title: Read CSV file that needs data sanitization prior to loading into dataframe
body: <p>I'm reading a CSV file into pandas. The issue is that the file needs removal of rows and calculated values on the other rows. My current idea starts like this</p>
  
  <pre><code>    with open(down_path.name) as csv_file:
      rdr = csv.DictReader(csv_file)
      for row in rdr:
          type = row['']
          if type == 'Summary':
              current_ward = row['Name']
          else:
              name = row['Name']
              count1 = row['Count1']
              count2 = row['Count2']
              count3 = row['Count3']
              index_count += 1
          # write to someplace
  
  ,Name,count1,count2,count3
  Ward Summary,Aloha 1,35,0,0
  Individual Statistics,John,35,0,0
  Ward Summary,Aloha I,794,0,0
  Individual Statistics,Walter,476,0,0
  Individual Statistics,Deborah,182,0,0
  </code></pre>
  
  <p>The end result needs to end up in a dataframe that i can concatenate to an existing dataframe.</p>
  
  <p>Braindead way to do this is simply do my conversions and create a new CSV file, then read that in. Seems like a non-pythonic way to go.</p>
  
  <p>Need to take out the summary lines, combine those with similar names (Aloha 1 and Aloha I), remove the individual stat info and put the Aloha 1 label on each of the individuals. Plus i need to add which month this data is from. As you can see the data needs some work :)</p>
  
  <p>desired output would be
      Jan-16, Aloha 1, John, 1,2,3</p>
  
  <p>Where the Aloha 1 comes from the summary line above it</p>