polars.Expr.is_close#

Expr.is_close( other: IntoExpr, *, abs_tol: float = 0.0, rel_tol: float = 1e-09, nans_equal: bool = False, ) → Expr[source]#

Check if this expression is close, i.e. almost equal, to the other expression.

Two values a and b are considered close if the following condition holds:

\[|a-b| \le max \{ \text{rel_tol} \cdot max \{ |a|, |b| \}, \text{abs_tol} \}\]

Parameters:

other: A literal or expression value to compare with.
abs_tol: Absolute tolerance. This is the maximum allowed absolute difference between two values. Must be non-negative.
rel_tol: Relative tolerance. This is the maximum allowed difference between two values, relative to the larger absolute value. Must be non-negative.
nans_equal: Whether NaN values should be considered equal.

Returns:

Expr: Expression of data type Boolean.

Notes

The implementation of this method is symmetric and mirrors the behavior of math.isclose(). Specifically note that this behavior is different to numpy.isclose().

Examples

>>> df = pl.DataFrame({"a": [1.5, 2.0, 2.5], "b": [1.55, 2.2, 3.0]})
>>> df.with_columns(pl.col("a").is_close("b", abs_tol=0.1).alias("is_close"))
shape: (3, 3)
┌─────┬──────┬──────────┐
│ a   ┆ b    ┆ is_close │
│ --- ┆ ---  ┆ ---      │
│ f64 ┆ f64  ┆ bool     │
╞═════╪══════╪══════════╡
│ 1.5 ┆ 1.55 ┆ true     │
│ 2.0 ┆ 2.2  ┆ false    │
│ 2.5 ┆ 3.0  ┆ false    │
└─────┴──────┴──────────┘