title: how to convert the new input non-numerical-data into numerical data
body: <p><strong>OUTLINE:</strong>
  I want to know <strong>how to keep the new input non-numerical-data as the unique one!</strong> </p>
  
  <p><strong>DESCRIBE:</strong>
  Namely, when we finish both train and test period, lot of new data that <strong>unions numerical and non-numerical data</strong> input into my model. </p>
  
  <p><strong>ISSUE(1):</strong>
   Thus, the first important matter is <strong>convert these new mixed data into a dataframe with all numerical format!!</strong></p>
  
  <p><strong>TRIED METHOD:</strong></p>
  
  <p>How can we do that? I used LabelEncoder to transform every non-numerical data into float type, however, the member of SCIKIT-LEARN feedbacked me that LabelEncoder is only available for Label[Y] and I should use OneHotEncoder
  convert feature. 
  [He answered me at URL: <a href="https://github.com/scikit-learn/scikit-learn/issues/8674]" rel="nofollow noreferrer">https://github.com/scikit-learn/scikit-learn/issues/8674]</a>, while misunderstanding my meaning</p>
  
  <p>Unfortunately, OneHotEncoder is only available for integer feature by SCIKIT-LEARN Official documentation said in p.1829.</p>
  
  <p>I know there is a title about 'Feature Union with Heterogeneous Data Sources', while it is not convenient as LabelEncoder does.
  <strong>ISSUE(2):</strong>
  The second reason why such issue confuses me is related to how we can ensure the input new non-numerical data can be transform into an unique value that differs from the previous-transformed-training-data or previous-transformed-test-data. Namely, although we can use LabelEncoder transform new input data into numerical data, there is a risk that the transformed data[in the new input data yield] might be equal to the transformed data[in the previous transformed data]</p>
  
  <p>Thanks for your help in advance</p>