title: More efficient way to do the same merge on multiple columns in a dataframe?
body: <p>Input:</p>
  <p><code>df1</code></p>
  <pre><code>    OFF_P1  OFF_P2  OFF_P3  OFF_P4  OFF_P5  GAME_ID
  0   1629675 1627736 1630162 201976  1629020 22101224
  1   201599  1630178 202699  1629680 201980  22101228
  2   1630191 1630180 1630587 1630240 1628402 22101228
  3   1627759 201143  1628464 1628369 203935  22101223
  4   1630573 1630271 1630238 1628436 1630346 22101223
  </code></pre>
  <p><code>df2</code></p>
  <pre><code>    PLAYER_ID GAME_ID   PTS
  0   201980    21900002  28
  1   201586    21900001  13
  2   1628366   21900001  8
  3   200755    21900001  16
  4   202324    21900001  6
  </code></pre>
  <p>Desired Output:</p>
  <pre><code>    OFF_P1  OFF_P2  OFF_P3  OFF_P4  OFF_P5  GAME_ID  OFF_P1_PTS  OFF_P2_PTS  etc...
  0   1629675 1627736 1630162 201976  1629020 22101224 28          13          ...
  1   201599  1630178 202699  1629680 201980  22101228 12
  2   1630191 1630180 1630587 1630240 1628402 22101228 14
  3   1627759 201143  1628464 1628369 203935  22101223 8
  4   1630573 1630271 1630238 1628436 1630346 22101223 19
  </code></pre>
  <p>I would like to merge the <code>PTS</code> column from <code>df2</code> to <code>df1</code> but for each column of <code>OFF_P1</code>, <code>OFF_P2</code>, etc...</p>
  <p>Is there a more efficient way to do this other than something like the below?</p>
  <pre><code>df1 = df1.merge(df2, left_on=['GAME_ID', 'OFF_P1'], right_on=['GAME_ID', 'PLAYER_ID'])
  df1 = df1.merge(df2, left_on=['GAME_ID', 'OFF_P2'], right_on=['GAME_ID', 'PLAYER_ID'])
  df1 = df1.merge(df2, left_on=['GAME_ID', 'OFF_P3'], right_on=['GAME_ID', 'PLAYER_ID'])
  df1 = df1.merge(df2, left_on=['GAME_ID', 'OFF_P4'], right_on=['GAME_ID', 'PLAYER_ID'])
  df1 = df1.merge(df2, left_on=['GAME_ID', 'OFF_P5'], right_on=['GAME_ID', 'PLAYER_ID'])
  </code></pre>