matplotlib & python for powerful data visualization

Here is an example of data that isn't obvious to analyze:
What is the gain and lost effect of percentage of seats in a point of view of proportional representation? Percentage of seats is usually chosen in legislative assemblies. It is the process used in Canadian and Québec elections.

Powerful visualization allow you to see easily the effect. Python & matplotlib is an amazing combination to do so. It took me 20 minutes to allow me to visualize the effect in federal and Quebec election of 2008.
Upper graph (seats vs votes) shows the lost of proportional vote % if you use a seats approach. As an example, liberals gain ~11% and ADQ lost of ~11%.
Lower graph (lost seats vs votes). The real impact of party is the ratio of this lost on their real vote proportion. In this example, it is a gain of ~25% for each Liberals votes (11/(66/125)) and a lost of 66% for the ADQ and ~88% for QS.
Basically:

• In Canadian election, PC & BQ gain power but BQ way more in proportion and Greens lost everything
  
• In Quebec election: QS & ADQ lost lot of power and PQ and LIB gain it: it might explain why they aren't talking of changing election formula
• Matplot lib and python is an amazing combination to automate data visualization
  

to get the code do:

svn co https://mlboost.svn.sourceforge.net/svnroot/mlboost/elections
python elections/seats_vs_prop.py

Gerrymandering Explained (youtube;

Gerrymandering - another reason why rep democracy is fundamentally corrupt http://bit.ly/qO4mpH)