I am in a situation where I have some time series data, potentially looking like this: { "t": [1, 2, 5, 6, 7], "y": [1, 1, 1, 1, 1], } As you can see, the time stamp jumps from 2 to 5. For my analysis, I would like to fill in zeros for the time stamps 3, and 4. In reality, I might have multiple gaps with varying lengths. I'd like to fill this gap for all other columns. I'd also really like to keep my data in a LazyFrame since this is only one step in my pipeline. I don't think that .interpolate is really addressing my issue, nor is fill_null helpful here. I managed to achieve what I want, but it looks too complex: # Dummy, lazy data. lf = pl.LazyFrame( { "t": [1, 2, 5, 6, 7], "y": [1, 1, 1, 1, 1], } ) lf_filled = lf.join( pl.Series( name="t", values=pl.int_range( start=lf.select("t").first().collect().item(0, 0), end=lf.select("t").last().collect().item(0, 0) + 1, eager=True, ), ) .to_frame() .lazy(), on="t", how="right", ).fill_null(0) The output is correct and I am never collecting any more data than the two values needed for start and end. This looks like there should be a better way to do this. Happy to hear other suggestions Continue reading...
