polars.corr

polars.corr(a: str | Expr, b: str | Expr, *, method: CorrelationMethod = 'pearson', ddof: int = 1, propagate_nans: bool = False) → Expr

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

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 │
└─────┘