Compare Cumulative Sales per Year-End - pandas

Question

Using this sample dataframe:
np.random.seed(1111)
df = pd.DataFrame({
'Category':np.random.choice( ['Group A','Group B','Group C','Group D'], 10000),
'Sub-Category':np.random.choice( ['X','Y','Z'], 10000),
'Sub-Category-2':np.random.choice( ['G','F','I'], 10000),
'Product':np.random.choice( ['Product 1','Product 2','Product 3'], 10000),
'Units_Sold':np.random.randint(1,100, size=(10000)),
'Dollars_Sold':np.random.randint(100,1000, size=10000),
'Customer':np.random.choice(pd.util.testing.rands_array(10,25,dtype='str'),10000),
'Date':np.random.choice( pd.date_range('1/1/2016','12/31/2020',
freq='M'), 10000)})
I am trying to compare 12 month time frames with seaborn plots for a sub-grouping of category. For example, I'd like to compare the cumulative 12 months for each year ending 4-30 vs. the same time period for each year. I cannot wrap my head around how to get a running total of data for each respective year (5/1/17-4/30/18, 5/1/18-4/30/19, 5/1/19-4/30/20). The dates are just examples - I'd like to be able to compare different year-end data points, even better would be able to compare 365 days. For instance, I'd love to compare 3/15/19-3/14/20 to 3/15/18-3/14/19, etc.
I envision a graph for each 'Category' (A,B,C,D) with lines for each respective year representing the running total starting with zero on May 1, building through April 30 of the next year. The x axis would be the month (starting with May 1) & y axis would be 'Units_Sold' as it grows.
Any help would be greatly appreciated!

Answer 1

One way to convert the date to fiscal quarters and extract the fiscal year:
df = pd.DataFrame({'Date':pd.date_range('2019-01-01', '2019-12-31', freq='M'),
'Values':np.arange(12)})
df['fiscal_year'] = df.Date.dt.to_period('Q-APR').dt.qyear
Output:
Date Values fiscal_year
0 2019-01-31 0 2019
1 2019-02-28 1 2019
2 2019-03-31 2 2019
3 2019-04-30 3 2019
4 2019-05-31 4 2020
5 2019-06-30 5 2020
6 2019-07-31 6 2020
7 2019-08-31 7 2020
8 2019-09-30 8 2020
9 2019-10-31 9 2020
10 2019-11-30 10 2020
11 2019-12-31 11 2020
And now you can group by fiscal_year to your heart's content.