{"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":"a235f682-c19a-5d22-818f-7d5c21217b84","task_key":"default--v0~2e1~2e0~5fhf--a235f682-c19a-5d22-818f-7d5c21217b84","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf a235f682-c19a-5d22-818f-7d5c21217b84","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nimport matplotlib.pyplot as plt\\n# Constants\\nSTOP_WORDS = [\\\"a\\\", \\\"an\\\", \\\"the\\\", \\\"in\\\", \\\"on\\\", \\\"at\\\", \\\"and\\\", \\\"or\\\"]\\ndef task_func(file_path, save_path=None):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nimport matplotlib.pyplot as plt\\n\\n# Constants\\nSTOP_WORDS = [\\\"a\\\", \\\"an\\\", \\\"the\\\", \\\"in\\\", \\\"on\\\", \\\"at\\\", \\\"and\\\", \\\"or\\\"]\\n\\n\\ndef task_func(file_path, save_path=None):\\n    \\\"\\\"\\\"\\n    This function processes a text dataset from a CSV file, performs text vectorization while excluding specific\\n    stopwords, and creates a histogram of the ten most common words. The function is robust to different input\\n    scenarios, such as empty data or data containing only stopwords.\\n\\n    Parameters:\\n    - file_path (str): Path to the CSV file containing the text data. The CSV should have a single text column named \\\"Text\\\".\\n    - save_path (str, optional): Path where the histogram plot will be saved. If not provided, the plot is displayed.\\n\\n    Returns:\\n    - matplotlib Axes object: If save_path is not provided and valid words are found in the input, the function\\n      displays the histogram plot and returns the matplotlib Axes object.\\n    - None: In two scenarios:\\n      1. If save_path is provided, saves the plot to the specified location and returns None.\\n      2. If the input file is empty or contains only stop words, prints a message and returns None.\\n\\n    Requirements:\\n    - pandas\\n    - scikit-learn\\n    - matplotlib\\n\\n    Examples:\\n    >>> ax = task_func('text_data.csv')\\n    # ax is the matplotlib Axes object for the plot\\n    >>> result = task_func('text_data.csv', 'output_plot.png')\\n    # result is None, and the plot is saved to 'output_plot.png'\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"This function processes a text dataset from a CSV file, performs text vectorization while excluding specific stopwords, and creates a histogram of the ten most common words. The function is robust to different input scenarios, such as empty data or data containing only stopwords.\\nThe function should output with:\\n    matplotlib Axes object: If save_path is not provided and valid words are found in the input, the function\\n    displays the histogram plot and returns the matplotlib Axes object.\\n    None: In two scenarios:\\n    1. If save_path is provided, saves the plot to the specified location and returns None.\\n    2. If the input file is empty or contains only stop words, prints a message and returns None.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nimport matplotlib.pyplot as plt\\n# Constants\\nSTOP_WORDS = [\\\"a\\\", \\\"an\\\", \\\"the\\\", \\\"in\\\", \\\"on\\\", \\\"at\\\", \\\"and\\\", \\\"or\\\"]\\ndef task_func(file_path, save_path=None):\\n```\",\"libs\":\"['pandas', 'matplotlib', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}