title: pandas dataframe append values to one column based on the values in another dataframe
body: <p>I have two data frame and I want to add one column into first data frame according to values matching with second dataframe.</p>
  
  <p>First df = df1</p>
  
  <pre><code>   OCC_1988 DIST    YEAR    COW EST_LEV
  0   0       100.00  1988    NaN 4
  1   10000   5.83    1988    3.0 4
  2   13002   0.28    1988    3.0 4
  3   13005   0.16    1988    3.0 4
  4   13008   0.06    1988    3.0 4
  5   13011   0.38    1988    3.0 4
  6   13014   0.39    1988    3.0 4
  7   13017   0.16    1988    3.0 4
  8   15017   0.22    1988    3.0 4
  9   15023   1.96    1988    3.0 4
  </code></pre>
  
  <p>10   19005   1.30    1988    3.0 4</p>
  
  <p>Second df = df2</p>
  
  <pre><code>    soccode oescode oes99code
  0   11-1011 19002   11-1011
  1   11-1011 19005   11-1011
  2   11-1021 19002   11-1021
  3   11-1021 19005   11-1021
  4   11-1031 15023   11-1031
  5   11-2011 13011   11-2011
  6   11-2021 13014   11-2021
  7   11-2022 13002   11-2022
  8   11-2031 13005   11-2031
  9   11-3011 13008   11-3011
  10  11-3021 13017   11-3021
  11  11-3031 15017   11-3031
  12  11-3041 10000   11-3040
  </code></pre>
  
  <p>I want to matching with df1['OCC_1988'] to df2['oescode'] and add matched df2['soccode'] values to newly added column 'new_occ_2000'.</p>
  
  <p>So the final df will be like this:</p>
  
  <pre><code>   OCC_1988 DIST    YEAR    COW EST_LEV new_occ_2000
  0   0       100.00  1988    NaN 4       Nan
  1   10000   5.83    1988    3.0 4       11-3041      
  2   13002   0.28    1988    3.0 4       11-2022
  3   13005   0.16    1988    3.0 4       11-2031
  4   13008   0.06    1988    3.0 4       11-3011
  5   13011   0.38    1988    3.0 4       11-2011
  6   13014   0.39    1988    3.0 4       11-2021
  7   13017   0.16    1988    3.0 4       11-3021
  8   15017   0.22    1988    3.0 4       11-3031
  9   15023   1.96    1988    3.0 4       11-1031
  </code></pre>
  
  <p>10   19005   1.30    1988    3.0 4       11-1021</p>
  
  <p>Is there any elegant way to do this?</p>