My data frame data
has a date variable dateOpen
with the following format date_format = "%Y-%m-%d %H:%M:%S.%f"
and I would like to have a new column called openDay
which is the day number based on 365 days a year. I tried applying the following
data['dateOpen'] = [datetime.strptime(dt, date_format) for dt in data['dateOpen']]
data['openDay'] = [dt.day for dt in data['dateOpen']]
however, I get the day in the month. For example if the date was 2013-02-21 10:12:14.3
then the above formula would return 21. However, I want it to return 52 which is 31 days from January plus the 21 days from February.
Is there a simple way to do this in Pandas?
On latest pandas you can use date-time properties :
>>> ts = pd.Series(pd.to_datetime(['2013-02-21 10:12:14.3']))
>>> ts
0 2013-02-21 10:12:14.300000
dtype: datetime64[ns]
>>> ts.dt.dayofyear
0 52
dtype: int64
On older versions, you may be able to convert to a DatetimeIndex
and then use .dayofyear
property:
>>> pd.Index(ts).dayofyear # may work
array([52], dtype=int32)
Not sure if there's a pandas
builtin, but in Python, you can get the "Julian" day, eg:
data['openDay'] = [int(format(dt, '%j')) for dt in data['dateOpen']]
Example:
>>> from datetime import datetime
>>> int(format(datetime(2013,2,21), '%j'))
52
#To find number of days in this year sofar
from datetime import datetime
from datetime import date
today = date.today()
print("Today's date:", today)
print(int(format(today, '%j')))
Today's date: 2020-03-26
86
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