title: Concatenate two dataframes and remove duplicate rows based on column value
body: <p>I have two dataframes. </p>
  
  <p><code>df1</code>:</p>
  
  <pre><code>    Name Symbol         ID
  0    Jay    N/A    372Y105
  1    Ray    N/A    4446100
  2   Faye    N/A    484MAA4
  3   Maye    N/A    504W308
  4    Kay    N/A    782L107
  5   Trey    FFF    782L111
  </code></pre>
  
  <p><code>df2</code>:</p>
  
  <pre><code>    Name Symbol         ID
  0    Jay    AAA    372Y105
  1   Faye    CCC    484MAA4
  2    Kay    EEE    782L107
  </code></pre>
  
  <p>If <code>ID</code> matches between <code>df1</code> and <code>df2</code>, I want to replace the <code>symbol</code> in <code>df1</code> with the <code>symbol</code> from <code>df2</code>, so that the output looks like:</p>
  
  <pre><code>    Name Symbol         ID
  0    Jay    AAA    372Y105
  1    Ray    N/A    4446100
  2   Faye    CCC    484MAA4
  3   Maye    N/A    504W308
  4    Kay    EEE    782L107
  5   Trey    FFF    782L111
  </code></pre>
  
  <p>It sounds like I should first concatenate the two dataframes, and then drop duplicates somehow, e.g., </p>
  
  <pre><code>df3 = pd.concat([df1, df2])
  df3 = df3.drop_duplicates(subset='ID', keep='last')
  </code></pre>
  
  <p>But rather than only keep the first or last duplicate, I only want to remove the ones where <code>symbol</code> = <code>N/A</code>. </p>