[Python] Pandas pct_change but loop back to start

I'm looking at how to use the pandas pct_change() function, but I need the values 'wrap around', so the last and first values create a percent change value in position 0 rather than NaN.

For example:

df = pd.DataFrame({'Month':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
'Value':[1, 0.9, 0.8, 0.75, 0.75, 0.8, 0.7, 0.65, 0.7, 0.8, 0.85, 0.9]})

Month Value
0 1 1.00
1 2 0.90
2 3 0.80
3 4 0.75
4 5 0.75
5 6 0.80
6 7 0.70
7 8 0.65
8 9 0.70
9 10 0.80
10 11 0.85
11 12 0.90


Using pct_change() + 1 gives:

df['percent change'] = df['Value'].pct_change() + 1
Month Value percent change
0 1 1.00 NaN
1 2 0.90 0.900000
2 3 0.80 0.888889
3 4 0.75 0.937500
4 5 0.75 1.000000
5 6 0.80 1.066667
6 7 0.70 0.875000
7 8 0.65 0.928571
8 9 0.70 1.076923
9 10 0.80 1.142857
10 11 0.85 1.062500
11 12 0.90 1.058824


However I also need to know the % change between December (moth=12) and January (month=1), so the NaN should be 1.111111. I hope to eventually do this to several groups within a group by, so muddling about filling in the Nan with one value over the other, or manually calculating all the percentages seems a long winded way to do it. Is there a simpler way to achieve this?

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