title: Python pandas map multilevel column heading df to another df
body: <p>what I am trying to do is use the map function to tie back a pivot table to another dataframe. A1:A4 are columns that denote a different location. So the value in the pivot table represents the # of instances at A1 or A3, etc.</p>
  
  <p>I have one dataframe and one pivot table. The dataframe (df1) looks like this:</p>
  
  <pre><code>    SubscriberKey Inst    A1   A2   A3   A4
          'abc'      2      0    0     0    0
          'bcd'      4      0    0     0    0
          'cde'      1      0    0     0    0
          'def'      0      0    0     0    0
          'efg'      0      0    0     0    0
  </code></pre>
  
  <p>My pivot table (pt1) looks like this. It also has a multilevel column heading due to this being a pivot table. To clarify what I mean by multilevel column heading is that a single column heading outputs ('Instance', 'A1'). Anyways below is a snapshot of my pivot table (pt1):</p>
  
  <pre><code>     Subscriber Key Instance Instance Instance Instance
         Linkname       A1       A2       A3       A4
          'abc'          2         0       2        0
          'bcd'          4         1       1        2
          'cde'          1         1       0        0
  </code></pre>
  
  <p>I would like to somehow populate my df's A1:A4 columns based on what is in the pivot. The df has more subscriber keys than the pivot table contains and so the row counts are not the same.</p>
  
  <p>The output would look like:</p>
  
  <pre><code>    SubscriberKey  Inst   A1  A2   A3  A4
          'abc'       2     0    0    2   0
          'bcd'       4     0    1    1   2
          'cde'       1     1    0    0   0
          'def'       0     0    0    0   0
          'efg'       0     0    0    0   0
  </code></pre>
  
  <p>Any help is appreciated, thanks! I have already tried df1.update(pt1[column]) and it returns 'Type Error: expected tuple, got str'</p>