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BigCodeBench / BigCodeBench v0.1.0_hf 32f41bd0-dd53-5519-a6fd-b7b20480d825

Problem

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

complete prompt

import numpy as np
from scipy.stats import ttest_ind
import matplotlib.pyplot as plt


def task_func(kwargs):
    """
    Performs a two-sample t-test on numerical data from two groups to determine if there is a significant
    difference in their means. The function handles NaN values, computes descriptive statistics for each group,
    and generates a boxplot and histograms for data visualization.

    Parameters:
    - kwargs (dict): A dictionary with two keys, 'group1' and 'group2'. Each key maps to a list of numbers.
                     Lists can contain NaN values, which will be excluded from analysis.

    Returns:
    - dict: A dictionary containing:
        - 'significant': Boolean. True if the means of the two groups are significantly different (p < 0.05).
        - 'group1_stats': Dictionary with mean and standard deviation of 'group1' (excluding NaNs).
        - 'group2_stats': Dictionary with mean and standard deviation of 'group2' (excluding NaNs).
        - 'ax_boxplot': A matplotlib Axes object with a boxplot comparing 'group1' and 'group2'.
        - 'ax_histogram': A matplotlib Axes object with histograms of 'group1' and 'group2'.

    Raises:
    - ValueError: If either group is empty, contains only NaN values, has less than two non-NaN values,
                  or if the variance in one or both groups is below a threshold (1e-8).

    Requirements:
    - numpy
    - scipy
    - matplotlib

    Note:
    - The function sets the significance level (alpha) at 0.05.
    - It removes NaN values before performing any calculations or plotting.
    - A t-test is performed with the 'nan_policy' set to 'omit' to ignore NaNs.
    - The function checks for sufficient non-NaN data points and adequate variance in each group before conducting the t-test.
    - The boxplot and histograms provide a visual comparison of the data distributions.
    
    Example:
    >>> data = {'group1': [1, 2, 3, 4], 'group2': [5, 6, 7, 8]}
    >>> results = task_func(data)
    >>> results['significant']
    True
    """

instruct prompt

Performs a two-sample t-test on numerical data from two groups to determine if there is a significant difference in their means. The function handles NaN values, computes descriptive statistics for each group, and generates a boxplot and histograms for data visualization.
Note that: The function sets the significance level (alpha) at 0.05. It removes NaN values before performing any calculations or plotting. A t-test is performed with the 'nan_policy' set to 'omit' to ignore NaNs. The function checks for sufficient non-NaN data points and adequate variance in each group before conducting the t-test. The boxplot and histograms provide a visual comparison of the data distributions.
The function should raise the exception for: ValueError: If either group is empty, contains only NaN values, has less than two non-NaN values, or if the variance in one or both groups is below a threshold (1e-8).
The function should output with:
    dict: A dictionary containing:
    'significant': Boolean. True if the means of the two groups are significantly different (p < 0.05).
    'group1_stats': Dictionary with mean and standard deviation of 'group1' (excluding NaNs).
    'group2_stats': Dictionary with mean and standard deviation of 'group2' (excluding NaNs).
    'ax_boxplot': A matplotlib Axes object with a boxplot comparing 'group1' and 'group2'.
    'ax_histogram': A matplotlib Axes object with histograms of 'group1' and 'group2'.
You should write self-contained code starting with:

Code

import numpy as np
from scipy.stats import ttest_ind
import matplotlib.pyplot as plt
def task_func(kwargs):

code prompt

Code

import numpy as np
from scipy.stats import ttest_ind
import matplotlib.pyplot as plt
def task_func(kwargs):

entry point

task_func

libs

  • numpy
  • matplotlib
  • scipy

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Source and history

task_id
32f41bd0-dd53-5519-a6fd-b7b20480d825
task_key
default--v0~2e1~2e0~5fhf--32f41bd0-dd53-5519-a6fd-b7b20480d825
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import