polars.DataFrame.n_unique#

DataFrame.n_unique(subset: str | Expr | Sequence[str | Expr] | None = None) → int[source]#

Return the number of unique rows, or the number of unique row-subsets.

Parameters:

subset: One or more columns/expressions that define what to count; omit to return the count of unique rows.

Notes

This method operates at the DataFrame level; to operate on subsets at the expression level you can make use of struct-packing instead, for example:

>>> expr_unique_subset = pl.struct(["a", "b"]).n_unique()

If instead you want to count the number of unique values per-column, you can also use expression-level syntax to return a new frame containing that result:

>>> df = pl.DataFrame([[1, 2, 3], [1, 2, 4]], schema=["a", "b", "c"])
>>> df_nunique = df.select(pl.all().n_unique())

In aggregate context there is also an equivalent method for returning the unique values per-group:

>>> df_agg_nunique = df.groupby(by=["a"]).n_unique()

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 1, 2, 3, 4, 5],
...         "b": [0.5, 0.5, 1.0, 2.0, 3.0, 3.0],
...         "c": [True, True, True, False, True, True],
...     }
... )
>>> df.n_unique()
5

Simple columns subset.

>>> df.n_unique(subset=["b", "c"])
4

Expression subset.

>>> df.n_unique(
...     subset=[
...         (pl.col("a") // 2),
...         (pl.col("c") | (pl.col("b") >= 2)),
...     ],
... )
3