[Python] Rolling Time Window Average in Polars with Left-Edge Expansion

The goal is to compute a rolling average over 5-minute windows using Polars, where:

• the window ends at each timestamp t
• the left edge of the window is not strict — if there are no values exactly at t - 5min, it should include an earlier point
• input data contains columns: timestamp and value

In Pandas, I used to handle this kind of logic easily by overriding BaseIndexer along with some custom Numba-accelerated logic to compute rolling window boundaries manually. I'd love to have similar functionality in Polars, but I don't know how to achieve it yet.

Here is an example:

df = pl.DataFrame({
"timestamp": [
"2024-04-10 10:00:01",
"2024-04-10 10:01:30",
"2024-04-10 10:03:10",
"2024-04-10 10:05:00",
"2024-04-10 10:06:00",
"2024-04-10 10:08:10",
],
"value": [1, 2, 3, 4, 5, 6]
}).with_columns(
pl.col("timestamp").str.strptime(pl.Datetime)
)



┌─────────────────────┬───────┐
│ timestamp ┆ value │
│ --- ┆ --- │
│ datetime[μs] ┆ i64 │
╞═════════════════════╪═══════╡
│ 2024-04-10 10:00:01 ┆ 1 │
│ 2024-04-10 10:01:30 ┆ 2 │
│ 2024-04-10 10:03:10 ┆ 3 │
│ 2024-04-10 10:05:00 ┆ 4 │
│ 2024-04-10 10:06:00 ┆ 5 │
│ 2024-04-10 10:08:10 ┆ 6 │
└─────────────────────┴───────┘


After applying a rolling window, I want to get the following:

(df.rolling("timestamp", period="5m+????", closed="both") #??????
.agg(
pl.col("value"),
pl.mean("value").alias("rolling_value"))
)
)


┌─────────────────────┬────────────────┬───────────────┐
│ timestamp ┆ value ┆ rolling_value │
│ --- ┆ --- ┆ --- │
│ datetime[μs] ┆ list[i64] ┆ f64 │
╞═════════════════════╪════════════════╪═══════════════╡
│ 2024-04-10 10:00:01 ┆ [1] ┆ 1.0 │
│ 2024-04-10 10:01:30 ┆ [1, 2] ┆ 1.5 │
│ 2024-04-10 10:03:10 ┆ [1, 2, 3] ┆ 2.0 │
│ 2024-04-10 10:05:00 ┆ [1, 2, 3, 4] ┆ 2.5 │
│ 2024-04-10 10:06:00 ┆ [1, 2, 3, 4, 5]┆ 3.0 │. <- include the first value
│ 2024-04-10 10:08:10 ┆ [3, 4, 5, 6] ┆ 4.5 │
└─────────────────────┴────────────────┴───────────────┘

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