title: Index - Match using Pandas
body: <p>I have the following 2 data frames:</p>
  
  <pre><code>df1 = pd.DataFrame({
      'dates': ['02-Jan','03-Jan','30-Jan'],
      'currency': ['aud','gbp','eur'],
      'amount': [100,330,500]
  })
  
  df2 = pd.DataFrame({
      'dates': ['01-Jan','02-Jan','03-Jan','30-Jan'],
      'aud': [0.72,0.73,0.74,0.71],
      'gbp': [1.29,1.30,1.4,1.26],
      'eur': [1.15,1.16,1.17,1.18]
  })
  </code></pre>
  
  <p>I want to obtain the intersection of <code>df1.dates</code> &amp; <code>df1.currency</code>. For eg: Looking up the prevalent <code>'aud'</code> exchange rate on '02-Jan'</p>
  
  <p>It can be solved using the <code>Index + Match</code> functionality of excel. What shall be the best way to replicate it in Pandas.</p>
  
  <p>Desired Output: add a new column <code>'price'</code></p>
  
  <pre><code>dates  currency amount price
  02-Jan aud      100    0.73
  03-Jan gbp      330    1.4
  30-Jan eur      500    1.18
  </code></pre>