<jupyter_start><jupyter_text>Load demo data<jupyter_code>X = load_boston().data[:, [3,8,9]]<jupyter_output><empty_output><jupyter_text>Kendall tau rank correlation<jupyter_code>rho, pval = kendall(X)<jupyter_output><empty_output><jupyter_text>Display the results<jupyter_code>print("Kendall Tau correlation\n", rho)
print("\np-values\n", pval)
print("\nsubstantial corr?\n", pval < 0.05)<jupyter_output>Kendall Tau correlation
 [[ 1.     0.022 -0.038]
 [ 0.022  1.     0.558]
 [-0.038  0.558  1.   ]]

p-values
 [[0.    0.581 0.317]
 [0.581 0.    0.   ]
 [0.317 0.    0.   ]]

substantial corr?
 [[ True False False]
 [False  True  True]
 [False  True  True]]
{'fraction_non_alphanumeric': 0.1669195751138088, 'fraction_numerical': 0.0819423368740516, 'mean_word_length': 4.0, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 12, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '181512', 'license': 'Apache-2.0', 'n_tokens_mistral': 318, 'n_tokens_neox': 283, 'n_words': 53, 'path': 'examples/kendall.ipynb', 'repo_name': 'kmedian/korr', 'size': '659'}