{"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":"f41e7183-f489-5586-a1cb-d5337bb466ab","task_key":"default--test--855","task_revision_id":"1","upstream_id":"855","short_description":"I have a pandas DataFrame data","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI have a pandas DataFrame data\\nit has about 12k rows and more than 500 columns, each column has its unique name\\nHowever, when I used sklearn preprocessing, I found the result lose the information about the columns\\nHere's the code\\n\\nfrom sklearn import preprocessing\\npreprocessing.scale(data)\\noutputs a numpy array.\\n\\nSo my question is, how to apply preprocessing.scale to DataFrames, and don't lose the information(index, columns)?\\n\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn import preprocessing\\ndata = load_data()\\n</code>\\ndf_out = ... # 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":[]}