How to create a pandas DatetimeIndex with year as frequency?

Question

Using the pandas.date_range(startdate, periods=n, freq=f) function you can create a range of pandas Timestamp objects where the freq optional paramter denotes the frequency (second, minute, hour, day...) in the range.

The documentation does not mention the literals that are expected to be passed in, but after a few minutes you can easily find most of them.

• 's' : second
• 'min' : minute
• 'H' : hour
• 'D' : day
• 'w' : week
• 'm' : month

However, none of 'y', 'Y', 'yr', etc. create dates with year as frequency. Does anybody know what to pass in, or if it is possible at all?

Answer 1

Annual indexing to the beginning or end of the year

Frequency is freq='A' for end of year frequency, 'AS' for start of year. Check the aliases in the documentation.

eg. pd.date_range(start=pd.datetime(2000, 1, 1), periods=4, freq='A')

returns

DatetimeIndex(['2000-12-31', '2001-12-31', '2002-12-31', '2003-12-31'], dtype='datetime64[ns]', freq='A-DEC', tz=None)

Annual indexing to the beginning of an arbitrary month

If you need it to be annual from a particular time use an anchored offset, eg. pd.date_range(start=pd.datetime(2000, 1, 1), periods=10, freq='AS-AUG')

returns

DatetimeIndex(['2000-08-01', '2001-08-01', '2002-08-01', '2003-08-01'], dtype='datetime64[ns]', freq='AS-AUG', tz=None)

Annual indexing from an arbitrary date

To index from an arbitrary date, begin the series on that date and use a custom DateOffset object.

eg. pd.date_range(start=pd.datetime(2000, 9, 10), periods=4, freq=pd.DateOffset(years=1))

returns

DatetimeIndex(['2000-09-10', '2001-09-10', '2002-09-10', '2003-09-10'], dtype='datetime64[ns]', freq='<DateOffset: kwds={'years': 1}>', tz=None)

Answer 2

With all those hacks, there is a clear way:

pd.date_range(start=datetime.datetime.now(),periods=5,freq='A') 

A : Annually.

365D? Really? What about leap years?