I would like to count unique combinations in two Polars columns.

In R

df <- data.frame(a = c(2,0,1,0,0,0), b = c(1,1,1,0,0,1))
table(df) 
    0 1
  0 2 2
  1 0 1
  2 0 1

In Pandas

import numpy as np
a = np.array([2,0,1,0,0,0])
b = np.array([1,1,1,0,0,1])
pd.crosstab(a, b)
    0   1       
0   2   2
1   0   1
2   0   1

In Polars

Is this the proper way?

df = pl.DataFrame(
    {
        "a": [2,0,1,0,0,0],
        "b": [1,1,1,0,0,1]
    }
)
df.pivot(values="a", index="b", columns="a", aggregate_function="count").fill_null(0)

>Solution :

I think you want to invert your "a" and "b" in your pivot. You can also use the sort_columns parameter along with a .sort at the end to get the same output

df.pivot('b', 'a','b','count',sort_columns=True).fill_null(0).sort('a')
shape: (3, 3)
┌─────┬─────┬─────┐
│ a   ┆ 0   ┆ 1   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ u32 ┆ u32 │
╞═════╪═════╪═════╡
│ 0   ┆ 2   ┆ 2   │
│ 1   ┆ 0   ┆ 1   │
│ 2   ┆ 0   ┆ 1   │
└─────┴─────┴─────┘