[Python] Polars - Flatten rows into columns, aggregating by column values

I'm trying to write a script in Polars that would flatten a list of prices per date and minute. The catch is I want to incrementally aggregate into columns and zero out values in the future. For example. Idea is to make this solution vectorized if possible to make it performant.

df = pl.DataFrame({
"date": ["2022-01-01", "2022-01-01", "2022-01-02", "2022-01-02",
"2022-01-02", "2022-01-03", "2022-01-03", "2022-01-03"],
"minute": [1, 2, 1, 2, 3, 1, 2, 3],
"price": [10, 20, 15, 10, 20, 30, 60, 70]
})


Should build the following dataframe.

shape: (8, 5)
┌────────────┬────────┬─────────┬─────────┬───────────┐
│ date ┆ minute ┆ 1_price ┆ 2_price ┆ 3_price │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ i64 ┆ i64 │
╞════════════╪════════╪═════════╪═════════╪═══════════╡
│ 2022-01-01 ┆ 1 ┆ 10 ┆ 0 ┆ 0 │
│ 2022-01-01 ┆ 2 ┆ 10 ┆ 20 ┆ 0 │
│ 2022-01-02 ┆ 1 ┆ 15 ┆ 0 ┆ 0 │
│ 2022-01-02 ┆ 2 ┆ 15 ┆ 10 ┆ 0 │
│ 2022-01-02 ┆ 3 ┆ 15 ┆ 10 ┆ 20 │
│ 2022-01-03 ┆ 1 ┆ 30 ┆ 0 ┆ 0 │
│ 2022-01-03 ┆ 2 ┆ 30 ┆ 60 ┆ 0 │
│ 2022-01-03 ┆ 3 ┆ 30 ┆ 60 ┆ 70 │
└────────────┴────────┴─────────┴─────────┴───────────┘

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