polars.Expr.approx_n_unique#

Expr.approx_n_unique() Self[source]#

Approximate count of unique values.

This is done using the HyperLogLog++ algorithm for cardinality estimation.

Examples

>>> df = pl.DataFrame({"n": [1, 1, 2]})
>>> df.select(pl.col("n").approx_n_unique())
shape: (1, 1)
┌─────┐
│ n   │
│ --- │
│ u32 │
╞═════╡
│ 2   │
└─────┘
>>> df = pl.DataFrame({"n": range(1000)})
>>> df.select(
...     exact=pl.col("n").n_unique(),
...     approx=pl.col("n").approx_n_unique(),
... )
shape: (1, 2)
┌───────┬────────┐
│ exact ┆ approx │
│ ---   ┆ ---    │
│ u32   ┆ u32    │
╞═══════╪════════╡
│ 1000  ┆ 1005   │
└───────┴────────┘