title: dask dataframe column renames are slow(er) when read from parquet
body: <p>I'm finding that <code>dask.dataframe.rename</code> significantly increases compute times whenever the dataframe is being read from a parquet file:</p>
  
  <pre><code>In [1]: import dask.dataframe as dd
     ...: df = dd.demo.daily_stock('GOOG', '2008', '2010', freq='1s', random_state=1234)
     ...: 
  
  In [2]: %time df.close.mean().compute()
  CPU times: user 7.73 s, sys: 1.15 s, total: 8.88 s
  Wall time: 3.5 s
  Out[2]: 452.30345234893554
  
  In [3]: %time df = df.rename(columns={col: col.upper() for col in df.columns}); df.CLOSE.mean().compute()
  CPU times: user 8.06 s, sys: 1.21 s, total: 9.27 s
  Wall time: 3.81 s
  
  In [4]: df.to_parquet('df', compression='GZIP')
     ...: df = dd.read_parquet('df')
     ...: 
  
  In [5]: %time df.CLOSE.mean().compute()
  CPU times: user 4.14 s, sys: 729 ms, total: 4.87 s
  Wall time: 2.1 s
  Out[5]: 452.30345234893554
  
  In [6]: %time df = df.rename(columns={col: col.lower() for col in df.columns}); df.close.mean().compute()
  CPU times: user 9.72 s, sys: 1.89 s, total: 11.6 s
  Wall time: 4.81 s
  </code></pre>
  
  <p>Notice that the differences are small on the original dataframe, but more than double on the parquet-based dataframe.</p>
  
  <p>The problem is exaggerated on large datasets (~20-30GB) where I'm seeing <code>mean</code> computations go from seconds to multiple minutes.</p>
  
  <p>Is this something inherent to parquet files that I'm not aware of, or is this a bug of some sort?</p>