{"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":"26232fca-ac5b-56aa-8344-919362e2716a","task_key":"default--v0~2e1~2e0~5fhf--26232fca-ac5b-56aa-8344-919362e2716a","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 26232fca-ac5b-56aa-8344-919362e2716a","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport regex as re\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nCOLUMN_NAMES = [\\\"Name\\\", \\\"Email\\\", \\\"Age\\\", \\\"Country\\\"]\\ndef task_func(text):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport regex as re\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\nCOLUMN_NAMES = [\\\"Name\\\", \\\"Email\\\", \\\"Age\\\", \\\"Country\\\"]\\n\\n\\ndef task_func(text):\\n    \\\"\\\"\\\"\\n    Extract data from a text and create a Pandas DataFrame.\\n    The text contains several lines, each formatted as 'Name: John Doe, Email: john.doe@example.com, Age: 30, Country: USA'.\\n    Plot the age distribution using seaborn.\\n\\n    The data is extracted using the regular expression pattern:\\n    \\\"Name: (.*?), Email: (.*?), Age: (.*?), Country: (.*?)($|\\\\n)\\\"\\n    and the resulting DataFrame has columns: ['Name', 'Email', 'Age', 'Country']\\n\\n    Parameters:\\n    text (str): The text to analyze.\\n\\n    Returns:\\n    DataFrame: A pandas DataFrame with extracted data.\\n\\n    Requirements:\\n    - pandas\\n    - regex\\n    - seaborn\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> text = 'Name: John Doe, Email: john.doe@example.com, Age: 30, Country: USA\\\\\\\\nName: Jane Doe, Email: jane.doe@example.com, Age: 25, Country: UK'\\n    >>> df = task_func(text)\\n    >>> print(df)\\n           Name                 Email  Age Country\\n    0  John Doe  john.doe@example.com   30     USA\\n    1  Jane Doe  jane.doe@example.com   25      UK\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Extract data from a text and create a Pandas DataFrame. The text contains several lines, each formatted as 'Name: John Doe, Email: john.doe@example.com, Age: 30, Country: USA'. Plot the age distribution using seaborn. The data is extracted using the regular expression pattern: \\\"Name: (.*?), Email: (.*?), Age: (.*?), Country: (.*?)($|\\\\n)\\\" and the resulting DataFrame has columns: ['Name', 'Email', 'Age', 'Country']\\nThe function should output with:\\n    DataFrame: A pandas DataFrame with extracted data.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport regex as re\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nCOLUMN_NAMES = [\\\"Name\\\", \\\"Email\\\", \\\"Age\\\", \\\"Country\\\"]\\ndef task_func(text):\\n```\",\"libs\":\"['regex', 'pandas', 'matplotlib', 'seaborn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}