{"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":"fbbf32ac-6ffe-59fa-bfde-505c90e09c0a","task_key":"default--test--396","task_revision_id":"1","upstream_id":"396","short_description":"I could not find a built-in function in Python to generate a log uniform…","config":"default","split":"test","body":"{\"library\":\"Numpy\",\"prompt\":\"Problem:\\nI could not find a built-in function in Python to generate a log uniform distribution given a min and max value (the R equivalent is here), something like: loguni[n, exp(min), exp(max), base] that returns n log uniformly distributed in the range exp(min) and exp(max).\\nThe closest I found though was numpy.random.uniform.\\nThat is, given range of logx, I want to get samples of given size (n) that suit log-uniform distribution. \\nAny help would be appreciated!\\nA:\\n<code>\\nimport numpy as np\\n\\nmin = 0\\nmax = 1\\nn = 10000\\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":[]}