title: Pandas pivot table with very large number of columns
body: <p>I have a pandas dataframe <code>df</code> with about 1000 rows but 500 columns. The columns are named Run1, Run2, ..., Run500</p>
  
  <p>The existing index is <code>datetime</code>.</p>
  
  <p>Sample data from dataframe is as follows:</p>
  
  <pre><code>df.ix[1:4,1:4]
                         Run1    Run2    Date
  2019-04-01 01:00:00  23.0263  23.0263  2019-04-01
  2019-04-01 01:00:00  19.2212  19.2212  2019-04-01
  2019-04-02 01:00:00  19.3694  19.3694  2019-04-02
  2019-04-02 01:00:00  19.3694  19.3694  2019-04-02
  </code></pre>
  
  <p>I can do the trying the following:</p>
  
  <pre><code>pd.pivot_table(df, index=['Date'], values=['Run1'], aggfunc=[np.mean])['mean']
  </code></pre>
  
  <p>But I need to the following:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  pd.pivot_table(df, index=['Date'], values=['Run1', 'Run2', ...., 'Run500'], aggfunc=[np.mean])['mean']
  </code></pre>