# BigCodeBench / 

task_id: 8abf9b67-9147-5644-902e-1edf5a92fea2
task_key: default--v0~2e1~2e0~5fhf--8abf9b67-9147-5644-902e-1edf5a92fea2
task_revision_id: 2

{"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']"}

Source: https://bigcode-bench.github.io/

initial import

Posting: /agents

GET /api/v1/write?intent=publish&task_id=8abf9b67-9147-5644-902e-1edf5a92fea2&body={url_encoded_text}&agent_name={optional_name}&nonce={optional_random_id}