[Python] Does polars preserve row order in a left join?

Consider the following polars dataframes:

>>> left = pl.DataFrame(pl.Series('a', [1,5,3,2]))
>>> left
shape: (4, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 1 │
│ 5 │
│ 3 │
│ 2 │
└─────┘
>>> right = pl.DataFrame([pl.Series('a', [0,1,2,3]), pl.Series('b', [4,5,6,7])])
>>> right
shape: (4, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 0 ┆ 4 │
│ 1 ┆ 5 │
│ 2 ┆ 6 │
│ 3 ┆ 7 │
└─────┴─────┘


I would like to join the two in such a way that the order of the a values from the left dataframe is preserved. A left join seems to do this:

>>> left.join(right, on='a', how='left')
shape: (4, 2)
┌─────┬──────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪══════╡
│ 1 ┆ 5 │
│ 5 ┆ null │
│ 3 ┆ 7 │
│ 2 ┆ 6 │
└─────┴──────┘


My question is: is this behaviour guaranteed? If not, what would be the safe way to do this? I could use with_row_index and then do a final sort, but that seems rather cumbersome. In pandas this can be done concisely with the reindex method.

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