{"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":"fe05e07b-c4a7-50bf-8f54-0d85f22262b2","task_key":"default--v0~2e1~2e0~5fhf--fe05e07b-c4a7-50bf-8f54-0d85f22262b2","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf fe05e07b-c4a7-50bf-8f54-0d85f22262b2","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\ndef task_func(num_groups=5, data_size=5, labels=None):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\n\\n\\ndef task_func(num_groups=5, data_size=5, labels=None):\\n    \\\"\\\"\\\"\\n    Generate random data and visualize it with a stacked bar chart, saving the chart to a file.\\n    This function facilitates the exploration and sharing of data distribution across multiple categories.\\n\\n    Parameters:\\n    num_groups (int): Number of groups for which data is to be generated, defaulting to 5.\\n    data_size (int): Number of data points for each group, defaulting to 5.\\n    labels (list of str, optional): Labels for the groups. If None, default labels 'Group1', 'Group2', ...,\\n    'GroupN' are generated.\\n\\n    Returns:\\n    tuple: A tuple containing:\\n        - matplotlib.figure.Figure: The Figure object containing the stacked bar chart.\\n        - pandas.DataFrame: The DataFrame with randomly generated data.\\n        - str: The filename where the plot is saved ('test_plot.png').\\n\\n    Requirements:\\n    - pandas\\n    - matplotlib\\n    - numpy\\n\\n    Example:\\n    >>> np.random.seed(0)\\n    >>> fig, data, plot_filename = task_func(3, 3, ['A', 'B', 'C'])\\n    >>> print(data)\\n              A         B         C\\n    0  0.548814  0.715189  0.602763\\n    1  0.544883  0.423655  0.645894\\n    2  0.437587  0.891773  0.963663\\n    >>> print(plot_filename)\\n    test_plot.png\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate random data and visualize it with a stacked bar chart, saving the chart to a file. This function facilitates the exploration and sharing of data distribution across multiple categories.\\nThe function should output with:\\n    tuple: A tuple containing:\\n    matplotlib.figure.Figure: The Figure object containing the stacked bar chart.\\n    pandas.DataFrame: The DataFrame with randomly generated data.\\n    str: The filename where the plot is saved ('test_plot.png').\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\ndef task_func(num_groups=5, data_size=5, labels=None):\\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":"noindex","subproblems":[],"grids":[]}