title: Performance reading large SPSS file in pandas dataframe on Windows 7 (x64)
body: <p>I have a large SPSS-file (containing a little over 1 million records, with a little under 150 columns) that I want to convert to a Pandas DataFrame.</p>
  
  <p>It takes a few minutes to convert the file to a list, than another couple of minutes to convert it to a dataframe, than another few minutes to set the columnheaders.</p>
  
  <p>Are there any optimizations possible, that I'm missing?</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  import savReaderWriter as spss
  
  raw_data = spss.SavReader('largefile.sav', returnHeader = True) # This is fast
  raw_data_list = list(raw_data) # this is slow
  data = pd.DataFrame(raw_data_list) # this is slow
  data = data.rename(columns=data.loc[0]).iloc[1:] # setting columnheaders, this is slow too.
  </code></pre>