polars.Expr.unique_counts#

Expr.unique_counts() → Expr[source]#

Return a count of the unique values in the order of appearance.

This method differs from value_counts in that it does not return the values, only the counts and might be faster

Examples

>>> df = pl.DataFrame(
...     {
...         "id": ["a", "b", "b", "c", "c", "c"],
...     }
... )
>>> df.select(pl.col("id").unique_counts())
shape: (3, 1)
┌─────┐
│ id  │
│ --- │
│ u32 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

Note that group_by can be used to generate counts.

>>> df.group_by("id", maintain_order=True).len().select("len")
shape: (3, 1)
┌─────┐
│ len │
│ --- │
│ u32 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

To add counts as a new column pl.len() can be used as a window function.

>>> df.with_columns(pl.len().over("id"))
shape: (6, 2)
┌─────┬─────┐
│ id  ┆ len │
│ --- ┆ --- │
│ str ┆ u32 │
╞═════╪═════╡
│ a   ┆ 1   │
│ b   ┆ 2   │
│ b   ┆ 2   │
│ c   ┆ 3   │
│ c   ┆ 3   │
│ c   ┆ 3   │
└─────┴─────┘