title: sklearn.impute.MissingIndicator ValueError: Input contains NaN, infinity or a value too large for dtype('float64').:
body: <p>While creating and testing some examples with sklearn's impute library (v0.0.20) I ran into to the above error. I set up the following simple example and want to create a MissingIndicator feature for the NaN's and 999's.</p>
  
  <p>Everything works fine for the NaN's, but if I try to replace the missing values parameter by 999 I get this error. According to the documentation numbers should be fine.</p>
  
  <p><a href="https://i.stack.imgur.com/VwHrJ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VwHrJ.png" alt="enter image description here"></a></p>
  
  <p>Here's the code and the error:</p>
  
  <pre><code>X = pd.DataFrame(
      np.array([5,7,8,-5,0,25,999,
                1,-1, np.NaN, 0, np.NaN])\
                .reshape((4,3)))
  
  from sklearn.impute import MissingIndicator
  indicator_1 = MissingIndicator(missing_values=np.NaN)
  indicator_1.fit_transform(X)
  indicator_2 = MissingIndicator(missing_values=999)
  indicator_2.fit_transform(X)
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/2CYnX.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/2CYnX.png" alt="enter image description here"></a></p>
  
  <p>I know it's really is easy to build a working missing indicator yourself. But I was wondering if I'm rather using the library wrongly, or if this is some internal problem?</p>
  
  <p>Thanks!</p>