{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.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":"604120ff-36df-5018-837a-8b5c578fe1a1","task_key":"default--v0~2e1~2e0~5fhf--604120ff-36df-5018-837a-8b5c578fe1a1","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 604120ff-36df-5018-837a-8b5c578fe1a1","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(data=None):\\n\",\"complete_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\n\\n\\ndef task_func(data=None):\\n    \\\"\\\"\\\"\\n    Pre-process a dataset by converting it to a Pandas DataFrame,\\n    replacing values less than 0.5 with zeros, and\\n    standardizing the data using StandardScaler.\\n\\n    Parameters:\\n    - data (numpy.ndarray, optional): A numpy array representing the dataset. If not provided, a random dataset\\n      of shape (100, 5) is generated.\\n\\n    Returns:\\n    - pandas.DataFrame: The preprocessed dataset. Original values less than 0.5 are replaced with zeros, and the\\n      entire dataset is standardized.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n    - sklearn.preprocessing.StandardScaler\\n\\n    Example:\\n    >>> np.random.seed(0)\\n    >>> dataset = np.random.rand(10, 5)\\n    >>> preprocessed_data = task_func(dataset)\\n    >>> preprocessed_data.head(2)\\n              0         1         2        3         4\\n    0  0.175481  1.062315  0.244316 -0.17039 -0.647463\\n    1  0.461851 -0.978767  1.052947  1.06408 -0.647463\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Pre-process a dataset by converting it to a Pandas DataFrame, replacing values less than 0.5 with zeros, and standardizing the data using StandardScaler.\\nThe function should output with:\\n    pandas.DataFrame: The preprocessed dataset. Original values less than 0.5 are replaced with zeros, and the\\n    entire dataset is standardized.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(data=None):\\n```\",\"libs\":\"['pandas', 'numpy', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}