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16 changes: 8 additions & 8 deletions docs/source/user/5minguide.rst
Original file line number Diff line number Diff line change
Expand Up @@ -138,21 +138,21 @@ For example::
return a + trace

# DO NOT REPORT THIS... COMPILATION TIME IS INCLUDED IN THE EXECUTION TIME!
start = time.time()
start = time.perf_counter()
go_fast(x)
end = time.time()
print("Elapsed (with compilation) = %s" % (end - start))
end = time.perf_counter()
print("Elapsed (with compilation) = {}s".format((end - start)))

# NOW THE FUNCTION IS COMPILED, RE-TIME IT EXECUTING FROM CACHE
start = time.time()
start = time.perf_counter()
go_fast(x)
end = time.time()
print("Elapsed (after compilation) = %s" % (end - start))
end = time.perf_counter()
print("Elapsed (after compilation) = {}s".format((end - start)))

This, for example prints::

Elapsed (with compilation) = 0.33030009269714355
Elapsed (after compilation) = 6.67572021484375e-06
Elapsed (with compilation) = 0.33030009269714355s
Elapsed (after compilation) = 6.67572021484375e-06s

A good way to measure the impact Numba JIT has on your code is to time execution
using the `timeit <https://docs.python.org/3/library/timeit.html>`_ module
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