title: Scipy - statistical tests between two groups
body: <p>I have two samples from the population of neurons in the brain, each sample consisting of a thousand neuron instances, of categories:</p>
  <ol>
  <li>cerebellum</li>
  <li>cortex</li>
  </ol>
  <p>Now I'm extracting multiple metrics for each sample using complex network analysis, for example, neuron degree of connectivity <code>k</code>, a discreet number n = 0, 1, ...., n, or clustering coefficient <code>C</code>, a continous value between 0.00000 and 1.00000.</p>
  <p><code>df.sample(3)</code> (where web is category) in my pandas dataframes:</p>
  <p>cortex:</p>
  <pre><code>         web    k   clustering_coeff
  3080    cortex  6.0         0.733333        
  2951    cortex  11.0        0.428571    
  1435    cortex  5.0         0.563571    
  
  ...
  </code></pre>
  <p>cerebellum</p>
  <pre><code>815 cerebellum  10.0        0.533333    
  850 cerebellum  9.0         0.416667    
  1213 cerebellum 7.0         0.454545
  ...
  </code></pre>
  <hr />
  <p>How can I use <code>scipy</code> <code>stats</code> methods to I compare both metrics in order to know if theres a statistically significant difference between the two gropus?</p>
  <p>Assuming a distribution close to Gaussian, but skewed to the right, I'm not sure what is the best approach. Parametric, Non-Parametric, T-test and so on.</p>
  <p>Any ideas?</p>