{"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":"5adf6b18-fcda-51f7-8273-e413fde6a0b7","task_key":"default--v0~2e1~2e0~5fhf--5adf6b18-fcda-51f7-8273-e413fde6a0b7","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 5adf6b18-fcda-51f7-8273-e413fde6a0b7","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport folium\\nfrom geopy.geocoders import Photon\\ndef task_func(dic):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport folium\\nfrom geopy.geocoders import Photon\\n\\ndef task_func(dic):\\n    \\\"\\\"\\\"\\n    Generates a Folium map with markers for specified locations. It preprocesses the input to handle\\n    both direct geographical coordinates and address strings. For address strings, it dynamically resolves\\n    their latitude and longitude using the Photon geolocation service. This flexible input handling\\n    allows for easy mapping of various location types.\\n\\n    Parameters:\\n        dic (dict): A dictionary with location names as keys. Each key can either map to a dictionary\\n                    {'Lat': latitude, 'Lon': longitude} for direct coordinates, or to a string indicating\\n                    the location's address for geolocation lookup using Photon.\\n\\n    Returns:\\n        folium.Map: A Folium map object with markers for each specified location.\\n\\n    Requirements:\\n    - pandas\\n    - folium\\n    - geopy.geocoders.Photon\\n\\n    Notes:\\n    - The geolocator, instantiated as Photon(user_agent=\\\"geoapiExercises\\\"), plays a crucial role in enabling\\n    the function to handle string addresses by converting them into latitude and longitude, thus broadening\\n    the scope of input data that can be mapped.\\n\\n    Examples:\\n    >>> locations = {'Place1': {'Lat': 0, 'Lon': 0}, 'Place2': 'New York, USA'}\\n    >>> result = task_func(locations)\\n    >>> isinstance(result, folium.Map)\\n    True\\n    >>> [0.0, 0.0] == result.location\\n    True\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generates a Folium map with markers for specified locations. It preprocesses the input to handle both direct geographical coordinates and address strings. For address strings, it dynamically resolves their latitude and longitude using the Photon geolocation service. This flexible input handling allows for easy mapping of various location types.\\nNote that: Notes: The geolocator, instantiated as Photon(user_agent=\\\"geoapiExercises\\\"), plays a crucial role in enabling the function to handle string addresses by converting them into latitude and longitude, thus broadening the scope of input data that can be mapped.\\nThe function should output with:\\n    folium.Map: A Folium map object with markers for each specified location.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport folium\\nfrom geopy.geocoders import Photon\\ndef task_func(dic):\\n```\",\"libs\":\"['pandas', 'geopy', 'folium']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}