Python pandas: Best way to normalize data?
<p>I have a large pandas dataframe with about 80 columns. Each of the 80 columns in the dataframe report daily traffic statistics for websites (the columns are the websites).</p>

<p>As I don't want to work with the raw traffic statistics, I rather like to normalize all of my columns (except for the first, which is the date). Either from 0 to 1 or (even better) from 0 to 100.</p>

<pre><code>Date        A      B      ...
10/10/2010  100.0  402.0  ...
11/10/2010  250.0  800.0  ...
12/10/2010  800.0  2000.0 ...
13/10/2010  400.0  1800.0 ...
</code></pre>

<p>That being said, I wonder which normalization to apply. <a href="http://sebastianraschka.com/Articles/2014_about_feature_scaling.html" rel="noreferrer">Min-Max scaling</a> vs. z-Score Normalization (standardization)? Some of my columns have strong outliers. It would be great to have an example. I am sorry not being able to provide the full data.</p>