{"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":"4bf94901-c5b5-530c-979c-e84ff9aa1eda","task_key":"default--v0~2e1~2e0~5fhf--4bf94901-c5b5-530c-979c-e84ff9aa1eda","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 4bf94901-c5b5-530c-979c-e84ff9aa1eda","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from collections import defaultdict\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\ndef task_func(data):\\n\",\"complete_prompt\":\"from collections import defaultdict\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(data):\\n    \\\"\\\"\\\"\\n    Calculate statistical measurements (mean and standard deviation) of the values associated with\\n    each key in a list of dictionaries, and visualize mean and standard deviation with bar charts.\\n\\n    Parameters:\\n    data (list): The list of dictionaries. Must not be empty. Each dictionary must have numeric values.\\n\\n    Returns:\\n    tuple:\\n        - dict: A dictionary with keys and their corresponding mean and standard deviation.\\n        - list: A list of matplotlib Axes objects for each key's visualization.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - collections.defaultdict\\n    \\n    Raises:\\n    - ValueError: If the input data is empty.\\n    - TypeError: If the input is not a list of dictionaries or if any value in the dictionaries is not numeric.\\n    \\n    Example:\\n    >>> stats, axes = task_func([{'cat': 1, 'dog': 3}, {'cat' : 2, 'dog': 5}, {'cat' : 3, 'dog': 7}])\\n    >>> stats\\n    {'cat': {'mean': 2.0, 'std': 0.816496580927726}, 'dog': {'mean': 5.0, 'std': 1.632993161855452}}\\n    >>> axes\\n    [<Axes: title={'center': 'Statistics of cat'}, ylabel='Value'>, <Axes: title={'center': 'Statistics of dog'}, ylabel='Value'>]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Calculate statistical measurements (mean and standard deviation) of the values associated with each key in a list of dictionaries, and visualize mean and standard deviation with bar charts.\\nThe function should raise the exception for: ValueError: If the input data is empty. TypeError: If the input is not a list of dictionaries or if any value in the dictionaries is not numeric.\\nThe function should output with:\\n    tuple:\\n    dict: A dictionary with keys and their corresponding mean and standard deviation.\\n    list: A list of matplotlib Axes objects for each key's visualization.\\nYou should write self-contained code starting with:\\n```\\nfrom collections import defaultdict\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\ndef task_func(data):\\n```\",\"libs\":\"['collections', 'numpy', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}