{"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":"268cb2f7-10a7-5034-80c1-d2a6a6722bc1","task_key":"default--test--865","task_revision_id":"1","upstream_id":"865","short_description":"I have fitted a k-means algorithm on 5000+ samples using the python scikit-learn…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI have fitted a k-means algorithm on 5000+ samples using the python scikit-learn library. I want to have the 50 samples closest (data, not just index) to a cluster center \\\"p\\\" (e.g. p=2) as an output, here \\\"p\\\" means the p^th center. How do I perform this task?\\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()\\ndef get_samples(p, X, km):\\n    # return the solution in this function\\n    # samples = get_samples(p, X, km)\\n    ### BEGIN SOLUTION\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}