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BigCodeBench / BigCodeBench v0.1.0_hf 4bf94901-c5b5-530c-979c-e84ff9aa1eda

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt


def task_func(data):
    """
    Calculate statistical measurements (mean and standard deviation) of the values associated with
    each key in a list of dictionaries, and visualize mean and standard deviation with bar charts.

    Parameters:
    data (list): The list of dictionaries. Must not be empty. Each dictionary must have numeric values.

    Returns:
    tuple:
        - dict: A dictionary with keys and their corresponding mean and standard deviation.
        - list: A list of matplotlib Axes objects for each key's visualization.

    Requirements:
    - numpy
    - matplotlib.pyplot
    - collections.defaultdict
    
    Raises:
    - ValueError: If the input data is empty.
    - TypeError: If the input is not a list of dictionaries or if any value in the dictionaries is not numeric.
    
    Example:
    >>> stats, axes = task_func([{'cat': 1, 'dog': 3}, {'cat' : 2, 'dog': 5}, {'cat' : 3, 'dog': 7}])
    >>> stats
    {'cat': {'mean': 2.0, 'std': 0.816496580927726}, 'dog': {'mean': 5.0, 'std': 1.632993161855452}}
    >>> axes
    [<Axes: title={'center': 'Statistics of cat'}, ylabel='Value'>, <Axes: title={'center': 'Statistics of dog'}, ylabel='Value'>]
    """

instruct prompt

Calculate statistical measurements (mean and standard deviation) of the values associated with each key in a list of dictionaries, and visualize mean and standard deviation with bar charts.
The function should raise the exception for: ValueError: If the input data is empty. TypeError: If the input is not a list of dictionaries or if any value in the dictionaries is not numeric.
The function should output with:
    tuple:
    dict: A dictionary with keys and their corresponding mean and standard deviation.
    list: A list of matplotlib Axes objects for each key's visualization.
You should write self-contained code starting with:

Code

from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
def task_func(data):

code prompt

Code

from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
def task_func(data):

entry point

task_func

libs

  • collections
  • numpy
  • matplotlib

Discussion

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Source and history

task_id
4bf94901-c5b5-530c-979c-e84ff9aa1eda
task_key
default--v0~2e1~2e0~5fhf--4bf94901-c5b5-530c-979c-e84ff9aa1eda
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import