{"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":"d0437431-3569-5ba0-b795-7eb04d1ebc67","task_key":"default--v0~2e1~2e0~5fhf--d0437431-3569-5ba0-b795-7eb04d1ebc67","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf d0437431-3569-5ba0-b795-7eb04d1ebc67","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom matplotlib import pyplot as plt\\ndef task_func(arr):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom matplotlib import pyplot as plt\\n\\n\\ndef task_func(arr):\\n    \\\"\\\"\\\"\\n    Calculate the sum of each row in a 2D numpy array and plot these sums as a time series.\\n\\n    This function takes a 2D numpy array and computes the sum of elements in each row. It\\n    then creates a Pandas DataFrame with these row sums and plots them as a time series,\\n    using dates starting from January 1, 2020, for each row.\\n\\n    Parameters:\\n    arr (numpy.ndarray): A 2D numpy array.\\n\\n    Returns:\\n    matplotlib.axes._axes.Axes: A plot representing the time series of row sums.\\n\\n    Requirements:\\n    - pandas\\n    - matplotlib\\n\\n    Handling Scenarios:\\n    - For non-empty arrays: The function computes the sum of elements for each row, \\n    stores these sums in a Pandas DataFrame, and then plots them. Each row in the plot represents \\n    the sum for a specific day, starting from January 1, 2020.\\n    - For empty arrays: The function creates an empty plot with the \\n    title 'Time Series of Row Sums' but without data. This is achieved by checking if the array size \\n    is zero (empty array) and if so, creating a subplot without any data.\\n    \\n    Note: \\n    - The function uses 'pandas' for DataFrame creation and 'matplotlib.pyplot' for plotting. \\n    The dates in the plot start from January 1, 2020, and each subsequent row represents the next day.\\n    \\n    Example:\\n    >>> arr = np.array([[i + j for i in range(3)] for j in range(5)])\\n    >>> ax = task_func(arr)\\n    >>> ax.get_title()\\n    'Time Series of Row Sums'\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Calculate the sum of each row in a 2D numpy array and plot these sums as a time series. This function takes a 2D numpy array and computes the sum of elements in each row. It then creates a Pandas DataFrame with these row sums and plots them as a time series, using dates starting from January 1, 2020, for each row. Handling Scenarios: - For non-empty arrays: The function computes the sum of elements for each row, stores these sums in a Pandas DataFrame, and then plots them. Each row in the plot represents the sum for a specific day, starting from January 1, 2020. - For empty arrays: The function creates an empty plot with the title 'Time Series of Row Sums' but without data. This is achieved by checking if the array size is zero (empty array) and if so, creating a subplot without any data.\\nNote that: The function uses 'pandas' for DataFrame creation and 'matplotlib.pyplot' for plotting. The dates in the plot start from January 1, 2020, and each subsequent row represents the next day.\\nThe function should output with:\\n    matplotlib.axes._axes.Axes: A plot representing the time series of row sums.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom matplotlib import pyplot as plt\\ndef task_func(arr):\\n```\",\"libs\":\"['pandas', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}