title: Parse an incorrectly delimited text file using pandas
body: <p>I would like to parse the file listed below into a pandas dataframe with year and month as a datetime index and the remaining 11 columns into dataframe columns.</p>
  
  <pre><code> STANDARDIZED NORTHERN HEMISPHERE TELECONNECTION INDICES (1981-2010 Clim)
  
  column 1: Year (yy)
  column 2: Month (mm)
  column 3: North Atlantic Oscillation (NAO)
  column 4: East Atlantic Pattern (EA)
  column 5: West Pacific Pattern (WP)
  column 6: EastPacific/ North Pacific Pattern (EP/NP)
  column 7: Pacific/ North American Pattern (PNA)
  column 8: East Atlantic/West Russia Pattern (EA/WR)
  column 9: Scandinavia Pattern (SCA)
  column 10: Tropical/ Northern Hemisphere Pattern (TNH)
  column 11: Polar/ Eurasia Pattern (POL)
  column 12: Pacific Transition Pattern (PT)
  column 13: Explained Variance (%) of leading 10 modes
  PATTERN VALUES ARE SET TO -99.9 FOR MONTHS IN WHICH THE PATTERN IS NOT A LEADING MODE
  
  yyyy mm   NAO   EA    WP   EP/NP  PNA  EA/WR  SCA   TNH   POL  PT    Expl. Var.
  
  1950  1   0.56 -2.71 -1.69  0.91 -3.65  2.29  0.78  0.55 -0.71-99.90   86.0
  1950  2   0.01  0.66 -1.36 -1.13 -1.69 -0.57 -0.94 -1.07  1.25-99.90   58.6
  1950  3  -0.78  0.82 -0.38 -0.02 -0.06 -1.80 -0.22-99.90  0.78-99.90   54.3
  1950  4   0.65  0.28 -0.50 -1.87 -0.23 -2.50  0.46-99.90  0.10-99.90   64.8
  1950  5  -0.50 -0.51  0.23 -0.98 -0.40  1.41  0.28-99.90  0.55-99.90   49.6
  </code></pre>
  
  <p>I have read in the file, filename using pandas command below:</p>
  
  <pre><code>df = pd.read_csv(filename, delim_whitespace=True, index_col=[0], parse_dates=[[0, 1]], skiprows=17)
  </code></pre>
  
  <p>A snippet of the output:</p>
  
  <pre><code>             NAO    EA           WP  EP/NP   PNA  EA/WR          SCA  \
  yyyy_mm                                                                
  1950-01-01  0.56 -2.71        -1.69   0.91 -3.65   2.29         0.78   
  1950-02-01  0.01  0.66        -1.36  -1.13 -1.69  -0.57        -0.94   
  1950-03-01 -0.78  0.82        -0.38  -0.02 -0.06  -1.80  -0.22-99.90   
  1950-04-01  0.65  0.28        -0.50  -1.87 -0.23  -2.50   0.46-99.90   
  1950-05-01 -0.50 -0.51         0.23  -0.98 -0.40   1.41   0.28-99.90 
  </code></pre>
  
  <p>While I am able to parse most of the data correctly, the <code>-99.90</code> data values don't seem to be delimited from the previous value and thus get lumped into the previous columns. I assume these values are flagged anyhow, so I would be happy to omit them from the resulting dataframe. </p>
  
  <p>I have used <code>na_values</code> kwarg, but that doesn't have an effect.</p>
  
  <p>If there is a built in solution to this problem, or would I need to write a custom text parser in front of the pandas parsing? If a custom parser is needed, what is the most direct way to eliminate/replace the <code>-99.90</code> values prior to pandas parsing so that the resulting data frame is parsed correctly?</p>