benchmarks.wiki

benchmarks.wiki / Public workspace

BigCodeBench / BigCodeBench v0.1.0_hf abb08ccb-be08-5e27-ba08-f5f1ecf64e09

Problem

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

complete prompt

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


def task_func(data_dict):
    """
    Analyze the uniformity of a distribution represented by a dictionary of categories and their counts,
    and create a description to introduce this distribution.

    Parameters:
    - data_dict (dict): A dictionary with categories as keys and counts as values.

    Returns:
    - tuple: A tuple containing:
        - matplotlib.axes._axes.Axes: The axes object of the histogram.
        - str: A message indicating whether the distribution is uniform ("The distribution is uniform.")
               or not ("The distribution is not uniform.").

    Note:
    - If 'data_dict' is empty, the function returns None and a message "The distribution is uniform."
       indicating that an empty distribution is considered uniform by default.
    - If 'data_dict' is not empty, it calculates the average count of the categories.
       - The distribution is considered uniform if the absolute difference between each count and the
         average count is less than or equal to 1e-5.
       - If any count's absolute difference with the average count is more than 1e-5, the distribution
         is considered not uniform.
    - The function then creates a histogram of the counts using matplotlib, with the number of bins
       being the lesser of 10 or the number of unique counts. The histogram's x-ticks are labeled with
       the category names.

    Requirements:
    - collections
    - numpy
    - matplotlib

    Example:
    >>> data = {'A': 2, 'B': 3, 'C': 4, 'D': 1, 'E': 2}
    >>> ax, message = task_func(data)
    >>> print(message)
    The distribution is not uniform.
    """

instruct prompt

Analyze the uniformity of a distribution represented by a dictionary of categories and their counts, and create a description to introduce this distribution.
Note that: If 'data_dict' is empty, the function returns None and a message "The distribution is uniform." indicating that an empty distribution is considered uniform by default. If 'data_dict' is not empty, it calculates the average count of the categories. The distribution is considered uniform if the absolute difference between each count and the average count is less than or equal to 1e-5. If any count's absolute difference with the average count is more than 1e-5, the distribution is considered not uniform. The function then creates a histogram of the counts using matplotlib, with the number of bins being the lesser of 10 or the number of unique counts. The histogram's x-ticks are labeled with the category names.
The function should output with:
    tuple: A tuple containing:
    matplotlib.axes._axes.Axes: The axes object of the histogram.
    str: A message indicating whether the distribution is uniform ("The distribution is uniform.")
    or not ("The distribution is not uniform.").
You should write self-contained code starting with:

Code

import collections
import numpy as np
import matplotlib.pyplot as plt
def task_func(data_dict):

code prompt

Code

import collections
import numpy as np
import matplotlib.pyplot as plt
def task_func(data_dict):

entry point

task_func

libs

  • collections
  • numpy
  • matplotlib

Discussion

Discussion

No discussion posts on this page yet. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

See answer Answer published by the source

Artifacts

Code, notes and reproducible work shared by participants. Files are served from a separate origin.

No artifacts on this page yet. Share reproducible code or notes in a contribution. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

Source and history

task_id
abb08ccb-be08-5e27-ba08-f5f1ecf64e09
task_key
default--v0~2e1~2e0~5fhf--abb08ccb-be08-5e27-ba08-f5f1ecf64e09
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import