title: Reshape, recombine row with column
body: <p>I have dataset like this:</p>
  
  <pre><code>Block   Vector
  blk_-1  0.0 2 3, 0.5 3 8, 0.7 33 5
  blk_-2  1.0 4 1, 2.0 2 4
  blk_-3  0.0 0 0, 6.0 0 7
  blk_-4  8.0 3 0, 7.0 5 8
  blk_-5  9.0 0 5, 5.0 0 2, 5.2 3 2, 5.9 5 3
  
  dat = {'Block': ['blk_-1', 'blk_-2', 'blk_-3', 'blk_-4',  'blk_-5'],\
          'Vector': ['0.0 2 3, 0.5 3 8, 0.7 33 5',\
                     '1.0 4 1, 2.0 2 4',\
                     '0.0 0 0, 6.0 0 7',\
                     '8.0 3 0, 7.0 5 8',\
                    '9.0 0 5, 5.0 0 2, 5.2 3 2, 5.9 5 3']
         }
  </code></pre>
  
  <p>I want to get:</p>
  
  <pre><code>Block   Vector
  
  blk_-1   0.0 2 3
  
  blk_-1   0.5 3 8
  
  blk_-1   0.7 33 5
  
  blk_-2   1.0 4 1
  
  blk_-2   2.0 2 4
  
  blk_-3   0.0 0 0
  
  blk_-3   6.0 0 7
  
  blk_-4   8.0 3 0
  
  blk_-4   7.0 5 8
  
  blk_-5   9.0 0 5
  
  blk_-5   5.0 0 2
  
  blk_-5   5.2 3 2    
  
  blk_-5   5.9 5 3
  </code></pre>
  
  <p>Try:</p>
  
  <pre><code>df['Vector'] = df['Vector'].apply(lambda x : list(map(str, x.split(','))))
  </code></pre>
  
  <pre><code>df.Vector.apply(pd.Series) \
      .merge(df, left_index = True, right_index = True) \
      .drop(["Vector"], axis = 1)
  </code></pre>
  
  <p>Get:</p>
  
  <pre><code>    0   1   2   3                   Block
  0   0.0 2 3 0.5 3 8 0.7 33 5    NaN blk_-1
  1   1.0 4 1 2.0 2 4 NaN NaN         blk_-2
  2   0.0 0 0 6.0 0 7 NaN NaN         blk_-3
  3   8.0 3 0 7.0 5 8 NaN NaN         blk_-4
  4   9.0 0 5 5.0 0 2 5.2 3 2 5.9 5 3         blk_-5
  </code></pre>
  
  <p>Actually
  Stuck at this moment. Waiting for your ideas and comments :)</p>