{"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":"8f5bab76-5dab-5c94-a167-7875bc69a960","task_key":"default--test--824","task_revision_id":"1","upstream_id":"824","short_description":"I would like to break down a pandas column, which is the last column, consisting…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI would like to break down a pandas column, which is the last column, consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence).\\n\\nFor example, taking dataframe df\\n\\nCol1   Col2         Col3\\n C      33     [Apple, Orange, Banana]\\n A      2.5    [Apple, Grape]\\n B      42     [Banana]\\nI would like to convert this to:\\n\\ndf\\n\\nCol1   Col2   Apple   Orange   Banana   Grape\\n C      33     1        1        1       0\\n A      2.5    1        0        0       1\\n B      42     0        0        1       0\\nSimilarly, if the original df has four columns, then should do the operation to the 4th one.\\nHow can I use pandas/sklearn to achieve this?\\n\\nA:\\n\\n<code>\\nimport pandas as pd\\nimport numpy as np\\nimport sklearn\\ndf = load_data()\\n</code>\\ndf_out = ... # 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":[]}