title: Normalizing values in each column of a pandas dataframe
body: <p>I have a huge dataframe and trying to figure out the most efficient way to normalize each value in a column and in turn go through all the columns using the mean and std.dev. </p>
  
  <p>A sample of the dataframe is as follows:</p>
  
  <pre><code>    TimeStamp          340          341          342          343       
  0    10:27:30     1.953036     2.110234     1.981548     1.705684  
  1    10:28:30     1.973408     2.046361     1.806923     1.496244   
  2    10:29:30     0.000000     0.000000     0.014881     0.198947   
  3    10:30:30     2.567976     3.169928     3.479591     3.557881   
  4    10:31:30  4415.498729  5075.996948  5653.925541  6133.202200   
  5    10:32:30  4473.930295  5146.802497  5736.030854  6224.380260
  </code></pre>
  
  <p>I want to:
  find a mean for col["340"]: </p>
  
  <pre><code>    for column in df.iteritems():
  df.mean()
  df.std()
  </code></pre>
  
  <p>...further calculations for normalizing</p>
  
  <p>However, I am extremely new to pandas and it is not working....:(
  I can find the mean per col but i have 2500 cols</p>