feat: <DataFrame>$partition_by()

[eitsupi]

Close #891

Perhaps rather than adding a method to GroupBy, DataFrame's partition_by is sufficient.

library(polars)

df <- readr::read_csv(I("
id,timestamp,timezone
1,2019-01-01T00:00:00Z,UTC
2,2019-01-01T00:00:00Z,Asia/Tokyo
3,2019-01-01T20:00:00Z,UTC
4,2019-01-01T20:00:00Z,Asia/Tokyo
"), show_col_types = FALSE)

as_polars_df(df)$partition_by(
  "timezone",
  maintain_order = FALSE, as_nested_list = TRUE
) |>
  lapply(
    \(l) l$data$with_columns(
      naive_time = pl$col("timestamp")$dt$convert_time_zone(
        l$key$timezone
      )$dt$replace_time_zone(NULL)
    )
  ) |>
  pl$concat()
#> shape: (4, 4)
#> ┌─────┬─────────────────────────┬────────────┬─────────────────────┐
#> │ id  ┆ timestamp               ┆ timezone   ┆ naive_time          │
#> │ --- ┆ ---                     ┆ ---        ┆ ---                 │
#> │ f64 ┆ datetime[ms, UTC]       ┆ str        ┆ datetime[ms]        │
#> ╞═════╪═════════════════════════╪════════════╪═════════════════════╡
#> │ 2.0 ┆ 2019-01-01 00:00:00 UTC ┆ Asia/Tokyo ┆ 2019-01-01 09:00:00 │
#> │ 4.0 ┆ 2019-01-01 20:00:00 UTC ┆ Asia/Tokyo ┆ 2019-01-02 05:00:00 │
#> │ 1.0 ┆ 2019-01-01 00:00:00 UTC ┆ UTC        ┆ 2019-01-01 00:00:00 │
#> │ 3.0 ┆ 2019-01-01 20:00:00 UTC ┆ UTC        ┆ 2019-01-01 20:00:00 │
#> └─────┴─────────────────────────┴────────────┴─────────────────────┘

^{Created on 2024-03-09 with reprex v2.1.0}

[eitsupi]

Is this name appropriate?

[etiennebacher]

Yes I think it is the best equivalent to Python's dict

[etiennebacher]

Thanks, some comments. Can you also bump NEWS?

[etiennebacher]

Yes I think it is the best equivalent to Python's dict

[etiennebacher]

I'm surprised this wasn't needed before , I think DataFrame$drop() should have a similar input as DataFrame$partition_by() based on the py-polars API

[eitsupi]

@etiennebacher Could you update $drop()?

[eitsupi]

@etiennebacher Thanks for your review.
I think I have addressed your comments.
(Can't merge because it hasn't been approved/you can approve with minor comments instead of requiring changes to reduce everyone's workload)