polars.corr#

polars.corr( a: IntoExpr, b: IntoExpr, *, method: CorrelationMethod = 'pearson', ddof: int = 1, propagate_nans: bool = False, ) → Expr[source]#

Compute the Pearson’s or Spearman rank correlation correlation between two columns.

Parameters:

a: Column name or Expression.
b: Column name or Expression.
ddof: “Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1.
method{‘pearson’, ‘spearman’}: Correlation method.
propagate_nans: If True any NaN encountered will lead to NaN in the output. Defaults to False where NaN are regarded as larger than any finite number and thus lead to the highest rank.

Examples

Pearson’s correlation:

>>> df = pl.DataFrame(
...     {
...         "a": [1, 8, 3],
...         "b": [4, 5, 2],
...         "c": ["foo", "bar", "foo"],
...     }
... )
>>> df.select(pl.corr("a", "b"))
shape: (1, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ 0.544705 │
└──────────┘

Spearman rank correlation:

>>> df = pl.DataFrame(
...     {
...         "a": [1, 8, 3],
...         "b": [4, 5, 2],
...         "c": ["foo", "bar", "foo"],
...     }
... )
>>> df.select(pl.corr("a", "b", method="spearman"))
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ 0.5 │
└─────┘