{"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":"0221d418-5b32-5159-aeea-ceea1710b5ad","task_key":"default--v0~2e1~2e0~5fhf--0221d418-5b32-5159-aeea-ceea1710b5ad","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 0221d418-5b32-5159-aeea-ceea1710b5ad","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom statsmodels.tsa.stattools import adfuller\\ndef task_func(df: pd.DataFrame, column_a: str, column_b: str, column_c: str) -> bool:\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom statsmodels.tsa.stattools import adfuller\\n\\n\\ndef task_func(df: pd.DataFrame, column_a: str, column_b: str, column_c: str) -> bool:\\n    \\\"\\\"\\\"\\n    Determines if a specific subset of data is stationary by filtering rows where column_b bigger than 50 and column_c equal to 900. \\n    Data is considered to be stationary if the p_value returned by the Augmented Dickey-Fuller test is smaller than 0.05.\\n\\n    If column_a is empty after filtering or if its values are constant, True\\n    is returned.\\n    \\n    Parameters:\\n        df (pd.DataFrame): A DataFrame containing the data.\\n        column_a (str): The name of the column to test for stationarity.\\n        column_b (str): The name of the column used for filtering based on its value being greater than 50.\\n        column_c (str): The name of the column used for filtering based on its value being equal to 900.\\n    \\n    Returns:\\n        bool: True if the data in column_a (after filtering based on column_b and column_c) is stationary, False otherwise.\\n    \\n    Requirements:\\n        pandas\\n        statsmodels: for using the adfuller test\\n\\n    Example:\\n    >>> df = pd.DataFrame({\\n    ...      'A': [1, 2, 3, 4, 5, 6],\\n    ...      'B': [60, 70, 80, 90, 100, 110],\\n    ...      'C': [900, 900, 900, 900, 900, 900]\\n    ... })\\n    >>> task_func(df, 'A', 'B', 'C')\\n    False\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Determines if a specific subset of data is stationary by filtering rows where column_b bigger than 50 and column_c equal to 900. Data is considered to be stationary if the p_value returned by the Augmented Dickey-Fuller test is smaller than 0.05. If column_a is empty after filtering or if its values are constant, True is returned.\\nThe function should output with:\\n    bool: True if the data in column_a (after filtering based on column_b and column_c) is stationary, False otherwise.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom statsmodels.tsa.stattools import adfuller\\ndef task_func(df: pd.DataFrame, column_a: str, column_b: str, column_c: str) -> bool:\\n```\",\"libs\":\"['pandas', 'statsmodels']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}