{"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":"c94395ca-4bca-5585-92c7-e255a88769d9","task_key":"default--test--374","task_revision_id":"1","upstream_id":"374","short_description":"What is the equivalent of R's ecdf(x)(x) function in Python, in either numpy or…","config":"default","split":"test","body":"{\"library\":\"Numpy\",\"prompt\":\"Problem:\\nWhat is the equivalent of R's ecdf(x)(x) function in Python, in either numpy or scipy? Is ecdf(x)(x) basically the same as:\\nimport numpy as np\\ndef ecdf(x):\\n  # normalize X to sum to 1\\n  x = x / np.sum(x)\\n  return np.cumsum(x)\\nor is something else required? \\nWhat I want to do is to apply the generated ECDF function to an eval array to gets corresponding values for elements in it.\\nA:\\n<code>\\nimport numpy as np\\ngrades = np.array((93.5,93,60.8,94.5,82,87.5,91.5,99.5,86,93.5,92.5,78,76,69,94.5,\\n          89.5,92.8,78,65.5,98,98.5,92.3,95.5,76,91,95,61))\\neval = np.array([88, 87, 62])\\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":[]}