resample is more general than asfreq. For example, using resample I can pass an arbitrary function to perform binning over a Series or DataFrame object in bins of arbitrary size. asfreq is a concise way of changing the frequency of a DatetimeIndex object. It also provides padding functionality.
As the pandas documentation says, asfreq is a thin wrapper around a call to date_range + a call to reindex. See here for an example.
An example of resample that I use in my daily work is computing the number of spikes of a neuron in 1 second bins by resampling a large boolean array where True means “spike” and False means “no spike”. I can do that as easy as large_bool.resample('S', how='sum'). Kind of neat!
asfreq can be used when you want to change a DatetimeIndex to have a different frequency while retaining the same values at the current index.
Here’s an example where they are equivalent:
In [6]: dr = date_range('1/1/2010', periods=3, freq=3 * datetools.bday)
In [7]: raw = randn(3)
In [8]: ts = Series(raw, index=dr)
In [9]: ts
Out[9]:
2010-01-01 -1.948
2010-01-06 0.112
2010-01-11 -0.117
Freq: 3B, dtype: float64
In [10]: ts.asfreq(datetools.BDay())
Out[10]:
2010-01-01 -1.948
2010-01-04 NaN
2010-01-05 NaN
2010-01-06 0.112
2010-01-07 NaN
2010-01-08 NaN
2010-01-11 -0.117
Freq: B, dtype: float64
In [11]: ts.resample(datetools.BDay())
Out[11]:
2010-01-01 -1.948
2010-01-04 NaN
2010-01-05 NaN
2010-01-06 0.112
2010-01-07 NaN
2010-01-08 NaN
2010-01-11 -0.117
Freq: B, dtype: float64
As far as when to use either: it depends on the problem you have in mind…care to share?