title: How do I combine different kind of missing_values in sklearn's SimpleImputer into one
body: <p>I have data with two different kind of missing values (np.nan and None) and I am trying to impute them using SimpleImputer.
  Though I am able to do this in two steps I was wondering if there is a way to combine it into one.
  My code is below:</p>
  
  <pre class="lang-py prettyprint-override"><code>import pandas as pd
  import numpy as np
  from sklearn.impute import SimpleImputer
  
  train = pd.DataFrame({
          'users':[None,'John Johnson',np.nan,'John Smith','Mary Williams','ted bundy'],
  })
  test = pd.DataFrame({
          'users':[None,np.nan,'John Smith','Mary Williams','Andy Rollins'],
  })
  
  si1 = SimpleImputer(strategy='constant',fill_value='NAN')
  si2 = SimpleImputer(strategy='constant',missing_values = None, fill_value='MISSING')
  train_imputed_interim1 = si1.fit_transform(train)
  train_imputed = si2.fit_transform(train_imputed_interim1)
  test_imputed_interim1 = si1.fit_transform(test)
  test_imputed = si2.fit_transform(test_imputed_interim1)
  print('\ntrain_imputed:')
  print(train_imputed)
  print('\ntest_imputed:')
  print(test_imputed)
  </code></pre>
  
  <p>Is there a way to combine si1 and si2 into one. I tried </p>
  
  <pre class="lang-py prettyprint-override"><code>si = SimpleImputer(strategy='constant',missing_values = [None,np.nan], fill_value='MISSING')
  </code></pre>
  
  <p>but this does not seem to work.</p>