{"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":"d737b4cf-df7c-5d9d-b4e5-34d7305c34df","task_key":"default--v0~2e1~2e0~5fhf--d737b4cf-df7c-5d9d-b4e5-34d7305c34df","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf d737b4cf-df7c-5d9d-b4e5-34d7305c34df","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import os\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\ndef task_func(file_path: str, plot_path: str) -> (float, float, str):\\n\",\"complete_prompt\":\"import os\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\n\\n\\ndef task_func(file_path: str, plot_path: str) -> (float, float, str):\\n    \\\"\\\"\\\"\\n    Processes a CSV file at the given path by reading its contents, cleaning the data,\\n    performing statistical analysis, and generating a plot, which is saved to the specified path.\\n\\n    Sets the title of the plot to \\\"Data Visualization\\\".\\n    Labels the x-axis as \\\"Index\\\" and the y-axis as \\\"Value\\\".\\n    Saves the generated plot to the file path specified in 'plot_path'.\\n\\n    Parameters:\\n    - file_path (str): Path to the CSV input file.\\n    - plot_path (str): Path where the plot will be saved.\\n\\n    Returns:\\n    - tuple: A tuple containing the following elements:\\n        - Mean (float): The average value of the data. Returns NaN if data is empty or non-numeric.\\n        - Median (float): The middle value of the data when sorted. Returns NaN if data is empty or non-numeric.\\n        - Plot Path (str): The path where the plot is saved.\\n\\n    Raises:\\n    - FileNotFoundError: If the CSV file at 'file_path' does not exist.\\n\\n    Requirements:\\n    - os\\n    - pandas\\n    - matplotlib\\n    - numpy\\n\\n    Example:\\n    >>> task_func(\\\"sample_data.csv\\\", \\\"output_plot.png\\\")\\n    (25.5, 23.0, \\\"output_plot.png\\\")\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Processes a CSV file at the given path by reading its contents, cleaning the data, performing statistical analysis, and generating a plot, which is saved to the specified path. Sets the title of the plot to \\\"Data Visualization\\\". Labels the x-axis as \\\"Index\\\" and the y-axis as \\\"Value\\\". Saves the generated plot to the file path specified in 'plot_path'.\\nThe function should raise the exception for: FileNotFoundError: If the CSV file at 'file_path' does not exist.\\nThe function should output with:\\n    tuple: A tuple containing the following elements:\\n    Mean (float): The average value of the data. Returns NaN if data is empty or non-numeric.\\n    Median (float): The middle value of the data when sorted. Returns NaN if data is empty or non-numeric.\\n    Plot Path (str): The path where the plot is saved.\\nYou should write self-contained code starting with:\\n```\\nimport os\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\ndef task_func(file_path: str, plot_path: str) -> (float, float, str):\\n```\",\"libs\":\"['pandas', 'numpy', 'matplotlib', 'os']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}