{"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":"c8892ed1-fa10-5118-9cfb-81eb690972c3","task_key":"default--v0~2e1~2e0~5fhf--c8892ed1-fa10-5118-9cfb-81eb690972c3","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf c8892ed1-fa10-5118-9cfb-81eb690972c3","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nfrom scipy import stats\\nfrom sklearn.preprocessing import MinMaxScaler\\nimport matplotlib.pyplot as plt\\ndef task_func(data_dict):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom scipy import stats\\nfrom sklearn.preprocessing import MinMaxScaler\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(data_dict):\\n    \\\"\\\"\\\"\\n    Performs the following operations on the input dictionary 'data_dict':\\n    1. Adds a key \\\"a\\\" with a value of 1.\\n    2. Conducts statistical analysis on its values (mean, median, mode), by rounding the mean to 2 decimal places.\\n    3. Normalizes the values using MinMaxScaler to a range of (0, 1).\\n    4. Plots a histogram of the normalized values, with the title \\\"Histogram of Normalized Values\\\", and x labels \\\"Value\\\" and y labels \\\"Frequency\\\".\\n    \\n    Parameters:\\n    data_dict (dict): The dictionary to be processed, containing numerical values.\\n    \\n    Returns:\\n    tuple: A tuple containing:\\n        - dict: The processed dictionary with key \\\"a\\\" added.\\n        - dict: A dictionary containing statistical properties (mean, median, mode).\\n        - matplotlib.axes.Axes: The histogram plot of normalized values.\\n    \\n    Requirements:\\n    - numpy\\n    - scipy\\n    - sklearn.preprocessing\\n    - matplotlib.pyplot\\n    \\n    Example:\\n    >>> data, stats, plot = task_func({'key': 5, 'another_key': 10})\\n    >>> data\\n    {'key': 5, 'another_key': 10, 'a': 1}\\n    >>> stats\\n    {'mean': 5.33, 'median': 5.0, 'mode': array([1])}\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Performs the following operations on the input dictionary 'data_dict': 1. Adds a key \\\"a\\\" with a value of 1. 2. Conducts statistical analysis on its values (mean, median, mode), by rounding the mean to 2 decimal places. 3. Normalizes the values using MinMaxScaler to a range of (0, 1). 4. Plots a histogram of the normalized values, with the title \\\"Histogram of Normalized Values\\\", and x labels \\\"Value\\\" and y labels \\\"Frequency\\\".\\nThe function should output with:\\n    tuple: A tuple containing:\\n    dict: The processed dictionary with key \\\"a\\\" added.\\n    dict: A dictionary containing statistical properties (mean, median, mode).\\n    matplotlib.axes.Axes: The histogram plot of normalized values.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nfrom scipy import stats\\nfrom sklearn.preprocessing import MinMaxScaler\\nimport matplotlib.pyplot as plt\\ndef task_func(data_dict):\\n```\",\"libs\":\"['numpy', 'matplotlib', 'scipy', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}