{"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":"fa8ee0db-b754-5b36-b428-d2ce0059c14a","task_key":"default--v0~2e1~2e0~5fhf--fa8ee0db-b754-5b36-b428-d2ce0059c14a","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf fa8ee0db-b754-5b36-b428-d2ce0059c14a","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(df):\\n\",\"complete_prompt\":\"import numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nfrom sklearn.preprocessing import StandardScaler\\n\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Plots the correlation matrix from numeric columns in a DataFrame and returns a DataFrame\\n    where the numeric columns are standardized to have mean 0 and variance 1.\\n\\n    Parameters:\\n    df (pandas.DataFrame): Input DataFrame with columns of numeric data.\\n\\n    Returns:\\n    pandas.DataFrame: Standardized DataFrame.\\n    matplotlib.figure.Figure: Figure object containing the heatmap of the correlation matrix.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n    - seaborn\\n    - matplotlib\\n    - sklearn\\n\\n    Raises:\\n    - ValueError: If the DataFrame is empty or if no numeric columns are present.\\n\\n    Notes:\\n    - Only numeric columns are considered for the heatmap. Non-numeric columns are ignored.\\n\\n    Examples:\\n    >>> import pandas as pd\\n    >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\\n    >>> standardized_df, fig = task_func(df)\\n    >>> standardized_df\\n              A         B\\n    0 -1.224745 -1.224745\\n    1  0.000000  0.000000\\n    2  1.224745  1.224745\\n    >>> type(fig)\\n    <class 'matplotlib.figure.Figure'>\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Plots the correlation matrix from numeric columns in a DataFrame and returns a DataFrame where the numeric columns are standardized to have mean 0 and variance 1.\\nNote that: Notes: Only numeric columns are considered for the heatmap. Non-numeric columns are ignored.\\nThe function should raise the exception for: ValueError: If the DataFrame is empty or if no numeric columns are present.\\nThe function should output with:\\n    pandas.DataFrame: Standardized DataFrame.\\n    matplotlib.figure.Figure: Figure object containing the heatmap of the correlation matrix.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(df):\\n```\",\"libs\":\"['sklearn', 'numpy', 'matplotlib', 'seaborn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}