title: data structure change after using iterrows
body: <p>I am attempting to select a row of data, merge it with another dataframe, then select the next row, performing the same operation. I believe I should be able to do this with pandas.iterrows.</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  
  one = {'A' : pd.Series([3, 4, 2]), 'B' : pd.Series(['one', 'two', 'one'])}
  two = {'A' : pd.Series([1, 2, 3, 4, 5, 6])}
  
  df1 = pd.DataFrame(one, columns = ['A', 'B'])
  df2 = pd.DataFrame(two)
  
  for index, row in df1.iterrows():
      row1 = pd.DataFrame(row)
      print(row1)
  
      df3 = pd.merge(df2, row1, on = 'A', how = 'left')
      print (df3)
  </code></pre>
  
  <p>When I print(row1), I get:</p>
  
  <pre><code>     0
  A    3
  B  one
       1
  A    4
  B  two
       2
  A    2
  B  one
  </code></pre>
  
  <p>The join fails due to a key error, which makes sense to me given the structure of print(row1).</p>
  
  <p>The desired outcome of df3 is:</p>
  
  <pre><code>    A   B
  0   1  Nan
  1   2  Nan
  2   3  one
  3   4  Nan
  4   5  Nan
  5   6  Nan
  
      A   B
  0   1  Nan
  1   2  Nan
  2   3  Nan
  3   4  two
  4   5  Nan
  5   6  Nan
  </code></pre>
  
  <p>It appears to me that the column labels are now the index. I think I need to reset the index, so that 'A' and 'B' will be values that I can join on. Is there an efficient way to accomplish this? </p>