Iterating through a range of dates in Python

Question

I have the following code to do this, but how can I do it better? Right now I think it\'s better than nested loops, but it starts to get Perl-one-linerish when you have a ge

Answer 1

What about the following for doing a range incremented by days:

for d in map( lambda x: startDate+datetime.timedelta(days=x), xrange( (stopDate-startDate).days ) ):
  # Do stuff here

• startDate and stopDate are datetime.date objects

For a generic version:

for d in map( lambda x: startTime+x*stepTime, xrange( (stopTime-startTime).total_seconds() / stepTime.total_seconds() ) ):
  # Do stuff here

• startTime and stopTime are datetime.date or datetime.datetime object (both should be the same type)
• stepTime is a timedelta object

Note that .total_seconds() is only supported after python 2.7 If you are stuck with an earlier version you can write your own function:

def total_seconds( td ):
  return float(td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10**6

Answer 2

For completeness, Pandas also has a period_range function for timestamps that are out of bounds:

import pandas as pd

pd.period_range(start='1/1/1626', end='1/08/1627', freq='D')

Answer 3

This is the most human-readable solution I can think of.

import datetime

def daterange(start, end, step=datetime.timedelta(1)):
    curr = start
    while curr < end:
        yield curr
        curr += step

Answer 4

Using pendulum.period:

import pendulum

start = pendulum.from_format('2020-05-01', 'YYYY-MM-DD', formatter='alternative')
end = pendulum.from_format('2020-05-02', 'YYYY-MM-DD', formatter='alternative')

period = pendulum.period(start, end)

for dt in period:
    print(dt.to_date_string())

Answer 5

import datetime
from dateutil.rrule import DAILY,rrule

date=datetime.datetime(2019,1,10)

date1=datetime.datetime(2019,2,2)

for i in rrule(DAILY , dtstart=date,until=date1):
     print(i.strftime('%Y%b%d'),sep='\n')

OUTPUT:

2019Jan10
2019Jan11
2019Jan12
2019Jan13
2019Jan14
2019Jan15
2019Jan16
2019Jan17
2019Jan18
2019Jan19
2019Jan20
2019Jan21
2019Jan22
2019Jan23
2019Jan24
2019Jan25
2019Jan26
2019Jan27
2019Jan28
2019Jan29
2019Jan30
2019Jan31
2019Feb01
2019Feb02