{"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":"687c0b80-7bcd-5965-b693-2510284de494","task_key":"default--test--732","task_revision_id":"1","upstream_id":"732","short_description":"I have two csr_matrix, c1 and c2.","config":"default","split":"test","body":"{\"library\":\"Scipy\",\"prompt\":\"Problem:\\nI have two csr_matrix, c1 and c2.\\n\\nI want a new sparse matrix Feature = [c1, c2], that is, to stack c1 and c2 horizontally to get a new sparse matrix.\\n\\nTo make use of sparse matrix's memory efficiency, I don't want results as dense arrays.\\n\\nBut if I directly concatenate them this way, there's an error that says the matrix Feature is a list.\\n\\nAnd if I try this: Feature = csr_matrix(Feature) It gives the error:\\n\\nTraceback (most recent call last):\\n  File \\\"yelpfilter.py\\\", line 91, in <module>\\n    Feature = csr_matrix(Feature)\\n  File \\\"c:\\\\python27\\\\lib\\\\site-packages\\\\scipy\\\\sparse\\\\compressed.py\\\", line 66, in __init__\\n    self._set_self( self.__class__(coo_matrix(arg1, dtype=dtype)) )\\n  File \\\"c:\\\\python27\\\\lib\\\\site-packages\\\\scipy\\\\sparse\\\\coo.py\\\", line 185, in __init__\\n    self.row, self.col = M.nonzero()\\nTypeError: __nonzero__ should return bool or int, returned numpy.bool_\\n\\nAny help would be appreciated!\\n\\nA:\\n<code>\\nfrom scipy import sparse\\nc1 = sparse.csr_matrix([[0, 0, 1, 0], [2, 0, 0, 0], [0, 0, 0, 0]])\\nc2 = sparse.csr_matrix([[0, 3, 4, 0], [0, 0, 0, 5], [6, 7, 0, 8]])\\n</code>\\nFeature = ... # 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":[]}