polars.Expr.truncate#

Expr.truncate(decimals: int = 0) → Expr[source]#

Truncate numeric data toward zero to decimals number of decimal places.

Parameters:

decimals: Number of decimal places to truncate to.

See also

ceil: Round up to the nearest integer.
floor: Round down to the nearest integer.
round: Round to a given number of decimals.
round_sig_figs: Round to a given number of significant figures.

Notes

Truncation discards the fractional part beyond the given number of decimals. For example, when rounding to 0 decimals 0.25, -0.25, 0.99, and -0.99 will all round to 0. When rounding to 1 decimal 1.9999 rounds to 1.9 and -1.9999 rounds to -1.9. There is no tiebreak behaviour at midpoint values as there is with round() so 0.5 and -0.5 will also round to 0 when decimals=1.
This method performs numeric truncation. For truncating temporal data (dates/datetimes), use Expr.dt.truncate() instead.

Examples

>>> df = pl.DataFrame({"n": [-9.9999, 0.12345, 1.0251, 8.8765]})
>>> df.with_columns(
...     t0=pl.col("n").truncate(0),
...     t1=pl.col("n").truncate(1),
...     t2=pl.col("n").truncate(2),
...     t3=pl.col("n").truncate(3),
...     t4=pl.col("n").truncate(4),
... )
shape: (4, 6)
┌─────────┬──────┬──────┬───────┬────────┬─────────┐
│ n       ┆ t0   ┆ t1   ┆ t2    ┆ t3     ┆ t4      │
│ ---     ┆ ---  ┆ ---  ┆ ---   ┆ ---    ┆ ---     │
│ f64     ┆ f64  ┆ f64  ┆ f64   ┆ f64    ┆ f64     │
╞═════════╪══════╪══════╪═══════╪════════╪═════════╡
│ -9.9999 ┆ -9.0 ┆ -9.9 ┆ -9.99 ┆ -9.999 ┆ -9.9999 │
│ 0.12345 ┆ 0.0  ┆ 0.1  ┆ 0.12  ┆ 0.123  ┆ 0.1234  │
│ 1.0251  ┆ 1.0  ┆ 1.0  ┆ 1.02  ┆ 1.025  ┆ 1.025   │
│ 8.8765  ┆ 8.0  ┆ 8.8  ┆ 8.87  ┆ 8.876  ┆ 8.8765  │
└─────────┴──────┴──────┴───────┴────────┴─────────┘