I want a list of date range in which each element is 'yyyymmdd' format string, such as : ['20130226','20130227','20130228','20130301','20130302'] .
I can use pandas to do so:
>>> pandas.date_range('20130226','20130302')
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-02-26 00:00:00, ..., 2013-03-02 00:00:00]
Length: 5, Freq: D, Timezone: None
But it is DatetimeIndex and I need to do some extra format transform, so how to do that in a neat way ?
Or using list comprehension:
[d.strftime('%Y%m%d') for d in pandas.date_range('20130226','20130302')]
Using format:
>>> r = pandas.date_range('20130226','20130302')
>>> r.format(formatter=lambda x: x.strftime('%Y%m%d'))
['20130226', '20130227', '20130228', '20130301', '20130302']
or using map:
>>> r.map(lambda x: x.strftime('%Y%m%d'))
array(['20130226', '20130227', '20130228', '20130301', '20130302'], dtype=object)
Easy and clean: do it directly with pandas date_range and strftime like this:
pd.date_range(start='20130226',end='20130302',freq='D').strftime('%Y%m%d')
Resulting:
Index(['20130226', '20130227', '20130228', '20130301', '20130302'], dtype='object')
For Just a daterange, pandas would be an overkill when you actually again have to reformat the date using datetime. The following solution simply uses datetime to serve your purpose
import datetime
def date_range(start_dt, end_dt = None):
start_dt = datetime.datetime.strptime(start_dt, "%Y%m%d")
if end_dt: end_dt = datetime.datetime.strptime(end_dt, "%Y%m%d")
while start_dt <= end_dt:
yield start_dt.strftime("%Y%m%d")
start_dt += datetime.timedelta(days=1)
[e for e in date_range('20130226','20130302')]
['20130226', '20130227', '20130228', '20130301', '20130302']
