extend a pandas datetimeindex by 1 period

pandas==1.1.1 Answer for +1

To follow up on this, for pandas==1.1.1, I found this to be the best solution:

dates.union(pd.date_range(dates[-1] + dates.freq, periods=1, freq=dates.freq))

Generalised Answer Using n

n=3
dates.union(pd.date_range(dates[-1] + dates.freq, periods=n, freq=dates.freq))

Credits

Taken by combining @alberto-garcia-raboso's answer and @ballpointben's comment.

What Didn't Work

• The following just got formatted to an Index, not a DateTimeIndex: dates.union([dates[-1] + dates.freq])
• Also dates[-1] + 1 is deprecated.

The timestamps in your DatetimeIndex already know that they are describing business month ends, so you can simply add 1:

import pandas as pd
dates = pd.date_range('2016-01-29', periods=4, freq='BM')

print(repr(dates[-1]))
# => Timestamp('2016-04-29 00:00:00', offset='BM')

print(repr(dates[-1] + 1))
# => Timestamp('2016-05-31 00:00:00', offset='BM')

You can add the latter to your index using .union:

dates = dates.union([dates[-1] + 1])
print(dates)
# => DatetimeIndex(['2016-01-29', '2016-02-29', '2016-03-31', '2016-04-29',
#                   '2016-05-31'],
#                  dtype='datetime64[ns]', freq='BM')

Compared to .append, this retains knowledge of the offset.