{"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":"a269663c-08de-5f8c-8153-b53c5299dddb","task_key":"default--v0~2e1~2e0~5fhf--a269663c-08de-5f8c-8153-b53c5299dddb","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf a269663c-08de-5f8c-8153-b53c5299dddb","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom datetime import datetime\\n# Constants\\nDATE_FORMAT = '%Y-%m-%d %H:%M:%S'\\ndef task_func(result):\\n\",\"complete_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom datetime import datetime\\n\\n# Constants\\nDATE_FORMAT = '%Y-%m-%d %H:%M:%S'\\n\\ndef task_func(result):\\n    \\\"\\\"\\\"\\n    Calculate the mean, median, min, max, and standard deviation of the \\\"from_user\\\" values in \\\"result\\\" \\n    and add the current date and time in the format YYYY-mm-dd HHL:MM:SS to the summary.\\n    The global constant DATE_FORMAT is used to transform the currnet date and time into this format.\\n\\n\\n    Parameters:\\n    result (list of dict): A list of dictionaries containing the key \\\"from_user\\\" whose numeric values are to be analyzed.\\n\\n    Returns:\\n    Series: A pandas Series with the statistical summary, including 'mean', 'median', 'min', 'max', 'std', and 'current_time'.\\n            If the input contains no \\\"from_user\\\" values all statistical values are set to np.nan\\n\\n    Data Structures:\\n    - Uses numpy arrays for efficient statistical computations.\\n\\n    Raises:\\n    - ValueError: If the \\\"from_user\\\" values are not numeric.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n    - datetime\\n\\n    Example:\\n    >>> result = [{\\\"hi\\\": 7, \\\"bye\\\": 4, \\\"from_user\\\": 0}, {\\\"from_user\\\": 0}, {\\\"from_user\\\": 1}]\\n    >>> stats = task_func(result)\\n    >>> print(stats['mean'], stats['median'], stats['min'], stats['max'], stats['std'])\\n    0.3333333333333333 0.0 0 1 0.4714045207910317\\n    >>> result = [{\\\"test\\\": 7, \\\"hallo\\\": 4, \\\"from_user\\\": 1.3},\\n    ...           {\\\"from_user\\\": 2},\\n    ...           {\\\"from_user\\\": 4.6},\\n    ...           {\\\"from_user\\\": -2.3, \\\"b\\\": 1},\\n    ...           {\\\"a\\\": \\\"test\\\", \\\"from_user\\\": 12.12},\\n    ...          ]\\n    >>> summary = task_func(result)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Calculate the mean, median, min, max, and standard deviation of the \\\"from_user\\\" values in \\\"result\\\" and add the current date and time in the format YYYY-mm-dd HHL:MM:SS to the summary. The global constant DATE_FORMAT is used to transform the currnet date and time into this format. Data Structures: - Uses numpy arrays for efficient statistical computations.\\nThe function should raise the exception for: ValueError: If the \\\"from_user\\\" values are not numeric.\\nThe function should output with:\\n    Series: A pandas Series with the statistical summary, including 'mean', 'median', 'min', 'max', 'std', and 'current_time'.\\n    If the input contains no \\\"from_user\\\" values all statistical values are set to np.nan\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport pandas as pd\\nfrom datetime import datetime\\n# Constants\\nDATE_FORMAT = '%Y-%m-%d %H:%M:%S'\\ndef task_func(result):\\n```\",\"libs\":\"['pandas', 'datetime', 'numpy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}