See https://github.com/pola-rs/pyo3-polars for more details.

expressions.rs

use polars::prelude::*;use pyo3_polars::derive::polars_expr;#[polars_expr(output_type=Int64)]fnmin_time(inputs:&[Series]) -> PolarsResult<Series>{let time = inputs[0].i64()?;let minutes = inputs[1].i64()?;let initial_value = time.get(0).unwrap() + minutes.get(0).unwrap();let out = time
.into_iter().zip(minutes.into_iter()).scan(initial_value, |state,(time, minutes)| {let time = time?;let minutes = minutes?;if*state > time {*state = time + minutes
}Some(*state)}).collect();Ok(out)}

run.py

importpolarsasplfromexpression_libimportAccumulatedf=pl.DataFrame({
"Time": [5, 3, 4, 1, 2],
"Minutes": [2, 1, 2, 1, 3],
})
print(
df.with_columns(
cum_min_time=pl.col("Time").accumulate.min_time("Minutes")
)
)

Output:

% cd example/derive_expression
% make run

shape: (5, 3)
┌──────┬─────────┬──────────────┐
│ Time ┆ Minutes ┆ cum_min_time │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞══════╪═════════╪══════════════╡
│ 5 ┆ 2 ┆ 7 │
│ 3 ┆ 1 ┆ 4 │
│ 4 ┆ 2 ┆ 4 │
│ 1 ┆ 1 ┆ 2 │
│ 2 ┆ 3 ┆ 2 │
└──────┴─────────┴──────────────┘