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JulianCologne avatar JulianCologne commented on August 18, 2024

I see you point and it makes sense!

Ideally you would use the Decimal type which maps exactly to those database types with a specific precision and scale!
Unfortunately this is still experimental and has lots of limitations and I just checked and its not possible to write a Decimal to csv 😆

example for Decimal type

(
    pl.DataFrame(
        {
            "x": ["0.123", "0.234", "0.345"],
            "y": ["0.123", "0.234", "0.345"],
        }
    )
    .with_columns(
        pl.col("x").cast(pl.Decimal(precision=19, scale=6)),
        pl.col("y").cast(pl.Decimal(precision=5, scale=3)),
    )
    # .write_csv("decimal.csv")  # not working
)

# shape: (3, 2)
# ┌───────────────┬──────────────┐
# │ x             ┆ y            │
# │ ---           ┆ ---          │
# │ decimal[19,6] ┆ decimal[5,3] │
# ╞═══════════════╪══════════════╡
# │ 0.123000      ┆ 0.123        │
# │ 0.234000      ┆ 0.234        │
# │ 0.345000      ┆ 0.345        │
# └───────────────┴──────────────┘

from polars.

d-reynol avatar d-reynol commented on August 18, 2024

Yes @JulianCologne , I agree that that would be the ideal solution.

from polars.

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