title: Scale(Normalise) a column in SPARK Dataframe - Pyspark
body: <p>I am trying to normalize a column in SPARK DataFrame using python.</p>
  
  <p>My dataset:</p>
  
  <pre><code>--------------------------
  userID|Name|Revenue|No.of.Days|
  --------------------------
  1      A     12560    45
  2      B     2312890  90
  .      .       .       .
  .      .       .       .
  .      .       .       .
  --------------------------
  </code></pre>
  
  <p>In this dataset, except the userID and Name, I have to normalize the Revenue and No.of Days.</p>
  
  <p>The output should look like this</p>
  
  <hr>
  
  <pre><code>userID|Name|Revenue|No.of.Days|
  --------------------------
  1      A     0.5     0.5
  2      B     0.9       1
  .      .       1     0.4
  .      .     0.6       .
  .      .       .       .
  --------------------------
  </code></pre>
  
  <p>The formula used to calculate or normalizing the values in each column is</p>
  
  <pre><code>val = (ei-min)/(max-min)
  ei = column value at i th position
  min = min value in that column
  max = max value in that column
  </code></pre>
  
  <p>How can I do this in easy steps using PySpark? </p>