{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"ds-1000","formal_name":"DS-1000","introduction":"DS-1000 builds 1,000 data-science problems from real StackOverflow questions across seven libraries including NumPy, Pandas and Matplotlib. The problems are perturbed so that recalling the original answer does not solve them.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://ds1000-code-gen.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"c258e334-8dee-519f-877e-0f219bd674d9","task_key":"default--test--863","task_revision_id":"1","upstream_id":"863","short_description":"I am using KMeans in sklearn on a data set which have more than 5000 samples.…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI am using KMeans in sklearn on a data set which have more than 5000 samples. And I want to get the 50 samples(not just index but full data) closest to \\\"p\\\" (e.g. p=2), a cluster center, as an output, here \\\"p\\\" means the p^th center.\\nAnyone can help me?\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.cluster import KMeans\\np, X = load_data()\\nassert type(X) == np.ndarray\\nkm = KMeans()\\n</code>\\nclosest_50_samples = ... # put solution in this variable\\nBEGIN SOLUTION\\n<code>\\n\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}