{"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":"cea2724c-6bc3-56be-b5ba-25a870e4bd4d","task_key":"default--test--436","task_revision_id":"1","upstream_id":"436","short_description":"Input example:","config":"default","split":"test","body":"{\"library\":\"Numpy\",\"prompt\":\"Problem:\\nInput example:\\nI have a numpy array, e.g.\\na=np.array([[0,1], [2, 1], [4, 8]])\\nDesired output:\\nI would like to produce a mask array with the max value along a given axis, in my case axis 1, being True and all others being False. e.g. in this case\\nmask = np.array([[False, True], [True, False], [False, True]])\\nAttempt:\\nI have tried approaches using np.amax but this returns the max values in a flattened list:\\n>>> np.amax(a, axis=1)\\narray([1, 2, 8])\\nand np.argmax similarly returns the indices of the max values along that axis.\\n>>> np.argmax(a, axis=1)\\narray([1, 0, 1])\\nI could iterate over this in some way but once these arrays become bigger I want the solution to remain something native in numpy.\\nA:\\n<code>\\nimport numpy as np\\na = np.array([[0, 1], [2, 1], [4, 8]])\\n</code>\\nmask = ... # 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":[]}