title: Pandas read scientific notation and change
body: <p>I have a dataframe in pandas that i'm reading in from a csv. </p>
  
  <p>One of my columns has values that include <code>NaN</code>, <code>floats</code>, and scientific notation, i.e. <code>5.3e-23</code> </p>
  
  <p>My trouble is that as I read in the csv, pandas views these data as an <code>object dtype</code>, not the <code>float32</code> that it should be. I guess because it thinks the scientific notation entries are strings. </p>
  
  <p>I've tried to convert the dtype using <code>df['speed'].astype(float)</code> after it's been read in, and tried to specify the dtype as it's being read in using <code>df = pd.read_csv('path/test.csv', dtype={'speed': np.float64}, na_values=['n/a'])</code>. This throws the error <code>ValueError: cannot safely convert passed user dtype of &lt;f4 for object dtyped data in column ...</code></p>
  
  <p>So far neither of these methods have worked. Am I missing something that is an incredibly easy fix?</p>
  
  <p><a href="https://stackoverflow.com/questions/30190459/how-to-gracefully-fallback-to-nan-value-while-reading-integers-from-a-csv-with">this question</a> seems to suggest I can specify known numbers that might throw an error, but i'd prefer to convert the scientific notation back to a float if possible.</p>
  
  <p>EDITED TO SHOW DATA FROM CSV AS REQUESTED IN COMMENTS</p>
  
  <pre><code>7425616,12375,28,2015-08-09 11:07:56,0,-8.18644,118.21463,2,0,2
  7425615,12375,28,2015-08-09 11:04:15,0,-8.18644,118.21463,2,NaN,2
  7425617,12375,28,2015-08-09 11:09:38,0,-8.18644,118.2145,2,0.14,2
  7425592,12375,28,2015-08-09 10:36:34,0,-8.18663,118.2157,2,0.05,2
  65999,1021,29,2015-01-30 21:43:26,0,-8.36728,118.29235,1,0.206836151554794,2
  204958,1160,30,2015-02-03 17:53:37,2,-8.36247,118.28664,1,9.49242000872744e-05,7
  384739,,32,2015-01-14 16:07:02,1,-8.36778,118.29206,2,Infinity,4
  275929,1160,30,2015-02-17 03:13:51,1,-8.36248,118.28656,1,113.318511172611,5
  </code></pre>