{"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":"36ca6869-8c60-5f41-947d-739db6fac7af","task_key":"default--v0~2e1~2e0~5fhf--36ca6869-8c60-5f41-947d-739db6fac7af","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 36ca6869-8c60-5f41-947d-739db6fac7af","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport seaborn as sns\\nimport numpy as np\\n# Constants\\nLABELS = ['H\\\\u2082O', 'O\\\\u2082', 'CO\\\\u2082', 'N\\\\u2082', 'Ar']\\ndef task_func(x, y, labels):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport seaborn as sns\\nimport numpy as np\\n\\n# Constants\\nLABELS = ['H\\\\u2082O', 'O\\\\u2082', 'CO\\\\u2082', 'N\\\\u2082', 'Ar']\\n\\n\\ndef task_func(x, y, labels):\\n    \\\"\\\"\\\"\\n    Create a heatmap using the seaborn library for \\\"x\\\" as x-values and \\\"y\\\" as y-values with labels.\\n\\n    Parameters:\\n    x (list): List of numpy arrays representing the x-values of the data points.\\n    y (list): List of numpy arrays representing the y-values of the data points.\\n    labels (list): List of strings representing the labels for the chemical compounds.\\n\\n    Returns:\\n    ax (Axes): A seaborn heatmap object.\\n    df (DataFrame): The dataframe used to create the heatmap.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n    - seaborn\\n\\n    Example:\\n    >>> x = [np.array([1,2,3]), np.array([4,5,6]), np.array([7,8,9])]\\n    >>> y = [np.array([4,5,6]), np.array([7,8,9]), np.array([10,11,12])]\\n    >>> labels = ['H\\\\u2082O', 'O\\\\u2082', 'CO\\\\u2082']\\n    >>> ax = task_func(x, y, labels)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Create a heatmap using the seaborn library for \\\"x\\\" as x-values and \\\"y\\\" as y-values with labels.\\nThe function should output with:\\n    ax (Axes): A seaborn heatmap object.\\n    df (DataFrame): The dataframe used to create the heatmap.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport seaborn as sns\\nimport numpy as np\\n# Constants\\nLABELS = ['H\\\\u2082O', 'O\\\\u2082', 'CO\\\\u2082', 'N\\\\u2082', 'Ar']\\ndef task_func(x, y, labels):\\n```\",\"libs\":\"['pandas', 'numpy', 'seaborn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}