[Python] Return two closest rows above and below a target value in Polars

I'm trying to figure out the most elegant way in Polars to find the two bracketing rows (first above and first below) a specific target. Essentially the Min > 0 & the Max < 0.

data = {
"strike": [5,10,15,20,25,30],
"target": [16] * 6,
}

df = (pl.DataFrame(data)
.with_columns(
diff = pl.col('strike') - pl.col('target')))

shape: (6, 3)
┌────────┬────────┬──────┐
│ strike ┆ target ┆ diff │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞════════╪════════╪══════╡
│ 5 ┆ 16 ┆ -11 │
│ 10 ┆ 16 ┆ -6 │
│ 15 ┆ 16 ┆ -1 │
│ 20 ┆ 16 ┆ 4 │
│ 25 ┆ 16 ┆ 9 │
│ 30 ┆ 16 ┆ 14 │
└────────┴────────┴──────┘


This is what I'm trying to arrive at.

shape: (2, 3)
┌────────┬────────┬──────┐
│ strike ┆ target ┆ diff │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞════════╪════════╪══════╡
│ 15 ┆ 16 ┆ -1 │
│ 20 ┆ 16 ┆ 4 │
└────────┴────────┴──────┘


I am able to do it through two separate filter operations but I can't seem to string these together and would like to avoid having to vstack the two individual results back together into a single data frame if possible.

df1 = (pl.DataFrame(data)
.with_columns(
diff = pl.col('strike') - pl.col('target'))
.filter((pl.col('diff') > 0)).min())

shape: (1, 3)
┌────────┬────────┬──────┐
│ strike ┆ target ┆ diff │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞════════╪════════╪══════╡
│ 20 ┆ 16 ┆ 4 │
└────────┴────────┴──────┘

df2 = (pl.DataFrame(data)
.with_columns(
diff = pl.col('strike') - pl.col('target'))
.filter((pl.col('diff') < 0)).max())

shape: (1, 3)
┌────────┬────────┬──────┐
│ strike ┆ target ┆ diff │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞════════╪════════╪══════╡
│ 15 ┆ 16 ┆ -1 │
└────────┴────────┴──────┘

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