The rolling window function pandas.DataFrame.rolling of pandas 0.22 takes a window argument that is described as follows:

window : int, or offset

Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size.

If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. This is new in 0.19.0

What actually is an offset in this context?

In a nutshell, if you use an offset like "2D" (2 days), pandas will use the datetime info in the index (if available), potentially accounting for any missing rows or irregular frequencies. But if you use a simple int like 2, then pandas will treat the index as a simple integer index [0,1,2,...] and ignore any datetime info in the index.

A simple example should make this clear:

df=pd.DataFrame({'x':range(4)}, 
    index=pd.to_datetime(['1-1-2018','1-2-2018','1-4-2018','1-5-2018']))

            x
2018-01-01  0
2018-01-02  1
2018-01-04  2
2018-01-05  3

Note that (1) the index is a datetime, but also (2) it is missing '2018-01-03'. So if you use a plain integer like 2, rolling will just look at the last two rows, regardless of the datetime value (in a sense it's behaving like iloc[i-1:i] where i is the current row):

df.rolling(2).count()

              x
2018-01-01  1.0
2018-01-02  2.0
2018-01-04  2.0
2018-01-05  2.0

Conversely, if you use an offset of 2 days ('2D'), rolling will use the actual datetime values and accounts for any irregularities in the datetime index.

df.rolling('2D').count()

              x
2018-01-01  1.0
2018-01-02  2.0
2018-01-04  1.0
2018-01-05  2.0

Also note, you need the index to be sorted in ascending order when using a date offset, but it doesn't matter when using a simple integer (since you're just ignoring the index anyway).