Difference between asfreq and resample

Question

Can some please explain the difference between the asfreq and resample methods in pandas? When should one use what?

Answer 1

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?