{"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":"8abf9b67-9147-5644-902e-1edf5a92fea2","task_key":"default--v0~2e1~2e0~5fhf--8abf9b67-9147-5644-902e-1edf5a92fea2","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 8abf9b67-9147-5644-902e-1edf5a92fea2","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport seaborn as sns\\n# Constants\\nCOLUMNS = ['col1', 'col2', 'col3']\\ndef task_func(data):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport seaborn as sns\\n\\n# Constants\\nCOLUMNS = ['col1', 'col2', 'col3']\\n\\ndef task_func(data):\\n    \\\"\\\"\\\"\\n    You are given a list of elements. Each element of the list is a list of 3 values. Use this list of elements to build a dataframe with 3 columns 'col1', 'col2' and 'col3' and create a distribution of chart of the different values of \\\"col3\\\" grouped by \\\"col1\\\" and \\\"col2\\\" using seaborn.\\n\\n    The function's logic is as follows:\\n    1. Build a pandas DataFrame by using list of elements. Make sure to name the columns as 'col1', 'col2' and 'col3', the constant COLUMNS is provided for this purpose.\\n    2. Create a new dataframe by grouping the values in the column 'col3' by ['col1', 'col2'].\\n    3. Reset the index of the newly created dataframe. This dataframe is the first element of the output tuple.\\n    4. Create a distribution plot of the 'col3' column of the previous dataframe using seaborn. This plot is the second and last element of the output tuple.\\n        - The xlabel (label for the x-axis) is set to the 'col3'.\\n\\n    Parameters:\\n    data (list): The DataFrame to be visualized.\\n\\n    Returns:\\n    tuple:\\n        pandas.DataFrame: The DataFrame of the analyzed data.\\n        plt.Axes: The seaborn plot object.\\n\\n    Requirements:\\n    - pandas\\n    - seaborn\\n\\n    Example:\\n    >>> data = [[1, 1, 1], [1, 1, 1], [1, 1, 2], [1, 2, 3], [1, 2, 3], [1, 2, 3], [2, 1, 1], [2, 1, 2], [2, 1, 3], [2, 2, 3], [2, 2, 3], [2, 2, 3]]\\n    >>> analyzed_df, plot = task_func(data)\\n    >>> print(analyzed_df)\\n       col1  col2  col3\\n    0     1     1     2\\n    1     1     2     1\\n    2     2     1     3\\n    3     2     2     1\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"You are given a list of elements. Each element of the list is a list of 3 values. Use this list of elements to build a dataframe with 3 columns 'col1', 'col2' and 'col3' and create a distribution of chart of the different values of \\\"col3\\\" grouped by \\\"col1\\\" and \\\"col2\\\" using seaborn. The function's logic is as follows: 1. Build a pandas DataFrame by using list of elements. Make sure to name the columns as 'col1', 'col2' and 'col3', the constant COLUMNS is provided for this purpose. 2. Create a new dataframe by grouping the values in the column 'col3' by ['col1', 'col2']. 3. Reset the index of the newly created dataframe. This dataframe is the first element of the output tuple. 4. Create a distribution plot of the 'col3' column of the previous dataframe using seaborn. This plot is the second and last element of the output tuple. - The xlabel (label for the x-axis) is set to the 'col3'.\\nThe function should output with:\\n    tuple:\\n    pandas.DataFrame: The DataFrame of the analyzed data.\\n    plt.Axes: The seaborn plot object.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport seaborn as sns\\n# Constants\\nCOLUMNS = ['col1', 'col2', 'col3']\\ndef task_func(data):\\n```\",\"libs\":\"['pandas', 'seaborn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}