{"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":"760ae9fe-e8a3-5fb9-8414-1782e6b8b1e6","task_key":"default--v0~2e1~2e0~5fhf--760ae9fe-e8a3-5fb9-8414-1782e6b8b1e6","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 760ae9fe-e8a3-5fb9-8414-1782e6b8b1e6","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom sklearn.impute import SimpleImputer\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\ndef task_func(df):\\n\",\"complete_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom sklearn.impute import SimpleImputer\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Impute missing values in the last column of the dataframe using mean imputation, then create a box plot to visualize the distribution of data in the last column.\\n\\n    Parameters:\\n    df (DataFrame): The input dataframe.\\n    \\n    Returns:\\n    DataFrame: A pandas DataFrame with the imputed last column.\\n    Axes: A matplotlib Axes object with the boxplot of the last column of the dataframe.\\n\\n    Raises:\\n    ValueError: If the input is not a DataFrame or has no columns.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n    - sklearn\\n    - seaborn\\n    - matplotlib.pyplot\\n    \\n    Example:\\n    >>> df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))\\n    >>> df.iloc[::3, -1] = np.nan  # Insert some NaN values\\n    >>> imputed_df, ax = task_func(df)\\n    >>> ax.get_title()  # 'Boxplot of Last Column'\\n    'Boxplot of Last Column'\\n    >>> ax.get_xlabel() # 'D'\\n    'D'\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Impute missing values in the last column of the dataframe using mean imputation, then create a box plot to visualize the distribution of data in the last column.\\nThe function should raise the exception for: ValueError: If the input is not a DataFrame or has no columns.\\nThe function should output with:\\n    DataFrame: A pandas DataFrame with the imputed last column.\\n    Axes: A matplotlib Axes object with the boxplot of the last column of the dataframe.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.impute import SimpleImputer\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\ndef task_func(df):\\n```\",\"libs\":\"['pandas', 'matplotlib', 'numpy', 'seaborn', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}