title: Pandas dataframe rolling correlation by date intervals
body: <p>I have a dataframe that is about 700 rows by 25 columns, with the index being a date. How would I go about calculating the 10 day rolling correlation for each column against all the other columns? Specifically 10 days, not 10 entries, as the days are non-regular. And then ideally return the correlations in a new dataframe with the same index and a readable column name (i.e. A_B_corr</p>
  <p>Here's a sample of my data:</p>
  <pre><code>               A     B     C     D     E
  date                                    
  2021-11-25  0.47 -0.97  0.64 -0.50 -0.55
  2021-11-26  0.99 -0.97  0.27 -0.53 -0.78
  2021-11-29  1.00 -0.15  0.06 -0.47 -0.73
  2021-11-30  0.77  0.07 -0.30 -0.48  0.33
  2021-12-01  0.99 -0.55 -0.82 -1.00  0.51
  2021-12-02  0.72 -0.98 -0.06 -0.63 -0.15
  2021-12-03  0.75 -0.53  0.58  0.09  0.57
  2021-12-06  0.99 -0.93  0.84 -0.30  0.72
  2021-12-07  0.94  0.65  0.48  0.96  0.25
  2021-12-08  0.75  0.87  0.76  1.00  0.92
  2021-12-09 -0.99 -0.31  0.76  0.83  0.51
  2021-12-10  0.83  0.16  0.56  0.86 -0.94
  2021-12-13  0.96  0.07  0.58  0.90 -0.89
  2021-12-14  0.84  0.76  1.00  0.21 -0.35
  2021-12-15  0.82  0.48  0.98  0.30 -0.59
  </code></pre>