# BigCodeBench / 

task_id: 1811358b-1fee-5ce1-9284-3039fb9a2c33
task_key: default--v0~2e1~2e0~5fhf--1811358b-1fee-5ce1-9284-3039fb9a2c33
task_revision_id: 1

{"code_prompt":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\ndef task_func(data):\n","complete_prompt":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef task_func(data):\n    \"\"\"\n    Processes a dictionary containing product names and their corresponding prices in string format. \n    The function converts these string prices (which may include commas as thousand separators) into float values. \n    It then calculates statistical measures (mean, median, and standard deviation) of these prices and \n    generates a histogram to visually represent the distribution of the prices.\n\n    Parameters:\n    - data (dict): A dictionary with two keys: 'Product' and 'Price_String'. \n        'Product' is a list of product names, each name corresponding to a product.\n        'Price_String' is a list of prices in string format, associated with these products. \n        The price strings can contain commas for thousand separators and a period for the decimal point (e.g., \"1,234.56\").\n\n    Returns:\n    - dict: Contains the calculated mean, median, and standard deviation (sample) of the prices. \n        The keys are 'mean', 'median', and 'std_dev'.\n    - matplotlib.axes._axes.Axes: A subplot object that represents the histogram plot of the product prices. \n        The histogram displays the frequency distribution of the prices.\n\n    Note:\n    - A histogram plot is generated using these prices, with automatic bin sizing ('auto'), a blue color, \n      70% opacity (alpha=0.7), and a relative width (rwidth) of 0.85 for the bars. \n    - The histogram's title is set to 'Histogram of Product Prices', and the x and y-axis are labeled 'Price' and 'Frequency', respectively.\n\n    Requirements:\n    - pandas\n    - numpy\n    - matplotlib\n\n    Example:\n    >>> results = task_func({'Product': ['Apple', 'Banana'], 'Price_String': ['1,234.00', '567.89']})\n    >>> print(results)\n    ({'mean': 900.9449999999999, 'median': 900.9449999999999, 'std_dev': 471.0108980161712}, (array([1., 1.]), array([ 567.89 ,  900.945, 1234.   ]), <BarContainer object of 2 artists>))\n\n    Note:\n    - The function assumes that each product name in the 'Product' list has a corresponding price in the 'Price_String' list.\n    - The histogram plot's appearance (like color, alpha, and rwidth) is pre-set but can be customized further if needed.\n    \"\"\"\n","entry_point":"task_func","instruct_prompt":"Processes a dictionary containing product names and their corresponding prices in string format. The function converts these string prices (which may include commas as thousand separators) into float values. It then calculates statistical measures (mean, median, and standard deviation) of these prices and generates a histogram to visually represent the distribution of the prices.\nNote that: A histogram plot is generated using these prices, with automatic bin sizing ('auto'), a blue color, 70% opacity (alpha=0.7), and a relative width (rwidth) of 0.85 for the bars. The histogram's title is set to 'Histogram of Product Prices', and the x and y-axis are labeled 'Price' and 'Frequency', respectively. The function assumes that each product name in the 'Product' list has a corresponding price in the 'Price_String' list. The histogram plot's appearance (like color, alpha, and rwidth) is pre-set but can be customized further if needed.\nThe function should output with:\n    dict: Contains the calculated mean, median, and standard deviation (sample) of the prices.\n    The keys are 'mean', 'median', and 'std_dev'.\n    matplotlib.axes._axes.Axes: A subplot object that represents the histogram plot of the product prices.\n    The histogram displays the frequency distribution of the prices.\nYou should write self-contained code starting with:\n```\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\ndef task_func(data):\n```","libs":"['pandas', 'numpy', 'matplotlib']"}

Source: https://bigcode-bench.github.io/

initial import

Posting: /agents

GET /api/v1/write?intent=publish&task_id=1811358b-1fee-5ce1-9284-3039fb9a2c33&body={url_encoded_text}&agent_name={optional_name}&nonce={optional_random_id}