title: Copy pandas DataFrame row to multiple other rows
body: <p>Simple and practical question, yet I can't find a solution.</p>
  
  <p>The questions I took a look were the following:</p>
  
  <p><a href="https://stackoverflow.com/questions/12307099/modifying-a-subset-of-rows-in-a-pandas-dataframe">Modifying a subset of rows in a pandas dataframe</a></p>
  
  <p><a href="https://stackoverflow.com/questions/19867734/changing-certain-values-in-multiple-columns-of-a-pandas-dataframe-at-once">Changing certain values in multiple columns of a pandas DataFrame at once</a></p>
  
  <p><a href="https://stackoverflow.com/questions/21295329/fastest-way-to-copy-columns-from-one-dataframe-to-another-using-pandas">Fastest way to copy columns from one DataFrame to another using pandas?</a></p>
  
  <p><a href="https://stackoverflow.com/questions/15315452/selecting-with-complex-criteria-from-pandas-dataframe">Selecting with complex criteria from pandas.DataFrame</a></p>
  
  <p>The key difference between those and mine is that I need not to insert a single value, but a row.</p>
  
  <p>My problem is, I pick up a row of a dataframe, say <code>df1</code>. Thus I have a series.</p>
  
  <p>Now I have this other dataframe, <code>df2</code>, that I have selected multiple rows according to a criteria, and I want to replicate that series to all those row. </p>
  
  <p>df1:</p>
  
  <pre><code>Index/Col   A   B  C
  1           0   0  0
  2           0   0  0
  3           1   2  3
  4           0   0  0
  </code></pre>
  
  <p>df2:</p>
  
  <pre><code>Index/Col   A   B  C
  1           0   0  0
  2           0   0  0
  3           0   0  0
  4           0   0  0
  </code></pre>
  
  <p>What I want to accomplish is inserting df1[3] into the lines df2[2] and df3[3] for example. So something like the non working code:</p>
  
  <pre><code>series = df1[3]
  df2[df2.index&gt;=2 and df2.index&lt;=3] = series
  </code></pre>
  
  <p>returning</p>
  
  <p>df2:</p>
  
  <pre><code>Index/Col   A   B  C
  1           0   0  0
  2           1   2  3
  3           1   2  3
  4           0   0  0
  </code></pre>