

The distribution looks a gaussian curve:
https://en.wikipedia.org/wiki/Gaussian_function

The interesting thing is that only 1 sample on 500 are in the minimum
bucket (26.1 ms). If you say that the performance is 26.1 ms, only
0.2% of your users will be able to reproduce this timing.

The average and std dev are 26.7 ms +- 0.2 ms, so numbers 26.5 ms ..
26.9 ms: we got 109+117+86+50+32 samples in this range which gives us
394/500 = 79%.

IMHO saying "26.7 ms +- 0.2 ms" (79% of samples) is less a lie than
26.1 ms (0.2%).

Victor
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[Python-Dev] Stop using timeit, use perf.timeit!