{"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":"noindex","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[]}},"task_id":"96f70580-fb19-5edf-95c3-9b9c7e30a222","task_key":"default--test--44","task_revision_id":"2","upstream_id":"44","short_description":"I have a DataFrame like :","config":"default","split":"test","body":"{\"prompt\":\"Problem:\\nI have a DataFrame like :\\n     0    1    2\\n0  0.0  1.0  2.0\\n1  NaN  1.0  2.0\\n2  NaN  NaN  2.0\\n\\nWhat I want to get is \\nOut[116]: \\n     0    1    2\\n0  0.0  1.0  2.0\\n1  1.0  2.0  NaN\\n2  2.0  NaN  NaN\\n\\nThis is my approach as of now.\\ndf.apply(lambda x : (x[x.notnull()].values.tolist()+x[x.isnull()].values.tolist()),1)\\nOut[117]: \\n     0    1    2\\n0  0.0  1.0  2.0\\n1  1.0  2.0  NaN\\n2  2.0  NaN  NaN\\n\\nIs there any efficient way to achieve this ? apply Here is way to slow .\\nThank you for your assistant!:) \\n\\nMy real data size\\ndf.shape\\nOut[117]: (54812040, 1522)\\n\\nA:\\n<code>\\nimport pandas as pd\\nimport numpy as np\\n\\ndf = pd.DataFrame([[3,1,2],[np.nan,1,2],[np.nan,np.nan,2]],columns=['0','1','2'])\\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":"noindex","subproblems":[],"grids":[]}