message: Add measurement with new timings
diff: @@ -86,24 +86,23 @@ user 3m14.250s
  sys 0m25.983s
  -$python3 test/pandas_performance_2.py
  -Starting ... 1629755776
  -All memory allocations are done. Calculating means ... 1629755930
  --0.2735071430490835, 1.032693628504156, 1.1017931372529224
  -1629756083 .. Done
  -
  -$Release/bin/dataframe_performance_2
  -Starting ... 1629755620
  -All memory allocations are done. Calculating means ... 1629755749
  -0.28646, 0.891678, 0.859827
  -1629755755 ... Done
  -
  +$ python3 test/pandas_performance.py
  +Starting ... 1629817655
  +All memory allocations are done. Calculating means ... 1629817883
  +6.166675403767268e-05, 1.6487168460770107, 0.9999539627671375
  +1629817894 ... Done
  +
  +$ Release/bin/dataframe_performance
  +Starting ... 1629818332
  +All memory allocations are done. Calculating means ... 1629818535
  +1, 1.64873, 1
  +1629818536 ... Done
  ```
  <B>The Interesting Part:</B><BR>
  1. Pandas script, I believe, is entirely implemented in Numpy which is in C.
  2. In case of Pandas, allocating memory + random number generation takes almost the same amount of time as calculating means.
  3. In case of DataFrame ~90% of the time is spent in allocating memory + random number generation.
  -4. You load data once, but calculate statistics many times. So DataFrame, in general, is about ~25x faster than parts of Pandas that are implemented in Numpy. I leave parts of Pandas that are purely in Python to imagination.
  +4. You load data once, but calculate statistics many times. So DataFrame, in general, is about ~11x faster than parts of Pandas that are implemented in Numpy. I leave parts of Pandas that are purely in Python to imagination.
  5. Pandas process image at its peak is ~105GB. C++ DataFrame process image at its peak is ~56GB.
  ---
  