在pandas python中分组3个月的聚合和转换期

Question

I have a dataframe with many regions and their respective units sold, visits performed and average visit times on a monthly basis. Not all the regions have the same starting date.

So my table looks something like this:

Region    Month       Visits  Average_minutes  Units_sold
Region_1  2018.01.01  12      2.22             120
Region_1  2018.02.01  10      2.02             108
Region_2  2017.04.01  4       1.8              60
Region_2  2017.05.01  4       1.6              56
Region_2  2017.06.01  3       1.5              58
Region_1  2018.03.01  11      2.1              103
Region_3  2018.04.01  3       2.22             20
Region_3  2018.05.01  2       2                22
Region_2  2017.07.01  6       1.7              61
Region_1  2018.04.01  14      2.1              125
Region_3  2018.06.01  3       2.3              21
Region_3  2018.07.01  3       2.4              19
Region_1  2018.05.01  10      2.12             116
Region_2  2017.08.01  3       2.1              55

What I would like to have is aggregate the monthly data for the different regions in 3 months frequencies by shifting one month forward.

So if we take Region_1 for example, the end result I would like to get is something like this:

Region    Date        Visits  Average_minutes  Units_sold  3M_shift
Region_1  2018.01.01  33      2.11             331         0
Region_1  2018.04.01  24      2.11             241         0
Region_1  2018.02.01  35      2.07             336         1
Region_1  2018.05.01  10      2.12             116         1
Region_1  2018.02.01  35      2.07             336         2
Region_1  2018.05.01  10      2.12             116         2

As you can see the Date now contains the starting date of the 3 month frequency and in the 3M_shift column I see the shifts made compared to the first available month.

Of course in the table above you can see Region_1 only but i would like to get this result for all the groups.

So I would like to have data per groups aggregated not only business year quarters but on 3 month frequency shifting by one month forwards for every iteration till I get to the last month.

My code looks like this, but this groups the months from the starting date of each region and I don't really know how to shift the starting month by one and iterate till the last month:

grp = joined.groupby(['Region', pd.Grouper(key="Date", freq='3M')]).agg({"Visits":"sum", "Average_minutes":"mean", "Units_sold":"sum"})

So for Region_1 for example I get this result:

Region  Date          Visits  Average_minutes  Units_sold
Region_1  2018.01.01  33      2.11             331
Region_1  2018.04.01  24      2.11             241

Edit: Added a better visualisation of what I would like to get.

In the picture below you can see what I mean. The green part is what I have so far. I would like to make a loop for the pink part, but I do not know how to do it.

Could you please help me to get the desired outcome?

Thank you very much in advance!

Answer 1

I'm not 100% sure what you are looking for, but the way I interpret, maybe this will help?

First sort Region and Month.

df = df.sort_values(['Region', 'Month'])

The set a multi index.

df = df.set_index(['Region', 'Month'])

Then groupby the region and apply a rolling window for aggregating and shift it back two periods.

df = df.groupby(level='Region').apply(lambda x: x.rolling(window=3).agg({"Visits":"sum", "Average_minutes":"mean", "Units_sold":"sum"}).shift(-2))

The result is:

                     Visits  Average_minutes  Units_sold
Region   Month                                          
Region_1 2018.01.01    33.0         2.113333       331.0
         2018.02.01    35.0         2.073333       336.0
         2018.03.01    35.0         2.106667       344.0
         2018.04.01     NaN              NaN         NaN
         2018.05.01     NaN              NaN         NaN
Region_2 2017.04.01    11.0         1.633333       174.0
         2017.05.01    13.0         1.600000       175.0
         2017.06.01    12.0         1.766667       174.0
         2017.07.01     NaN              NaN         NaN
         2017.08.01     NaN              NaN         NaN
Region_3 2018.04.01     8.0         2.173333        63.0
         2018.05.01     8.0         2.233333        62.0
         2018.06.01     NaN              NaN         NaN
         2018.07.01     NaN              NaN         NaN