{"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":"45f26b8e-9b91-565d-b76e-9cd3eb21fc80","task_key":"default--v0~2e1~2e0~5fhf--45f26b8e-9b91-565d-b76e-9cd3eb21fc80","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 45f26b8e-9b91-565d-b76e-9cd3eb21fc80","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nimport seaborn as sns\\nPRODUCTS = ['Product' + str(i) for i in range(1, 6)]\\nMONTHS = ['Month' + str(i) for i in range(1, 13)]\\ndef task_func():\\n\",\"complete_prompt\":\"import matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nimport seaborn as sns\\n\\nPRODUCTS = ['Product' + str(i) for i in range(1, 6)]\\nMONTHS = ['Month' + str(i) for i in range(1, 13)]\\n\\n\\ndef task_func():\\n    \\\"\\\"\\\"\\n    Generate a DataFrame representing monthly sales of products and visualize the total sales.\\n\\n    The function creates a DataFrame where each row represents a month, each column represents a product,\\n    and cell values represent sales figures. It then plots the total sales per product across all months\\n    using both a line plot and a heatmap for visualization.\\n\\n    Returns:\\n    - pd.DataFrame: A DataFrame with randomly generated sales figures for each product over 12 months.\\n\\n    The function also displays:\\n    - A line plot showing the total sales per product.\\n    - A heatmap visualizing sales figures across products and months.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n    - matplotlib.pyplot\\n    - seaborn\\n\\n    Example:\\n    >>> df = task_func()\\n    >>> df.shape\\n    (12, 5)\\n    >>> all(df.columns == PRODUCTS)\\n    True\\n    >>> all(df.index == MONTHS)\\n    True\\n    >>> (df.values >= 100).all() and (df.values <= 1000).all()\\n    True\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a DataFrame representing monthly sales of products and visualize the total sales. The function creates a DataFrame where each row represents a month, each column represents a product, and cell values represent sales figures. It then plots the total sales per product across all months using both a line plot and a heatmap for visualization. The function also displays: - A line plot showing the total sales per product. - A heatmap visualizing sales figures across products and months.\\nThe function should output with:\\n    pd.DataFrame: A DataFrame with randomly generated sales figures for each product over 12 months.\\nYou should write self-contained code starting with:\\n```\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nimport seaborn as sns\\nPRODUCTS = ['Product' + str(i) for i in range(1, 6)]\\nMONTHS = ['Month' + str(i) for i in range(1, 13)]\\ndef task_func():\\n```\",\"libs\":\"['pandas', 'numpy', 'matplotlib', 'seaborn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}