In time series data, it’s fairly common to need to compute the last known value “as of” a particular date. However, missing data is the norm, so it’s a touch more complicated than doing a simple binary search. Here is an implementation using array operations that takes these things into account:

Some algorithmic notes. First, let’s run this through line_profiler on a large time series to see where time is being taken up:

The main trickiness here is this step:

Since the indices returned by searchsorted are relative to the filtered values, you need to remap to what would have been the indices in the original array, and this does exactly that. Lastly, you might be interested in breaking up stamps[mask].searchsorted(where, side='right') to see how much time is spend in stamps[mask] versus searchsorted:

So, this could definitely be sped up by a factor or 2 or more by doing the whole operation in C or Cython, but it’s nice to be able to express it concisely and speedily with array operations.