重新取样pandas时间序列而不计算新的偏移量

Question

I'm reading in timeseries data that contains only the available times. This leads to a Series with no missing values, but an unequally spaced index. I'd like to convert this to a Series with an equally spaced index with missing values. Since I don't know a priori what the spacing will be, I'm currently using a function like

min_dt      = np.diff(series.index.values).min()
new_spacing = pandas.DateOffset(days=min_dt.days, seconds=min_dt.seconds,
                                microseconds=min_dt.microseconds)
series      = series.asfreq(new_spacing)

to compute what the spacing should be (note that this is using Pandas 0.7.3 - the 0.8 beta code looks slightly differently since I have to use series.index.to_pydatetime() for correct behavior with Numpy 1.6).

Is there an easier way to do this operation using the pandas library?

Answer 1

If you want NaN's in the places where there is no data, you can just use Minute() located in datetools (as of pandas 0.7.x)

from pandas.core.datetools import day, Minute
tseries.asfreq(Minute())

That should provide an evenly spaced time series with 1 minute differences with NaNs as the series values where there is no data.