{"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":"d339b555-d60d-50d2-818e-d6923a95c81f","task_key":"default--v0~2e1~2e0~5fhf--d339b555-d60d-50d2-818e-d6923a95c81f","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf d339b555-d60d-50d2-818e-d6923a95c81f","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\nimport matplotlib.pyplot as plt\\n# Constants\\nCOLUMNS = ['Date', 'Value']\\ndef task_func(df, plot=False):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\nimport matplotlib.pyplot as plt\\n\\n# Constants\\nCOLUMNS = ['Date', 'Value']\\n\\ndef task_func(df, plot=False):\\n    '''\\n    Splits a list in the 'Value' column of a DataFrame into several columns, scales these columns using StandardScaler, \\n    and optionally returned the scaled data using a bar chart. The 'Date' column is converted to datetime and used as \\n    the index in the plot.\\n\\n    Parameters:\\n    df (DataFrame): A pandas DataFrame with a 'Date' column and a 'Value' column where 'Value' contains lists of numbers.\\n    plot (bool): If True, a bar chart of the scaled values is displayed. Defaults to False.\\n\\n    Returns:\\n    DataFrame: A pandas DataFrame with the 'Date' column and additional columns for each element in the original 'Value' list,\\n               where these columns contain the scaled values.\\n    Axes (optional): A matplotlib Axes object containing the bar chart, returned if 'plot' is True.\\n\\n    Note:\\n    - This function use \\\"Scaled Values Over Time\\\" for the plot title.\\n    - This function use \\\"Date\\\" and \\\"Scaled Value\\\" as the xlabel and ylabel respectively.\\n\\n    Raises:\\n    - This function will raise KeyError if the DataFrame does not have the 'Date' and 'Value' columns.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.preprocessing.StandardScaler\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> df = pd.DataFrame([['2021-01-01', [8, 10, 12]], ['2021-01-02', [7, 9, 11]]], columns=COLUMNS)\\n    >>> scaled_df, ax = task_func(df, plot=True)\\n    >>> print(scaled_df.shape)\\n    (2, 4)\\n    >>> plt.close()\\n    '''\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Splits a list in the 'Value' column of a DataFrame into several columns, scales these columns using StandardScaler, and optionally returned the scaled data using a bar chart. The 'Date' column is converted to datetime and used as the index in the plot.\\nNote that: This function use \\\"Scaled Values Over Time\\\" for the plot title. This function use \\\"Date\\\" and \\\"Scaled Value\\\" as the xlabel and ylabel respectively.\\nThe function should raise the exception for: This function will raise KeyError if the DataFrame does not have the 'Date' and 'Value' columns.\\nThe function should output with:\\n    DataFrame: A pandas DataFrame with the 'Date' column and additional columns for each element in the original 'Value' list,\\n    where these columns contain the scaled values.\\n    Axes (optional): A matplotlib Axes object containing the bar chart, returned if 'plot' is True.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\nimport matplotlib.pyplot as plt\\n# Constants\\nCOLUMNS = ['Date', 'Value']\\ndef task_func(df, plot=False):\\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":[]}