{"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":"1e3c894d-2047-5251-accd-4bd4e8e7661f","task_key":"default--v0~2e1~2e0~5fhf--1e3c894d-2047-5251-accd-4bd4e8e7661f","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 1e3c894d-2047-5251-accd-4bd4e8e7661f","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from random import randint, seed\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport re\\n# Constants\\nTEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']\\nPENALTY_COST = 1000  # in dollars\\ndef task_func(goals, penalties, rng_seed=None, teams=TEAMS):\\n\",\"complete_prompt\":\"from random import randint, seed\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport re\\n\\n# Constants\\nTEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']\\nPENALTY_COST = 1000  # in dollars\\n\\n\\ndef task_func(goals, penalties, rng_seed=None, teams=TEAMS):\\n    \\\"\\\"\\\"\\n    Generate and analyze a Pandas DataFrame of football match results for multiple teams,\\n    incorporating random goals and penalties, then visualize the analyzed data with colomns 'Team', 'Goals',\\n    and 'Penalty Cost'. Penalties are converted into fines based on a predetermined penalty cost.\\n\\n    Parameters:\\n    - goals (int): The maximum number of goals a team can score in a match.\\n    - penalties (int): The maximum number of penalties a team can receive in a match.\\n    - rng_seed (int, optional): Seed for the random number generator to ensure reproducibility. Defaults to None.\\n    - teams (list of str, optional): List of team names to assign players\\n\\n    Returns:\\n    - DataFrame: A pandas DataFrame containing teams, their goals, and penalty costs, along with the original match results.\\n\\n    Requirements:\\n    - pandas\\n    - matplotlib.pyplot\\n    - random\\n    - re\\n\\n    Example:\\n    >>> analyzed_data = task_func(5, 3, rng_seed=42)\\n    >>> print(analyzed_data[['Team', 'Goals', 'Penalty Cost']])\\n         Team  Goals  Penalty Cost\\n    0  Team A      5             0\\n    1  Team B      0          2000\\n    2  Team C      1          1000\\n    3  Team D      1             0\\n    4  Team E      5             0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate and analyze a Pandas DataFrame of football match results for multiple teams, incorporating random goals and penalties, then visualize the analyzed data with colomns 'Team', 'Goals', and 'Penalty Cost'. Penalties are converted into fines based on a predetermined penalty cost.\\nThe function should output with:\\n    DataFrame: A pandas DataFrame containing teams, their goals, and penalty costs, along with the original match results.\\nYou should write self-contained code starting with:\\n```\\nfrom random import randint, seed\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport re\\n# Constants\\nTEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']\\nPENALTY_COST = 1000  # in dollars\\ndef task_func(goals, penalties, rng_seed=None, teams=TEAMS):\\n```\",\"libs\":\"['pandas', 'random', 'matplotlib', 're']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}