{"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":"e9baf54a-7f53-5e7f-b9fb-aee3d4d39204","task_key":"default--v0~2e1~2e0~5fhf--e9baf54a-7f53-5e7f-b9fb-aee3d4d39204","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf e9baf54a-7f53-5e7f-b9fb-aee3d4d39204","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.decomposition import PCA\\nimport matplotlib.pyplot as plt\\ndef task_func(df):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.decomposition import PCA\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(df):\\n    '''\\n    Processes a DataFrame containing dates and lists of numbers. It converts the lists into separate columns,\\n    performs Principal Component Analysis (PCA), and returns the explained variance ratio of the principal components\\n    along with a bar chart visualizing this ratio. Returns 0,0 if the input DataFrame is empty.\\n\\n    Parameters:\\n    df (DataFrame): A pandas DataFrame with columns 'Date' and 'Value'. 'Date' is a date column, and 'Value' contains \\n                    lists of numbers.\\n\\n    Returns:\\n    tuple: (explained_variance_ratio, ax)\\n           explained_variance_ratio (ndarray): The explained variance ratio of the principal components.\\n           ax (Axes): The matplotlib Axes object for the variance ratio bar chart.\\n\\n    Note:\\n    - The function use \\\"Explained Variance Ratio of Principal Components\\\" for the plot title.\\n    - The function use \\\"Principal Component\\\" and \\\"Explained Variance Ratio\\\" as the xlabel and ylabel respectively.\\n    \\n    Requirements:\\n    - pandas\\n    - sklearn.decomposition\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> df = pd.DataFrame([['2021-01-01', [8, 10, 12]], ['2021-01-02', [7, 9, 11]]], columns=['Date', 'Value'])\\n    >>> explained_variance_ratio, ax = task_func(df)\\n    >>> print(len(explained_variance_ratio))\\n    2\\n    '''\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Processes a DataFrame containing dates and lists of numbers. It converts the lists into separate columns, performs Principal Component Analysis (PCA), and returns the explained variance ratio of the principal components along with a bar chart visualizing this ratio. Returns 0,0 if the input DataFrame is empty.\\nNote that: The function use \\\"Explained Variance Ratio of Principal Components\\\" for the plot title. The function use \\\"Principal Component\\\" and \\\"Explained Variance Ratio\\\" as the xlabel and ylabel respectively.\\nThe function should output with:\\n    tuple: (explained_variance_ratio, ax)\\n    explained_variance_ratio (ndarray): The explained variance ratio of the principal components.\\n    ax (Axes): The matplotlib Axes object for the variance ratio bar chart.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.decomposition import PCA\\nimport matplotlib.pyplot as plt\\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":"index","subproblems":[],"grids":[]}