{"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":"87c2c69b-4c1d-5344-b20e-d495d2c25309","task_key":"default--v0~2e1~2e0~5fhf--87c2c69b-4c1d-5344-b20e-d495d2c25309","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 87c2c69b-4c1d-5344-b20e-d495d2c25309","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport json\\ndef task_func(data: dict, output_path: str = \\\"./default_data_output.json\\\") -> str:\\n\",\"complete_prompt\":\"import pandas as pd\\nimport json\\n\\n\\ndef task_func(data: dict, output_path: str = \\\"./default_data_output.json\\\") -> str:\\n    \\\"\\\"\\\"\\n    Converts the given DataFrame to a dictionary, dropping the column named 'c'\\n    if it exists, and then saves it as a JSON file.\\n\\n    Parameters:\\n    - data (dict): The input data dictionary.\\n    - output_path (str, optional): The path where the JSON file should be saved. Default is './default_data_output.json'.\\n\\n    Returns:\\n    - str: Path where the JSON file was saved.\\n\\n    Requirements:\\n    - pandas\\n    - json\\n\\n    Example:\\n    >>> task_func({'a': [1,2], 'b': [3,4], 'c': [5,6]})\\n    './default_data_output.json'\\n    >>> print(json.load(open(task_func({'a': [1,2], 'b': [3,4], 'c': [5,6]})))\\n    {'a': {'0': 1, '1': 2}, 'b': {'0': 3, '1': 4}}\\n    >>> task_func({'a': [1,2], 'b': [3,4], 'c': [5,6]}, 'custom/path/results.json')\\n    'custom/path/results.json'\\n    >>> print(json.load(open(task_func({'a': [1,2], 'b': [3,4], 'c': [5,6]}, 'custom/path/results.json')))\\n    {'a': {'0': 1, '1': 2}, 'b': {'0': 3, '1': 4}}\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Converts the given DataFrame to a dictionary, dropping the column named 'c' if it exists, and then saves it as a JSON file.\\nThe function should output with:\\n    str: Path where the JSON file was saved.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport json\\ndef task_func(data: dict, output_path: str = \\\"./default_data_output.json\\\") -> str:\\n```\",\"libs\":\"['pandas', 'json']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}