{"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":"af6e2f14-876f-5db2-a356-f6e1229499d1","task_key":"default--v0~2e1~2e0~5fhf--af6e2f14-876f-5db2-a356-f6e1229499d1","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf af6e2f14-876f-5db2-a356-f6e1229499d1","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import re\\nimport pandas as pd\\ndef task_func(input_df):\\n\",\"complete_prompt\":\"import re\\nimport pandas as pd\\n\\ndef task_func(input_df):\\n    \\\"\\\"\\\"\\n    Cleans the text in a pandas DataFrame column named 'text' by removing all special characters, punctuation marks, and spaces, then calculates the length of the cleaned text.\\n\\n    Requirements:\\n    - re\\n    - pandas\\n\\n    Parameters:\\n    - input_df (pandas.DataFrame): DataFrame with a column 'text' containing strings with alphanumeric and/or special characters.\\n\\n    Returns:\\n    - pandas.DataFrame: A DataFrame with two new columns 'clean_text' and 'text_length', where 'clean_text' is the cleaned text and 'text_length' is its length.\\n\\n    Examples:\\n    >>> df = pd.DataFrame({'text': ['Special $#! characters   spaces 888323']})\\n    >>> print(task_func(df))\\n                          clean_text  text_length\\n    0  Specialcharactersspaces888323           29\\n    >>> df = pd.DataFrame({'text': ['Hello, World!']})\\n    >>> print(task_func(df))\\n       clean_text  text_length\\n    0  HelloWorld           10\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Cleans the text in a pandas DataFrame column named 'text' by removing all special characters, punctuation marks, and spaces, then calculates the length of the cleaned text.\\nThe function should output with:\\n    pandas.DataFrame: A DataFrame with two new columns 'clean_text' and 'text_length', where 'clean_text' is the cleaned text and 'text_length' is its length.\\nYou should write self-contained code starting with:\\n```\\nimport re\\nimport pandas as pd\\ndef task_func(input_df):\\n```\",\"libs\":\"['pandas', 're']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}