{"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":"e592ef42-7ee6-5ea5-9be6-b8be3771f847","task_key":"default--v0~2e1~2e0~5fhf--e592ef42-7ee6-5ea5-9be6-b8be3771f847","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf e592ef42-7ee6-5ea5-9be6-b8be3771f847","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\ndef task_func(start_date: str, periods: int, freq: str, random_seed: int = 0) -> (pd.DataFrame, plt.Axes):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(start_date: str, periods: int, freq: str, random_seed: int = 0) -> (pd.DataFrame, plt.Axes):\\n    \\\"\\\"\\\"\\n    Generates and plots a sales forecast starting from a given date, for a specified number of periods and frequency.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n    - matplotlib.pyplot\\n    \\n    Parameters:\\n    - start_date (str): Start date for the forecast in 'YYYY-MM-DD' format.\\n    - periods (int): Number of periods to forecast.\\n    - freq (str): Frequency of the forecast (e.g., 'WOM-2FRI' for the second Friday of each month, 'M' for monthly).\\n    - random_seed (int, optional): Seed for the random number generator to ensure reproducibility.\\n\\n    Returns:\\n    - A tuple containing:\\n        1. A DataFrame with columns ['Date', 'Sales'], where 'Date' is the forecast date and 'Sales' are the forecasted sales.\\n        2. A matplotlib Axes object for the sales forecast plot.\\n\\n    Examples:\\n    >>> df, ax = task_func('2021-01-01', 5, 'WOM-2FRI')\\n    >>> print(df)\\n                Sales\\n    Date             \\n    2021-01-08    272\\n    2021-02-12    147\\n    2021-03-12    217\\n    2021-04-09    292\\n    2021-05-14    423\\n    >>> df, ax = task_func('2022-02-01', 3, 'M', random_seed=42)\\n    >>> print(df)\\n                Sales\\n    Date             \\n    2022-02-28    202\\n    2022-03-31    448\\n    2022-04-30    370\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generates and plots a sales forecast starting from a given date, for a specified number of periods and frequency.\\nThe function should output with:\\n    A tuple containing:\\n    1. A DataFrame with columns ['Date', 'Sales'], where 'Date' is the forecast date and 'Sales' are the forecasted sales.\\n    2. A matplotlib Axes object for the sales forecast plot.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\ndef task_func(start_date: str, periods: int, freq: str, random_seed: int = 0) -> (pd.DataFrame, plt.Axes):\\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":"index","subproblems":[],"grids":[]}