Python list of first day of month for given period

Question

I am trying find an efficient way of creating a list of dates only including the first day of the month for a given period. Something like this but better:

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Answer 1

There is no reason to bruteforce this:

import datetime
from pprint import pprint

dt = datetime.date.today()
end = datetime.date(2015, 2, 1)
dates = []

while dt < end:
    if not dt.month % 12:
        dt = datetime.date(dt.year+1, 1, 1)
    else:
        dt = datetime.date(dt.year, dt.month+1, 1)
    dates.append(dt)

pprint(dates)

Output:

[datetime.date(2014, 4, 1),
 datetime.date(2014, 5, 1),
 datetime.date(2014, 6, 1),
 datetime.date(2014, 7, 1),
 datetime.date(2014, 8, 1),
 datetime.date(2014, 9, 1),
 datetime.date(2014, 10, 1),
 datetime.date(2014, 11, 1),
 datetime.date(2014, 12, 1),
 datetime.date(2015, 1, 1),
 datetime.date(2015, 2, 1)]

Answer 2

You can use relativedelta from dateutil, and then create a function to use any date range:

from datetime import date
from dateutil.relativedelta import relativedelta

def mthStList(start_date, end_date):
    stdt_list = []
    cur_date = start_date.replace(day=1) # sets date range to start of month
    while cur_date <= end_date:
        stdt_list.append(cur_date)
        cur_date += relativedelta(months=+1)
    return stdt_list

mthStList(date(2012, 5, 26), date.today())

Answer 3

import datetime 
import calendar 
import re
from datetime import date

def findDay(date): 
    born = datetime.datetime.strptime(date, '%Y %m %d').weekday() 
    return (calendar.day_name[born]) 

def hyp(j):    
    no_hyphens = re.sub('-',' ',str(j))
    return no_hyphens

def send(year_start):    
    date = hyp(year_start)
    print (date)
    print(findDay(date))

epoch_year = 2019
for i in range(1,13):
    year_start = date(epoch_year, i, 1)
    send(year_start)

Answer 4

Try pandas.date_range() with the 'MS' (Month Start) frequency alias:

import pandas as pd

start = '2020-01-01'
end = '2020-12-01'

dates = pd.date_range(start, end, freq='MS')

print(dates)

Result:

DatetimeIndex(['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
               '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',
               '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01'],
              dtype='datetime64[ns]', freq='MS')

A list of all frequency aliases can be found here.

Answer 5

If you're only creating the list for a few years then efficiency should not be a concern. Clarity of code is the most important aspect.

dates = []
date = datetime.date.today()
while date.year < 2015:
    if date.day == 1:
        dates.append(date)
    date += datetime.timedelta(days=1)

Answer 6

With pandas :

   dates= pd.date_range('2018-01-01','2020-01-01' , freq='1M')-pd.offsets.MonthBegin(1)

result :

`DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',
               '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',
               '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',
               '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',
               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',
               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01'],
              dtype='datetime64[ns]', freq='MS')