How to normalize only certain columns in scikit-learn?
<p>I have data similar to the following:</p>

<pre><code>[
   [0, 4, 15]
   [0, 3, 7]
   [1, 5, 9]
   [2, 4, 15]
]
</code></pre>

<p>I used <code>oneHotEncoder</code> <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder.fit_transform" rel="noreferrer">http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder.fit_transform</a> to preprocess this data so it is suitable for linear regression to give me this:</p>

<pre><code>[
   [1, 0, 0, 4, 15]
   [1, 0, 0, 3, 7]
   [0, 1, 0, 5, 9]
   [0, 0, 1, 4, 15]
]
</code></pre>

<p>However, I then wish to normalise this data.</p>

<p>So far I am just normalising the data like so:</p>

<pre><code>preprocessing.normalize(data)
</code></pre>

<p>However, this normalises all the columns including category ones. </p>

<p>My questions are the following:</p>

<ul>
<li>How do I normalise only certain columns?</li>
<li>Is it desirable to normalise category data, or should I avoid it?</li>
</ul>

<p>Thank you!</p>