{"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":"0e43b962-26e8-5ece-bed8-2825102d5a60","task_key":"default--test--875","task_revision_id":"1","upstream_id":"875","short_description":"Given a list of variant length features:","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nGiven a list of variant length features:\\n\\nfeatures = [\\n    ['f1', 'f2', 'f3'],\\n    ['f2', 'f4', 'f5', 'f6'],\\n    ['f1', 'f2']\\n]\\nwhere each sample has variant number of features and the feature dtype is str and already one hot.\\n\\nIn order to use feature selection utilities of sklearn, I have to convert the features to a 2D-array which looks like:\\n\\n    f1  f2  f3  f4  f5  f6\\ns1   1   1   1   0   0   0\\ns2   0   1   0   1   1   1\\ns3   1   1   0   0   0   0\\nHow could I achieve it via sklearn or numpy?\\n\\nA:\\n\\n<code>\\nimport pandas as pd\\nimport numpy as np\\nimport sklearn\\nfeatures = load_data()\\n</code>\\nnew_features = ... # 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":[]}