Pandas DateTimeSlicing for specific months per year

Question

I was reading a lot of stuff about pandas and date time slicing but I haven't found a solution for my problem yet. I hope you could give me some good advices! I have a data frame with a Datetimeindex and for example a single column with floats. The time series is about 60 years. For example:

idx = pd.Series(pd.date_range("2016-11-1", freq="M", periods=48))    
dft = pd.DataFrame(np.random.randn(48,1),columns=["NW"], index=idx)

enter image description here

I want to aggregate the column "NW" as sum() per month. I have to solve two problems.

1. The year begins in November and ends in October.
2. I have two periods per 12 months to analyse: a) from November to End of April in the following year and b) from May to End of October in the same year For example: "2019-11-1":"2020-4-30" and "2020-05-01":"2020-10-31"

I think I could write a function but I wonder if there is an easier way with methods from pandas to solve this problems.

Do you have any tips? Best regards Tommi.

Here are some additional informations:
The real datas are daily observations. I want to show a scatter plot for a time series with only the sum() for every month from November-April along the timeline (60 years til now). And the same for the values from May to October.

Answer 1

this is my solution so far. Not the shortest way I think, but it works fine.

d_precipitation_winter = {}
 #for each year without the current year
 for year in dft.index.year.unique()[:-1]:
 #Definition start and end date to mark winter months
 start_date = date(year,11,1)
 end_date = date(year+1,4,30)
 dft_WH = dft.loc[start_date:end_date,:].sum()
 d_precipitation_winter[year]=dft_WH
df_precipitation_winter = pd.DataFrame(data=d_precipitation_winter)