This example seems to call for a weighted mean as suggested in @andyhayden's comment. For example, there are two polls on 10/25 and one each on 10/26 and 10/27. If you just resample and then take the mean, this effectively gives twice as much weighting to the polls on 10/26 and 10/27 compared to the ones on 10/25.
To give equal weight to each poll rather than equal weight to each day, you could do something like the following.
>>> wt = df.resample('D',limit=5).count()
favorable unfavorable other
enddate
2012-10-25 2 2 2
2012-10-26 1 1 1
2012-10-27 1 1 1
>>> df2 = df.resample('D').mean()
favorable unfavorable other
enddate
2012-10-25 0.495 0.485 0.025
2012-10-26 0.560 0.400 0.040
2012-10-27 0.510 0.470 0.020
That gives you the raw ingredients for doing a poll-based mean instead of a day-based mean. As before, the polls are averaged on 10/25, but the weight for 10/25 is also stored and is double the weight on 10/26 or 10/27 to reflect that two polls were taken on 10/25.
>>> df3 = df2 * wt
>>> df3 = df3.rolling(3,min_periods=1).sum()
>>> wt3 = wt.rolling(3,min_periods=1).sum()
>>> df3 = df3 / wt3
favorable unfavorable other
enddate
2012-10-25 0.495000 0.485000 0.025000
2012-10-26 0.516667 0.456667 0.030000
2012-10-27 0.515000 0.460000 0.027500
2012-10-28 0.496667 0.465000 0.041667
2012-10-29 0.484000 0.478000 0.042000
2012-10-30 0.488000 0.474000 0.042000
2012-10-31 0.530000 0.450000 0.020000
2012-11-01 0.500000 0.465000 0.035000
2012-11-02 0.490000 0.470000 0.040000
2012-11-03 0.490000 0.465000 0.045000
2012-11-04 0.500000 0.448333 0.035000
2012-11-05 0.501429 0.450000 0.032857
2012-11-06 0.503333 0.450000 0.028333
2012-11-07 0.510000 0.435000 0.010000
Note that the rolling mean for 10/27 is now 0.51500 (poll-weighted) rather than 52.1667 (day-weighted).
Also note that there have been changes to the APIs for resample and rolling as of version 0.18.0.