title: Row by row processing of a Dask DataFrame
body: <p>I need to process a large file and to change some values.</p>
  
  <p>I would like to do something like that:</p>
  
  <pre><code>for index, row in dataFrame.iterrows():
  
          foo = doSomeStuffWith(row)
          lol = doOtherStuffWith(row)
  
          dataFrame['colx'][index] = foo
          dataFrame['coly'][index] = lol
  </code></pre>
  
  <p>Bad for me, I cannot do  <em>dataFrame['colx'][index] = foo</em>!</p>
  
  <p>My number of row is quite large and I need to process a large number of column. So I'm afraid that dask may read the file several times if I do one dataFrame.apply(...) for each column.</p>
  
  <p>Other solutions are to manually break my data into chunks and to use pandas or to just throw anything in a database. But it could be nice if I may keep using my .csv and let dask do the chunk processing for me!</p>
  
  <p>Thank for your help.</p>