add final exercise

README.md

@@ -72,9 +72,17 @@ Setup:
     - … unless of course you plan to mostly loop *across* time series :)
     - watch out when migrating code from MATLAB® or to `pandas.DataFrame` ➔ they store data in memory using the opposite convention, the column-major order!!!
 
-Notes on the [Python benchmark](benchmark_python/):
+## A final exercise to put it all together
+  - fork this repo to your account and clone your fork on the laptop
+  - create a branch `ex` and switch to it
+  - work on the [exercise](exercise.ipynb)
+  - push your solution to yuor fork and create a Pull Request to this repo
 
-  - while running it attached to one core on my laptop, the core was running under a constant load of 100% (almost completely user-time) and at a fixed frequency of 3.8 GHz, where the theoretical max would be 5.2 GHz
+
+
+## Notes on the benchmarks
+
+  - while running the benchmarks attached to one core on my laptop, the core was running under a constant load of 100% (almost completely user-time) and at a fixed frequency of 3.8 GHz, where the theoretical max would be 5.2 GHz
     
     ➔ the CPU does not "starve" because it scales its speed down to match the memory throughput? Or I am misinterpreting this? This problem which at first sight should be perfectly memory-bound, becomes CPU-bound, or actually, exactly balanced? From the [Intel documentation](https://lenovopress.lenovo.com/lp1836-tuning-uefi-settings-4th-gen-intel-xeon-scalable-processor):
     > **Energy Efficient Turbo**