title: p -value adjustment Mann-Whitney U test in python
body: <p>I have a two-dimensional list file(name - 'hcl_file'). A shortened version of the file for clarity. Vertical-observations, horizontal-experiment number:</p>
  
  <p><code>ID     type    First       Second     Third</code></p>
  
  <pre><code>gerg    I   0.02695 0    0.00135      0.31312
  
  11P     I   0.02695 0    0.00135      0.31312
  
  112HP   II  0.02695 0    0.00135      0.31312
  
  1454HP  II  0.02695 0    0.00135      0.31312
  
  11544H  III 0.02695 0    0.00135      0.31312
  
  657BF   III 0.02695 0    0.00135      0.31312
  
  785DS   III 0.02695 0    0.00135      0.31312
  </code></pre>
  
  <p>I'm new to programming. Could you please tell me how I can calculate the significance of the differences between the type I,II,III, and then make an BH(Bennamini and Hochbberg) adjustment ? To avoid misunderstandings, let me clarify that we are conducting an experiment for different groups(I,II,III) and find the p-value for them, but then we repeat this for other data that requires adjustment of p-value for multiple comparisons. I have difficulty doing this in a cycle, please advise the direction of further movement. My script:</p>
  
  <p>for line in hcl_file:</p>
  
  <pre><code>     substrings = (len(line))
  </code></pre>
  
  <p>while j &lt; substrings:</p>
  
  <pre><code>k1 = []         # list of values in I-st group 
  
  k2 = []         II
  
  k3 = []         III
  
  for line in hcl_file:
  
          if line[1] == 'I':
  
                  v1 = float(line[j])
  
                  k1.append(v1)
  
          elif line[1] == 'II':
  
                  v2 = float(line[j])
  
                  k2.append(v2)
  
          elif line[1] == 'III':
  
                  v3 = float(line[j])
  
                  k3.append(v3)
  
  
  
  import pandas
  
  from scipy.stats import mannwhitneyu
  
  print(mannwhitneyu(k1, k2))
  
  j += 1
  </code></pre>