polars.Series.value_counts#

Series.value_counts(*, sort: bool = False, parallel: bool = False) → DataFrame[source]#

Count the occurrences of unique values.

Parameters:

sort: Sort the output by count in descending order. If set to False (default), the order of the output is random.
parallel: Execute the computation in parallel.

Note

This option should likely not be enabled in a group by context, as the computation is already parallelized per group.

Returns:

DataFrame: Mapping of unique values to their count.

Examples

>>> s = pl.Series("color", ["red", "blue", "red", "green", "blue", "blue"])
>>> s.value_counts()  
shape: (3, 2)
┌───────┬────────┐
│ color ┆ counts │
│ ---   ┆ ---    │
│ str   ┆ u32    │
╞═══════╪════════╡
│ red   ┆ 2      │
│ green ┆ 1      │
│ blue  ┆ 3      │
└───────┴────────┘

Sort the output by count.

shape: (3, 2) ┌───────┬────────┐ │ color ┆ counts │ │ — ┆ — │ │ str ┆ u32 │ ╞═══════╪════════╡ │ blue ┆ 3 │ │ red ┆ 2 │ │ green ┆ 1 │ └───────┴────────┘