title: create columns in csv based on existing column without pandas
body: <p>I have the following structure in CSV
  <a href="https://i.stack.imgur.com/M5eBb.png" rel="nofollow noreferrer">Image Link</a></p>
  <p><a href="https://i.stack.imgur.com/M5eBb.png" rel="nofollow noreferrer">enter image description here</a></p>
  <p><strong>Values to be stored in CSV :</strong></p>
  <pre><code>hashes = [('layer_1', 'start_1','40' ,'34'), ('layer_2', 'start_2','12','45')]
  </code></pre>
  <p><strong>What i want</strong></p>
  <p>i need to write hashes values into csv based on id column</p>
  <pre><code>**id**
  Street_1
  S1L4
  S1L3
  S1L2
  S1L1
  Street_2
  S2L3
  S2L2
  Street_3
  S3L3
  S3L2
  Street_4
  S4L3
  S4L2
  Street_5
  S5L3
  S5L4
  S5L2
  </code></pre>
  <p><strong>conditions:</strong></p>
  <ul>
  <li><p>If id column contains Street_1,Street_2 and so on ...i need to leave it as blank</p>
  </li>
  <li><p>In id S1L1 represents the Start_1 and Layer_1. If these values found in the list like below</p>
  <p>('layer_1', 'start_1','40' ,'34')
  then i need to write the values 40 and 34 corresponding S1L1 row.</p>
  </li>
  </ul>
  <p><strong>What i have tried</strong></p>
  <pre><code>import csv
  hashes = [('layer_1', 'start_1','40' ,'34'), ('layer_2', 'start_2','12','45')]
  in_file = open(r'C:\Users\user\Desktop\python\input.csv','r')
  reader = csv.reader(in_file)
  out_file = open(r'C:\Users\user\Desktop\python\output.csv', 'w')
  writer = csv.writer(out_file)
  for row in reader:
      
      temp = p[0]
      temp2 = p[1]
      temp3 = p[2]
      temp4= row[14]
      print(temp4)
      if temp4[-1] == int(temp[-1]) and temp4[-3] == int(temp2[-1]):
          writer.writerow(temp3)
     
      
  in_file.close()    
  out_file.close()
  </code></pre>
  <p><strong>i am expecting following output</strong></p>
  <pre><code>id           percentage     volume
  Street_1        
  S1L4        
  S1L3        
  S1L2        
  S1L1              40         34
  Street_2        
  S2L3        
  S2L2              12         45
  Street_3        
  S3L3        
  S3L2        
  Street_4        
  S4L3        
  S4L2        
  Street_5        
  S5L3        
  S5L4        
  S5L2    
  </code></pre>