{"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":"6816a290-885c-56d8-ada1-a236ea956584","task_key":"default--test--414","task_revision_id":"1","upstream_id":"414","short_description":"I have a numpy array which contains time series data. I want to bin that array…","config":"default","split":"test","body":"{\"library\":\"Numpy\",\"prompt\":\"Problem:\\nI have a numpy array which contains time series data. I want to bin that array into equal partitions of a given length (it is fine to drop the last partition if it is not the same size) and then calculate the mean of each of those bins.\\nI suspect there is numpy, scipy, or pandas functionality to do this.\\nexample:\\ndata = [4,2,5,6,7,5,4,3,5,7]\\nfor a bin size of 2:\\nbin_data = [(4,2),(5,6),(7,5),(4,3),(5,7)]\\nbin_data_mean = [3,5.5,6,3.5,6]\\nfor a bin size of 3:\\nbin_data = [(4,2,5),(6,7,5),(4,3,5)]\\nbin_data_mean = [3.67,6,4]\\nA:\\n<code>\\nimport numpy as np\\ndata = np.array([4, 2, 5, 6, 7, 5, 4, 3, 5, 7])\\nbin_size = 3\\n</code>\\nbin_data_mean = ... # 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":[]}