QST: Rolling pd.Grouper over N days
Starting from a desired date in a dataframe, can we group and sum the last 30 days and compare the prior 30 day period? And it would have to be days since this calculation could be done at any point in a month.

The first option below isn't very elegant, but I think gets to the right place. The second option using pd.Grouper, but chops off values during the group and produces different results and groups to the beginning of the period.

Any suggestions? Is there another function to try? Thanks!

```python
data = pd.DataFrame({
    'Timestamp': pd.date_range('8-11-2020', '10-12-20', freq='D'),
    'Qty': [4, 8, 5, 0, 6, 1, 0, 5, 4, 8, 4, 4, 8, 2, 6, 5, 3, 2, 4, 8, 0, 4,
       0, 9, 5, 3, 8, 2, 2, 4, 5, 7, 6, 7, 6, 5, 1, 6, 0, 1, 0, 5, 0, 2,
       3, 6, 5, 2, 5, 8, 3, 7, 8, 6, 4, 5, 8, 7, 7, 5, 9, 6, 6]
})
df = data.groupby(pd.Grouper(key='Timestamp', freq='D')).sum()[['Qty']].sort_index()
df = df.rolling('30D').sum().pct_change(1, freq='30D')
df.loc['10-12-2020']['Qty']

# or...

data \
    .groupby(pd.Grouper(key='Timestamp', freq='30D', label='right', closed='right')) \
    .sum()[['Qty']].sort_index().pct_change()
```
Using grouper begins the grouping of the Timestamp column from the earliest date to the latest. I would want to do the opposite.