message: made performance comparison write up more clear
diff: @@ -136,5 +136,8 @@ user 2m54.362s
  sys 2m14.951s
  ```
  <B>The interesting part:</B><BR>
  -In case of Pandas, allocating memory + random number generation and calculating means take almost the same time.<BR>
  -In case of DataFrame 85% of the time is spent in allocating memory + random number generation.
  +1. Pandas script, I believe, is entirely implemented in Numpy which C.
  +2. In case of Pandas, allocating memory + random number generation and calculating means take almost the same time.
  +3. In case of DataFrame 85% 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 8x faster than parts of Pandas that are implemented in Numpy. I leave parts of Pandas that are purely in Python to imagination.
  +
  