{"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":"7b827b5f-6c4b-540a-837a-1abec52e24d8","task_key":"default--v0~2e1~2e0~5fhf--7b827b5f-6c4b-540a-837a-1abec52e24d8","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 7b827b5f-6c4b-540a-837a-1abec52e24d8","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport random\\nfrom sklearn.preprocessing import StandardScaler\\n# Constants\\nN_DATA_POINTS = 5000\\nMIN_VALUE = 0.0\\nMAX_VALUE = 10.0\\ndef task_func(n_data_points=5000, min_value=0.0, max_value=10.0):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport random\\nfrom sklearn.preprocessing import StandardScaler\\n\\n# Constants\\nN_DATA_POINTS = 5000\\nMIN_VALUE = 0.0\\nMAX_VALUE = 10.0\\n\\ndef task_func(n_data_points=5000, min_value=0.0, max_value=10.0):\\n    \\\"\\\"\\\"\\n    Generate a random dataset of floating point numbers, truncate each value to 3 decimal places and normalize the data using standard scaling (mean = 0, std = 1).\\n    \\n    Parameters:\\n    n_data_points (int): Number of data points to generate. Default is 5000.\\n    min_value (float): Minimum value range for data points. Default is 0.0.\\n    max_value (float): Maximum value range for data points. Default is 10.0.\\n    \\n    Returns:\\n    DataFrame: A pandas DataFrame with the normalized data.\\n    \\n    Raises:\\n    If max_value is less than min_value, a ValueError is raised.\\n    \\n    Note:\\n    - The function use \\\"Normalized Value\\\" for the column name in the DataFrame that being returned.\\n\\n    Requirements:\\n    - pandas\\n    - random\\n    - sklearn.preprocessing.StandardScaler\\n\\n    Example:\\n    >>> random.seed(0)\\n    >>> normalized_data = task_func(5000, 5, 5)\\n    >>> print(normalized_data['Normalized Value'][0])\\n    0.0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a random dataset of floating point numbers, truncate each value to 3 decimal places and normalize the data using standard scaling (mean = 0, std = 1).\\nNote that: The function use \\\"Normalized Value\\\" for the column name in the DataFrame that being returned.\\nThe function should raise the exception for: If max_value is less than min_value, a ValueError is raised.\\nThe function should output with:\\n    DataFrame: A pandas DataFrame with the normalized data.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport random\\nfrom sklearn.preprocessing import StandardScaler\\n# Constants\\nN_DATA_POINTS = 5000\\nMIN_VALUE = 0.0\\nMAX_VALUE = 10.0\\ndef task_func(n_data_points=5000, min_value=0.0, max_value=10.0):\\n```\",\"libs\":\"['pandas', 'random', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}