group values in pandas and sum after all dates - pandas

Question

I have a pandas dataframe like this:
date id flow type
2020-04-26 1 3 A
2020-04-27 2 4 A
2020-04-28 1 2 A
2020-04-26 1 -3 B
2020-04-27 1 4 B
2020-04-28 2 3 B
2020-04-26 3 0 C
2020-04-27 2 5 C
i also have a dictionary like this of 'trailing_date' keys.
{'T-1': Timestamp('2020-04-27')
'T-2' : Timestamp('2020-04-26')}
I would like to sum the flows for each id and group by they keys in my dictionary where
the sum of flows is inclusive of this trailing dates minus the flows of most recent date. In other words. i would like to have this:
type T-1 T-2
A 4 7
B 4 1
Why did i get 4 for T-1 at A? its because if today is 28th, then T-1 is 27th, hence answer is 4. Likewise at T-2, its 3+4 = 7 etc.
I tried:
df2 = df.groupby(["type","date"])['flow'].sum().unstack("type")
Im somewhat stuck what to do after this. Thanks

Answer 1

Tough problem. There might be a more elegant way to do this, but here is what I came up with.
import pandas as pd
dates1 = pd.Series(range(3), index=pd.date_range('2020-04-26', freq='D', periods=3)).index
dates2 = dates1.copy()
dates3 = dates1.copy()[0:-1]
dates = dates1.append([dates2, dates3])
types = ['A']*3 + ['B']*3 + ['C']*2
df = pd.DataFrame({'date': dates, 'id':[1,2,1,1,1,2,3,2],
'flow': [3,4,2,-3,4,3,0,5], 'type': types})
dates_dict = {'T-1': pd.Timestamp('2020-04-27'), 'T-2': pd.Timestamp('2020-04-26')}
grouped_df = df.groupby(["type","date"])['flow'].sum()
new_dict = {}
for key in dates_dict:
sums_list = []
# loops through the unique levels of the grouped_df: 'A', 'B', 'C'
types = grouped_df.index.get_level_values(0).unique()
new_dict.update({'type': types})
for letter in types:
# sums up the flows by dates
# coming before the timestamp label corresponding to the key
# but leaves out the most recent date
sums_list.append(grouped_df[letter][grouped_df[letter].index >= dates_dict[key]].iloc[:-1].sum())
new_dict.update({key: sums_list})
final_df = pd.DataFrame(new_dict)
Output:
>>> final_df
type T-1 T-2
0 A 4 7
1 B 4 1
2 C 0 0