change time.time() to time.perf_counter() in docs

* time.perf_counter() is preferred way to measure execution time
* changed style of string formatting and added 's' as the unit

docs/source/user/5minguide.rst

@@ -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