title: Drop row in Pandas Series and clean up index
body: <p>I have a Pandas Series and based on a random number I want to pick a row (5 in the code example below) and drop that row. When the row is dropped I want to create a new index for the remaining rows (0 to 8). The code below:</p>
  
  <pre><code>print 'Original series: ', sample_mean_series
  print 'Length of original series', len(sample_mean_series)
  sample_mean_series = sample_mean_series.drop([5],axis=0)
  print 'Series with item 5 dropped: ', sample_mean_series
  print 'Length of modified series:', len(sample_mean_series)
  print sample_mean_series.reindex(range(len(sample_mean_series)))
  </code></pre>
  
  <p>And this is the output:</p>
  
  <pre><code>Original series:  
  0    0.000074
  1   -0.000067
  2    0.000076
  3   -0.000017
  4   -0.000038
  5   -0.000051
  6    0.000125
  7   -0.000108
  8   -0.000009
  9   -0.000052
  Length of original series 10
  Series with item 5 dropped:  
  0    0.000074
  1   -0.000067
  2    0.000076
  3   -0.000017
  4   -0.000038
  6    0.000125
  7   -0.000108
  8   -0.000009
  9   -0.000052
  Length of modified series: 9
  0    0.000074
  1   -0.000067
  2    0.000076
  3   -0.000017
  4   -0.000038
  5         NaN
  6    0.000125
  7   -0.000108
  8   -0.000009
  </code></pre>
  
  <p>My problem is that the row number 8 is dropped. I want to drop row "5    NaN" and keep -0.000052 with an index 0 to 8. This is what I want it to look like:</p>
  
  <pre><code>0    0.000074
  1   -0.000067
  2    0.000076
  3   -0.000017
  4   -0.000038
  5    0.000125
  6   -0.000108
  7   -0.000009
  8   -0.000052
  </code></pre>