{"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":"207041a0-de51-50c0-9fb3-4832fe792d12","task_key":"default--v0~2e1~2e0~5fhf--207041a0-de51-50c0-9fb3-4832fe792d12","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 207041a0-de51-50c0-9fb3-4832fe792d12","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    Perform Principal Component Analysis (PCA) on the dataframe and visualize the two main components.\\n\\n    Parameters:\\n        df (DataFrame): The input dataframe containing numerical data.\\n\\n    Returns:\\n        DataFrame: A pandas DataFrame with the principal components named 'Principal Component 1' and 'Principal Component 2'.\\n        Axes: A Matplotlib Axes object representing the scatter plot of the two principal components. The plot includes:\\n              - Title: '2 Component PCA'\\n              - X-axis label: 'Principal Component 1'\\n              - Y-axis label: 'Principal Component 2'\\n\\n    Raises:\\n        ValueError: If the input is not a DataFrame, or if the DataFrame is empty.\\n\\n    Requirements:\\n        - pandas\\n        - sklearn.decomposition\\n        - matplotlib.pyplot\\n\\n    Example:\\n        >>> df = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list('ABCD'))\\n        >>> pca_df, ax = task_func(df)\\n        >>> plt.show()\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Perform Principal Component Analysis (PCA) on the dataframe and visualize the two main components.\\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 with the principal components named 'Principal Component 1' and 'Principal Component 2'.\\n    Axes: A Matplotlib Axes object representing the scatter plot of the two principal components. The plot includes:\\n    Title: '2 Component PCA'\\n    X-axis label: 'Principal Component 1'\\n    Y-axis label: 'Principal Component 2'\\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":"noindex","subproblems":[],"grids":[]}