title: Drop specific multiIndex columns in a pandas dataframe
body: <p>Suppose one has a dataframe created as such: </p>
  
  <pre><code>tdata = {('A', 50): [1, 2, 3, 4],
           ('A', 55): [5, 6, 7, 8],
           ('B', 10): [10, 20, 30, 40],
           ('B', 20): [50, 60, 70, 80],
           ('B', 50): [2, 4, 6, 8],
           ('B', 55): [10, 12, 14, 16]}
  tdf = pd.DataFrame(tdata, index=range(0,4))
  
        A      B
       50 55  10  20 50  55
     0  1  5  10  50  2  10
     1  2  6  20  60  4  12
     2  3  7  30  70  6  14
     3  4  8  40  80  8  16
  </code></pre>
  
  <ol>
  <li>How would one drop specific columns, say ('B', 10) and ('B', 20) from the dataframe? </li>
  <li>Is there a way to drop the columns in one command such as <code>tdf.drop(['B', [10,20]])</code>?  Note, I know that my example of the command is by no means close to what it should be, but I hope that it gets the gist across.</li>
  <li>Is there a way to drop the columns through some logical expression? For example, say I want to drop columns having the sublevel indices less than 50 (again, the 10, 20 columns).  Can I do some general command that would encompass column 'A', even though the 10,20 sublevel indices don't exist or must I specifically reference column 'B'?</li>
  </ol>