title: How to deal with NaN and None in Multi-label binarization
body: <p>I am doing a multi-label classification project with scikitlearn. What I am going to do is to binarize the target feature, however, I have some difficulties during the data transform.</p>
  
  <p>Here is the raw data:</p>
  
  <pre><code>107              RA37|RA41|RM153 |RWT037
  108    DA35|DA47|DWT030|DA35|DA47|DWT030
  109                                  NaN
  110                        PI001 |PI040 
  111                        PI001 |PI040 
  112                     RA37|RA41|RWT037
  113    DA35|DA47|DWT030|DA35|DA47|DWT030
  114                                  NaN
  Name: exclusions, dtype: object
  </code></pre>
  
  <p>Then I split it up to more columns with <code>str.split('|',expand=True)</code> and I got the following output：</p>
  
  <pre><code>        0   1   2   3   4   5   6   7   8   9   ... 18  19  20  21  22  23  24  25  26  27
  107 RA37    RA41    RM153   RWT037  None    None    None    None    None    None    ... None    None    None    None    None    None    None    None    None    None
  108 DA35    DA47    DWT030  DA35    DA47    DWT030  None    None    None    None    ... None    None    None    None    None    None    None    None    None    None
  109 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
  110 PI001   PI040   None    None    None    None    None    None    None    None    ... None    None    None    None    None    None    None    None    None    None
  111 PI001   PI040   None    None    None    None    None    None    None    None    ... None    None    None    None    None    None    None    None    None    None
  112 RA37    RA41    RWT037  None    None    None    None    None    None    None    ... None    None    None    None    None    None    None    None    None    None
  113 DA35    DA47    DWT030  DA35    DA47    DWT030  None    None    None    None    ... None    None    None    None    None    None    None    None    None    None
  114 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
  </code></pre>
  
  <p>As you can see, Since there are tons of NaN before processed, the result is mixed with NaN and None. That means I cannot directly use multilaberbinarizer to deal with all these different data types. How do it fix this problem, thanks in advance!</p>