{"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":"2741339b-198d-5c63-8825-e24d50d7a30a","task_key":"default--test--482","task_revision_id":"1","upstream_id":"482","short_description":"Suppose I have a hypotetical function I'd like to approximate:","config":"default","split":"test","body":"{\"library\":\"Numpy\",\"prompt\":\"Problem:\\nSuppose I have a hypotetical function I'd like to approximate:\\ndef f(x):\\n    return a * x ** 2 + b * x + c\\nWhere a, b and c are the values I don't know.\\nAnd I have certain points where the function output is known, i.e.\\nx = [-1, 2, 5, 100]\\ny = [123, 456, 789, 1255]\\n(actually there are way more values)\\nI'd like to get a, b and c while minimizing the squared error .\\nWhat is the way to do that in Python? The result should be an array like [a, b, c], from highest order to lowest order.\\nThere should be existing solutions in numpy or anywhere like that.\\nA:\\n<code>\\nimport numpy as np\\nx = [-1, 2, 5, 100]\\ny = [123, 456, 789, 1255]\\n</code>\\nresult = ... # 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":[]}