sklearn pipeline - how to apply different transformations on different columns
<p>I am pretty new to pipelines in sklearn and I am running into this problem: I have a dataset that has a mixture of text and numbers i.e. certain columns have text only and rest have integers (or floating point numbers). </p>

<p>I was wondering if it was possible to build a pipeline where I can for example call <code>LabelEncoder()</code> on the text features and <code>MinMaxScaler()</code> on the numbers columns. The examples I have seen on the web mostly point towards using <code>LabelEncoder()</code> on the entire dataset and not on select columns. Is this possible? If so any pointers would be greatly appreciated.</p>