Polars truncates decimals

Question

I'm trying to truncate floating point numbers in my DataFrame to a desired number of decimal places. I've found that this can be done using Pandas and NumPy here, but I've also seen that it might be possible with polars.Config.set_float_precision.

Below is my current approach, but I think I might be taking extra steps.

import polars as pl

data = {
    "name": ["Alice", "Bob", "Charlie"],
    "grade": [90.23456, 80.98765, 85.12345],
}

df = pl.DataFrame(data)

(
    df
    # Convert to string
    .with_columns(
        pl.col("grade").map_elements(
            lambda x: f"{x:.5f}", 
            return_dtype=pl.String
        ).alias("formatted_grade")
    )
    # Slice to get desired decimals
    .with_columns(
        pl.col("formatted_grade").str.slice(0, length = 4)
    )
    # Convert back to Float
    .with_columns(
        pl.col("formatted_grade").cast(pl.Float64)
    )
)

Answer 1

You can use the Polars - Numpy integration like this:

df = df.with_columns(truncated_grade=np.trunc(pl.col("grade") * 10) / 10)

Output:

┌─────────┬──────────┬─────────────────┐
│ name    ┆ grade    ┆ truncated_grade │
│ ---     ┆ ---      ┆ ---             │
│ str     ┆ f64      ┆ f64             │
╞═════════╪══════════╪═════════════════╡
│ Alice   ┆ 90.23456 ┆ 90.2            │
│ Bob     ┆ 80.98765 ┆ 80.9            │
│ Charlie ┆ 85.12345 ┆ 85.1            │
└─────────┴──────────┴─────────────────┘

Full code:

import numpy as np
import polars as pl

data = {
    "name": ["Alice", "Bob", "Charlie"],
    "grade": [90.23456, 80.98765, 85.12345],
}

df = pl.DataFrame(data)
df = df.with_columns(truncated_grade=np.trunc(pl.col("grade") * 10) / 10)

print(df)