import datetime as DT
import itertools
start_date=DT.date(2008,5,5)
def mkdate(datestring):
return DT.datetime.strptime(datestring, "%Y-%m-%d").date()
def fortnight(date):
return (date-start_date).days //14
raw = ("2010-08-01",
"2010-06-25",
"2010-07-01",
"2010-07-08")
transactions=[(date,"Some data") for date in map(mkdate,raw)]
transactions.sort(key=lambda (date,data):date)
for key,grp in itertools.groupby(transactions,key=lambda (date,data):fortnight(date)):
print(key,list(grp))
yields
# (55, [(datetime.date(2010, 6, 25), 'Some data')])
# (56, [(datetime.date(2010, 7, 1), 'Some data'), (datetime.date(2010, 7, 8), 'Some data')])
# (58, [(datetime.date(2010, 8, 1), 'Some data')])
Note that 2010-6-25 is in the 55th fortnight from 2008-5-5, while 2010-7-1 is in the 56th. If you want them grouped together, simply change start_date
(to something like 2008-5-16).
PS. The key tool used above is itertools.groupby
, which is explained in detail here.
Edit: The lambda
s are simply a way to make "anonymous" functions. (They are anonymous in the sense that they are not given names like functions defined by def
). Anywhere you see a lambda, it is also possible to use a def
to create an equivalent function. For example, you could do this:
import operator
transactions.sort(key=operator.itemgetter(0))
def transaction_fortnight(transaction):
date,data=transaction
return fortnight(date)
for key,grp in itertools.groupby(transactions,key=transaction_fortnight):
print(key,list(grp))