polars.Expr.quantile

Expr.quantile(

quantile: float | list_[float] | Expr,

interpolation: QuantileMethod = 'nearest',

) → Expr

Get quantile value.

Parameters:

quantile

Quantile(s) between 0.0 and 1.0. Can be a single float or a list of floats.

• If a single float, returns a single f64 value per row.

• If a list of floats, returns a list of f64 values per row (one value per quantile).

interpolation{‘nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’, ‘equiprobable’}

Interpolation method.

Examples

>>> df = pl.DataFrame({"a": [0, 1, 2, 3, 4, 5]})
>>> df.select(pl.col("a").quantile(0.3))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 2.0 │
└─────┘
>>> df.select(pl.col("a").quantile(0.3, interpolation="higher"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 2.0 │
└─────┘
>>> df.select(pl.col("a").quantile(0.3, interpolation="lower"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.0 │
└─────┘
>>> df.select(pl.col("a").quantile(0.3, interpolation="midpoint"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.5 │
└─────┘
>>> df.select(pl.col("a").quantile(0.3, interpolation="linear"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 1.5 │
└─────┘
>>> df.select(pl.col("a").quantile([0.25, 0.75], interpolation="linear"))
shape: (1, 1)
┌──────────────┐
│ a            │
│ ---          │
│ list[f64]    │
╞══════════════╡
│ [1.25, 3.75] │
└──────────────┘