chatterjeexi — SciPy v1.17.0 Manual
Hacker News
February 22, 2026
AI-Generated Deep Dive Summary
SciPy v1.17.0 introduces a new function `chatterjeexi` for computing the xi correlation coefficient, offering a powerful tool for measuring associations between variables in non-linear and non-monotonic relationships. Unlike traditional correlation coefficients like Pearson or Spearman, the xi correlation is particularly effective at detecting complex dependencies, making it especially useful for datasets where variables exhibit intricate patterns of association.
The xi correlation statistic ranges from -1 to 1, with values close to zero indicating independence and values near 1 (or -1) suggesting strong positive (or negative) associations. This method is particularly advantageous in scenarios where the relationship between variables is not strictly monotonic, such as in certain machine learning or statistical analyses. The function also provides a p-value for testing the null hypothesis of independence, calculated either through an asymptotic approximation or via permutation methods.
Key features of the `chatterjeexi` function include flexibility in handling input data dimensions, support for both continuous and categorical variables, and robustness to missing values (NaNs) based on specified policies. The function also allows users to assume y is drawn from a continuous distribution, which can improve computational efficiency and accuracy.
For tech professionals and researchers working with complex datasets, the xi correlation offers a valuable addition to their analytical toolkit. Its ability to uncover associations in non-linear relationships makes it particularly relevant for fields like machine learning, data science, and statistical analysis where traditional methods may fall short. By incorporating this function into their workflows, users can gain deeper insights into variable dependencies and make more informed decisions.
The inclusion of experimental support for Python Array API Standard-compatible backends further enhances the function's versatility, enabling compatibility with frameworks like CuPy, PyTorch, JAX, or D
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Originally published on Hacker News on 2/22/2026