title: Subset dask dataframe by column position
body: <p>Once I have a dask dataframe, how can I selectively pull columns into an in-memory pandas DataFrame? Say I have an N x M dataframe. How can I create an N x m dataframe where m &lt;&lt; M and is arbitrary.</p>
  
  <pre><code>from sklearn.datasets import load_iris
  import dask.dataframe as dd
  
  d = load_iris()
  df = pd.DataFrame(d.data)
  ddf = dd.from_pandas(df, chunksize=100)
  </code></pre>
  
  <h3>What I would like to do:</h3>
  
  <pre><code>in_memory = ddf.iloc[:,2:4].compute()
  </code></pre>
  
  <h3>What I have been able to do:</h3>
  
  <pre><code>ddf.map_partitions(lambda x: x.iloc[:,2:4]).compute()
  </code></pre>
  
  <p><code>map_partitions</code> works but it was quite slow on a file that wasn't very large. I hope I am missing something very obvious. </p>