{"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":"9b8676c5-29dd-58d6-82b9-686849d1693f","task_key":"default--v0~2e1~2e0~5fhf--9b8676c5-29dd-58d6-82b9-686849d1693f","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 9b8676c5-29dd-58d6-82b9-686849d1693f","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(df):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Normalize the last column of the DataFrame using MinMaxScaler from sklearn and plot the normalized data.\\n\\n    Parameters:\\n    - df (DataFrame): The input DataFrame.\\n    - bins (int, optional): Number of bins for the histogram. Defaults to 20.\\n\\n    Returns:\\n    - DataFrame: A pandas DataFrame where the last column has been normalized.\\n    - Axes: A Matplotlib Axes object representing the plot of the normalized last column. The plot includes:\\n      - Title: 'Normalized Data of <column_name>'\\n      - X-axis label: 'Index'\\n      - Y-axis label: 'Normalized Value'\\n\\n    Raises:\\n    - ValueError: If the input is not a DataFrame or if the DataFrame is empty.\\n\\n    Requirements:\\n    - pandas\\n    - matplotlib.pyplot\\n    - sklearn\\n\\n    Example:\\n    >>> df = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list('ABCD'))\\n    >>> normalized_df, ax = task_func(df)\\n    >>> plt.show()\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Normalize the last column of the DataFrame using MinMaxScaler from sklearn and plot the normalized data.\\nThe function should raise the exception for: ValueError: If the input is not a DataFrame or if the DataFrame is empty.\\nThe function should output with:\\n    DataFrame: A pandas DataFrame where the last column has been normalized.\\n    Axes: A Matplotlib Axes object representing the plot of the normalized last column. The plot includes:\\n    Title: 'Normalized Data of <column_name>'\\n    X-axis label: 'Index'\\n    Y-axis label: 'Normalized Value'\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(df):\\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":"noindex","subproblems":[],"grids":[]}