{"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":"e8a4dd9b-238a-5c8e-9069-9a59b262e5ab","task_key":"default--v0~2e1~2e0~5fhf--e8a4dd9b-238a-5c8e-9069-9a59b262e5ab","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf e8a4dd9b-238a-5c8e-9069-9a59b262e5ab","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\ndef task_func(dictionary, key, value, n=100, bins=30, seed=0):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\n\\n\\ndef task_func(dictionary, key, value, n=100, bins=30, seed=0):\\n    \\\"\\\"\\\"\\n    Updates the provided dictionary with a specified key-value pair and generates a random dataset of size 'n' \\n    following a normal distribution. The mean and standard deviation of the distribution are set to the value \\n    associated with the given key. Additionally, it returns a histogram of the generated dataset.\\n    \\n    Parameters:\\n    - dictionary (dict): The dictionary to be updated.\\n    - key (str): The key to be added to the dictionary.\\n    - value (str): The value to be associated with the provided key.\\n    - n (int, optional): The size of the random dataset to be generated. Default is 100.\\n    - bins (int, optional): The number of bins for the histogram. Default is 30.\\n    - seed (int, optional): The seed for the random number generator. Default is 0.\\n    \\n    Returns:\\n    - tuple: Updated dictionary and the generated dataset as a pandas Series along with the histogram plot.\\n    \\n    Requirements:\\n    - numpy\\n    - matplotlib\\n    - pandas\\n\\n    Raises:\\n    - ValueError: If the provided value is not a number.\\n    \\n    Example:\\n    >>> d, data, ax = task_func({'key1': 10, 'key2': 20}, 'newkey', '25', n=500)\\n    >>> d\\n    {'key1': 10, 'key2': 20, 'newkey': '25'}\\n    >>> len(data)\\n    500\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Updates the provided dictionary with a specified key-value pair and generates a random dataset of size 'n' following a normal distribution. The mean and standard deviation of the distribution are set to the value associated with the given key. Additionally, it returns a histogram of the generated dataset.\\nThe function should raise the exception for: ValueError: If the provided value is not a number.\\nThe function should output with:\\n    tuple: Updated dictionary and the generated dataset as a pandas Series along with the histogram plot.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\ndef task_func(dictionary, key, value, n=100, bins=30, seed=0):\\n```\",\"libs\":\"['pandas', '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":[]}