title: OneHotEncoder categorical_features deprecated, how to transform specific column
body: <p>I need to transform the independent field from string to arithmetical notation. I am using OneHotEncoder for the transformation. My dataset has many independent columns of which some are as:</p>
  
  <pre><code>Country     |    Age       
  --------------------------
  Germany     |    23
  Spain       |    25
  Germany     |    24
  Italy       |    30 
  </code></pre>
  
  <p>I have to encode the Country column like</p>
  
  <pre><code>0     |    1     |     2     |       3
  --------------------------------------
  1     |    0     |     0     |      23
  0     |    1     |     0     |      25
  1     |    0     |     0     |      24 
  0     |    0     |     1     |      30
  </code></pre>
  
  <p>I succeed to get the desire transformation via using OneHotEncoder as</p>
  
  <pre><code>#Encoding the categorical data
  from sklearn.preprocessing import LabelEncoder
  
  labelencoder_X = LabelEncoder()
  X[:,0] = labelencoder_X.fit_transform(X[:,0])
  
  #we are dummy encoding as the machine learning algorithms will be
  #confused with the values like Spain &gt; Germany &gt; France
  from sklearn.preprocessing import OneHotEncoder
  
  onehotencoder = OneHotEncoder(categorical_features=[0])
  X = onehotencoder.fit_transform(X).toarray()
  </code></pre>
  
  <p>Now I'm getting the depreciation message to use <code>categories='auto'</code>. If I do so the transformation is being done for the all independent columns like country, age, salary etc.</p>
  
  <p><strong>How to achieve the transformation on the dataset 0th column only?</strong></p>