in [ ]irr_ts[indexernp nan in [ ]irr_ts[ : ': : 'out[ ] : : : : nan : : : : : : nan : : : : nan : : nan : : nan : : nan : : nan by passing an array of timestamps to the asof methodyou will obtain an array of the last valid (non-navalues at or before each timestamp so we construct date range at am for each day and pass that to asofin [ ]selection pd date_range( : 'periods= freq=' 'in [ ]irr_ts asof(selectionout[ ] : : : : : : : : freqb splicing together data sources in described number of strategies for merging together two related data sets in financial or economic contextthere are few widely occurring use casesswitching from one data source ( time series or collection of time seriesto another at specific point in time "patchingmissing values in time series at the beginningmiddleor end using another time series completely replacing the data for subset of symbols (countriesasset tickersand so onin the first caseswitching from one set of time series to another at specific instantit is matter of splicing together two timeseries or dataframe objects using pandas con catin [ ]data dataframe(np ones(( )dtype=float)columns=[' '' '' ']index=pd date_range(''periods= ) financial and economic data applications www it-ebooks info