polars.Expr.value_counts#

Expr.value_counts(*, sort: bool = False, parallel: bool = False) → Self[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:

Expr: Expression of data type Struct with mapping of unique values to their count.

Examples

>>> df = pl.DataFrame(
...     {"color": ["red", "blue", "red", "green", "blue", "blue"]}
... )
>>> df.select(pl.col("color").value_counts())  
shape: (3, 1)
┌─────────────┐
│ color       │
│ ---         │
│ struct[2]   │
╞═════════════╡
│ {"red",2}   │
│ {"green",1} │
│ {"blue",3}  │
└─────────────┘

Sort the output by count.

>>> df.select(pl.col("color").value_counts(sort=True))
shape: (3, 1)
┌─────────────┐
│ color       │
│ ---         │
│ struct[2]   │
╞═════════════╡
│ {"blue",3}  │
│ {"red",2}   │
│ {"green",1} │
└─────────────┘