title: Normalizing the data by using SWS method
body: <p>I am new in python world. I have dataset contain two columns and 1044 rows. I am trying to normalize my data but after google search, i came to know that different normalization methods are available to normalize our data. Normalization Methods are as follow:</p>
  <ol>
  <li>Whole Spectrum Normalization</li>
  <li>Sum of Whole Spectrum Normalization</li>
  <li>Single point Normalization</li>
  <li>Area under the peak</li>
  <li>Max within Range</li>
  </ol>
  <p>Which method is best, the <em><strong>SWS [Signal to Noise Ratio(SNR) across whole spectrum] method</strong></em> will help in this regard. I tried too much on google how to implement this method on my dataset. But didn't find any helpful material except one document in which the writer said,
  <em><strong>&quot; SNR were calculated via the SWS method on 30 randomly chosen spectres, with this process being repeated 20 times. Error bars represent the standard error across the 20 randomised subsets.&quot;</strong></em><br />
  <a href="https://i.stack.imgur.com/wYou4.png" rel="nofollow noreferrer">SWS Graph, SNR for different Normalization methods</a></p>
  <p>Can anyone guide me how to implement SWS method on the dataset. Kindly take random dataset consists of 2 columns and 1044 rows to illustrate this method implementation.
  Thanks.</p>