{"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":"noindex","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[]}},"task_id":"a70129c5-da1c-5a67-bb69-c07eab3c593b","task_key":"default--v0~2e1~2e0~5fhf--a70129c5-da1c-5a67-bb69-c07eab3c593b","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf a70129c5-da1c-5a67-bb69-c07eab3c593b","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(data_dict, data_keys):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\n\\ndef task_func(data_dict, data_keys):\\n    \\\"\\\"\\\"\\n    Normalize data specified by keys in a dictionary using MinMax scaling and plot the results. This function is\\n    useful for preprocessing data for machine learning models where data scaling can impact performance.\\n\\n    Parameters:\\n    data_dict (dict): A dictionary where keys map to lists of numeric values.\\n    data_keys (list): Keys within the dictionary whose corresponding values are to be normalized.\\n\\n    Returns:\\n    tuple: A tuple containing a DataFrame of normalized values and a matplotlib Axes object representing a plot of the\\n    normalized data.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn\\n\\n    Raises:\\n    ValueError: If no keys in `data_keys` are found in `data_dict`.\\n\\n    Example:\\n    >>> data_dict = {'A': [1, 2, 3], 'B': [4, 5, 6]}\\n    >>> data_keys = ['A', 'B']\\n    >>> normalized_df, ax = task_func(data_dict, data_keys)\\n    >>> print(normalized_df.to_string(index=False))\\n      A   B\\n    0.0 0.0\\n    0.5 0.5\\n    1.0 1.0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Normalize data specified by keys in a dictionary using MinMax scaling and plot the results. This function is useful for preprocessing data for machine learning models where data scaling can impact performance.\\nThe function should raise the exception for: ValueError: If no keys in `data_keys` are found in `data_dict`.\\nThe function should output with:\\n    tuple: A tuple containing a DataFrame of normalized values and a matplotlib Axes object representing a plot of the\\n    normalized data.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(data_dict, data_keys):\\n```\",\"libs\":\"['pandas', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}