title: How can I reindex a series created through the collapsing of a DataFrame?
body: <p>Consider the code below:</p>
  
  <pre><code>sfix = sub['fix']  # a pandas.Panel
  (sfix.minor_xs('tstop') - sfix.minor_xs('tstart'))  # slicey slicey!
  </code></pre>
  
  <p>output:</p>
  
  <pre><code>&lt;class 'pandas.core.frame.DataFrame'&gt;
  Int64Index: 804 entries, 0 to 803
  Data columns (total 8 columns):
  0    573  non-null values
  1    675  non-null values
  2    804  non-null values
  3    715  non-null values
  4    578  non-null values
  5    568  non-null values
  6    664  non-null values
  7    599  non-null values
  dtypes: float64(8)
  </code></pre>
  
  <p>This output corresponds to the difference between the <code>tstop</code> and <code>tstart</code> columns for each of the 8 DataFrames contained in the Panel object.</p>
  
  <p>These columns all contain an identical kind of data, and I'd like to stack them into a single series, ergo:</p>
  
  <pre><code>s = pd.concat([df[i] for i in df])
  </code></pre>
  
  <p>This is a good start, but now all my indexes are duplicated 8 times:</p>
  
  <pre><code>&gt;&gt;&gt; s.ix[0]
  
  0     98
  0    184
  0    178
  0    188
  0    176
  0    234
  0    128
  0     82
  dtype: float64
  </code></pre>
  
  <p>From here, I can't quite figure out how to reindex my series such that the indexes go from 0 to <code>len(s)</code>.  I have tried the following, to no avail:</p>
  
  <pre><code>s.reindex(copy=True)
  s.reindex(index=xrange(len(s)), copy=True)
  </code></pre>
  
  <p>What am I missing?</p>