{"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":"5af9b939-9de0-5eb3-a295-179176200231","task_key":"default--v0~2e1~2e0~5fhf--5af9b939-9de0-5eb3-a295-179176200231","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 5af9b939-9de0-5eb3-a295-179176200231","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(data: pd.DataFrame) -> (pd.DataFrame, plt.Axes):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\n\\ndef task_func(data: pd.DataFrame) -> (pd.DataFrame, plt.Axes):\\n    \\\"\\\"\\\"\\n    Normalize the data and visualize it using a heatmap.\\n\\n    This function takes a pandas DataFrame, normalizes the data to a range [0, 1], and then visualizes this\\n    normalized data using a seaborn heatmap.  The heatmap uses the \\\"YlGnBu\\\" colormap to represent normalized\\n    values and includes a color bar labeled \\\"Normalized Value\\\" to indicate the range of data values.\\n    It returns both the normalized data and the heatmap plot.\\n\\n    Parameters:\\n    - data (pd.DataFrame): The input data with multiple features in columns.\\n\\n    Returns:\\n    - pd.DataFrame: Normalized data.\\n    - plt.Axes: Heatmap plot of the normalized data.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n    - matplotlib.pyplot\\n    - seaborn\\n    \\n    Example:\\n    >>> df = pd.DataFrame([[1,1,1], [2,2,2], [3,3,3]], columns=['Feature1', 'Feature2', 'Feature3'])\\n    >>> normalized_df, _ = task_func(df)\\n    >>> type(normalized_df)\\n    <class 'pandas.core.frame.DataFrame'>\\n    >>> normalized_df['Feature1'].iloc[0]  # Returns a normalized value between 0 and 1\\n    0.0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Normalize the data and visualize it using a heatmap. This function takes a pandas DataFrame, normalizes the data to a range [0, 1], and then visualizes this normalized data using a seaborn heatmap.  The heatmap uses the \\\"YlGnBu\\\" colormap to represent normalized values and includes a color bar labeled \\\"Normalized Value\\\" to indicate the range of data values. It returns both the normalized data and the heatmap plot.\\nThe function should output with:\\n    pd.DataFrame: Normalized data.\\n    plt.Axes: Heatmap plot of the normalized data.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(data: pd.DataFrame) -> (pd.DataFrame, plt.Axes):\\n```\",\"libs\":\"['pandas', 'sklearn', '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":[]}