{"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":"dacdab86-2be8-571e-9bc0-d8fa17feb8e9","task_key":"default--test--718","task_revision_id":"1","upstream_id":"718","short_description":"How does one convert a list of Z-scores from the Z-distribution (standard normal…","config":"default","split":"test","body":"{\"library\":\"Scipy\",\"prompt\":\"Problem:\\nHow does one convert a list of Z-scores from the Z-distribution (standard normal distribution, Gaussian distribution) to left-tailed p-values? Original data is sampled from X ~ N(mu, sigma). I have yet to find the magical function in Scipy's stats module to do this, but one must be there.\\nA:\\n<code>\\nimport scipy.stats\\nimport numpy as np\\nz_scores = [-3, -2, 0, 2, 2.5]\\nmu = 3\\nsigma = 4\\n</code>\\np_values = ... # 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":[]}