# Pearsonr

> Compute the pearsonr metric — provided by scipy.stats. Use when the user has predictions and ground-truth and needs to compute pearsonr, or asks how to score with pearsonr.

- Skill: `qhjqhj00/pearsonr` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/pearsonr`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/pearsonr/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/pearsonr

---


# pearsonr

> Metric `pearsonr` from `scipy.stats` (scipy.stats.pearsonr)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with pearsonr, or
mentions `scipy.stats.pearsonr` directly, or wants the standard scipy.stats implementation.

## Reference signature

```python
from scipy.stats import pearsonr

# pearsonr(x, y, *, alternative='two-sided', method=None, axis=0)
```

## Library docstring

```
Pearson correlation coefficient and p-value for testing non-correlation.

The Pearson correlation coefficient [1]_ measures the linear relationship
between two datasets. Like other correlation
coefficients, this one varies between -1 and +1 with 0 implying no
correlation. Correlations of -1 or +1 imply an exact linear relationship.
Positive correlations imply that as x increases, so does y. Negative
correlations imply that as x increases, y decreases.

This function also performs a test of the null hypothesis that the
distributions underlying the samples are uncorrelated and normally
distributed. (See Kowalski [3]_
for a discussion of the effects of non-normality of the input on the
distribution of the correlation coefficient.)
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Pearson correlation at least as extreme
as the one computed from these datasets.

Parameters
----------
x : array_like
    Input array.
y : array_like
    Input array.
axis : int or None, default
    Axis along which to perform the calculation. Default is 0.
    If None, ravel both arrays before performing the calculation.

    .. versionadded:: 1.14.0
alternative : {'two-sided', 'greater', 'less'}, optional
    Defines the alternative hypothesis. Default is 'two-sided'.
    The following options are available:

    * 'two-sided': the correlation is nonzero
    * 'less': the correlation is negative (less than zero)
    * 'greater':  the correlation is positive (greater than zero)

    .. versionadded:: 1.9.0
method : ResamplingMethod, optional
    Defines the method used to compute the p-value. If `method` is an
    instance of `PermutationMethod`/`MonteCarloMethod`, the p-value is
    computed using
    `scipy.stats.permutation_test`/`scipy.stats.monte_carlo_test` with the
    provided configuration options and other appropriate settings.
    Otherwise, the p-value is computed as documented in the notes.

    .. versionadded:: 1.11.0

Returns
-------
result : `~scipy.stats._result_classes.PearsonRResult`
    An object with the following attributes:

    statistic : float
        Pearson product-moment correlation coefficient.
    pvalue
```

## Quick recipe

```python
import scipy.stats as _m
score = _m.pearsonr(y_true, y_pred)
```

## Don'ts

- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.