Determine season given timestamp in Python using datetime

if the date falls between March 21 and June 20, it is spring. Regardless of the year. I want it to just look at the month and day and ignore the year in this calculation.

#!/usr/bin/env python
from datetime import date, datetime

Y = 2000 # dummy leap year to allow input X-02-29 (leap day)
seasons = [('winter', (date(Y,  1,  1),  date(Y,  3, 20))),
           ('spring', (date(Y,  3, 21),  date(Y,  6, 20))),
           ('summer', (date(Y,  6, 21),  date(Y,  9, 22))),
           ('autumn', (date(Y,  9, 23),  date(Y, 12, 20))),
           ('winter', (date(Y, 12, 21),  date(Y, 12, 31)))]

def get_season(now):
    if isinstance(now, datetime):
        now = now.date()
    now = now.replace(year=Y)
    return next(season for season, (start, end) in seasons
                if start <= now <= end)

print(get_season(date.today()))

It is an extended version of @Manuel G answer to support any year.

It might be easier just to use the day of year parameter. It's not much different than your approach, but possibly easier to understand than the magic numbers.

# get the current day of the year
doy = datetime.today().timetuple().tm_yday

# "day of year" ranges for the northern hemisphere
spring = range(80, 172)
summer = range(172, 264)
fall = range(264, 355)
# winter = everything else

if doy in spring:
  season = 'spring'
elif doy in summer:
  season = 'summer'
elif doy in fall:
  season = 'fall'
else:
  season = 'winter'

I came here looking how to map dates to seasons, and based on this answer I finally solved it in the following way:

def season_of_date(date):
    year = str(date.year)
    seasons = {'spring': pd.date_range(start='21/03/'+year, end='20/06/'+year),
               'summer': pd.date_range(start='21/06/'+year, end='22/09/'+year),
               'autumn': pd.date_range(start='23/09/'+year, end='20/12/'+year)}
    if date in seasons['spring']:
        return 'spring'
    if date in seasons['summer']:
        return 'summer'
    if date in seasons['autumn']:
        return 'autumn'
    else:
        return 'winter'

# Assuming df has a date column of type `datetime`
df['season'] = df.date.map(season_of_date)

So in principle it works for any year, given a datetime.