title: DataFrame correlation produces NaN although its values are all integers
body: <p>I have a dataframe <code>df</code>:</p>
  
  <pre><code>df   = pandas.DataFrame(pd.read_csv(loggerfile, header = 2))
  
  values = df.as_matrix()
  
  df2 = pd.DataFrame.from_records(values, index = datetimeIdx, columns = Columns) 
  </code></pre>
  
  <p>EDIT:</p>
  
  <p>Now reading the data this way as suggested:</p>
  
  <pre><code>df2 = pd.read_csv(loggerfile, header = None, skiprows = [0,1,2])
  </code></pre>
  
  <p>Sample:</p>
  
  <pre><code>                         0              1       2   3   4   5   6   7   8   \
  0  2014-03-19T12:44:32.695Z  1395233072695  703425   0   2   1  13   5  21   
  1  2014-03-19T12:44:32.727Z  1395233072727  703425   0   2   1  13   5  21   
  
     9   10  11   12  13   14  15  16  
  0  25   0  25  209   0  145   0   0  
  1  25   0  25  209   0  146   0   0
  </code></pre>
  
  <p>The columns are all type int (except the first one):</p>
  
  <pre><code>print df2.dtypes
  
  0     object
  1      int64
  2      int64
  3      int64
  4      int64
  5      int64
  6      int64
  7      int64
  8      int64
  9      int64
  10     int64
  11     int64
  12     int64
  13     int64
  14     int64
  15     int64
  16     int64
  </code></pre>
  
  <p>But in my correlation, some columns seem to be NaN. </p>
  
  <pre><code>df2.corr()
  
       1          2    3          4           5   6   7            8           ...    
  1    1.000000   NaN  0.018752   -0.550307   NaN NaN 0.075191     0.775725
  2    NaN        NaN  NaN         NaN        NaN NaN NaN          NaN
  3    0.018752   NaN  1.000000   -0.067293   NaN NaN -0.579651    0.004593 
  ...
  </code></pre>