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BigCodeBench / BigCodeBench v0.1.0_hf cdace57f-016f-5f8e-86fa-b029856d2545

Problem

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

complete prompt

import csv
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt


def task_func(file_path):
    """
    This function processes a CSV file containing numeric data representing a population. It randomly
    selects 30 individuals from this population without replacement to form a sample. The function
    calculates the mean and standard deviation of this sample. The means delta degree is 1. It also generates a histogram of the
    sample data and overlays a normal distribution curve on this histogram.

    Parameters:
    - file_path (str): A string representing the path to the CSV file. Each line in the file should contain
                     a single numeric value representing an individual in the population.

    Returns:
    - Tuple (float, float, matplotlib.axes._axes.Axes): The function returns a tuple containing
    three elements:
        - Sample mean (float): The mean of the sample.
        - Sample standard deviation (float): The standard deviation of the sample, calculated with a
           degrees of freedom (ddof) of 1.
        - Matplotlib subplot (matplotlib.axes._axes.Axes): An object representing the
           generated histogram plot with the normal distribution curve.

    Requirements:
    - csv
    - numpy
    - scipy
    - matplotlib

    Notes:
    - The function uses numpy for random sampling and statistical calculations.
    - The matplotlib library is used to plot the histogram and the normal distribution curve.
    - The function includes exception handling for file input/output errors, ensuring that any issues
      with reading the CSV file are properly communicated.
    - The function plots a histogram of the sample using matplotlib, with the number of bins
         determined automatically ('auto').

    Example:
    >>> mean, std_dev, ax = task_func('population_data.csv')
    >>> print(mean, std_dev)
    (50.5, 29.011491975882016)

    In this example, 'population_data.csv' is a CSV file where each line contains a numeric value. The
    function reads this file, samples 30 values, computes their mean and standard deviation, and plots
    a histogram with a normal distribution curve.
    """

instruct prompt

This function processes a CSV file containing numeric data representing a population. It randomly selects 30 individuals from this population without replacement to form a sample. The function calculates the mean and standard deviation of this sample. The means delta degree is 1. It also generates a histogram of the sample data and overlays a normal distribution curve on this histogram. In this example, 'population_data.csv' is a CSV file where each line contains a numeric value. The function reads this file, samples 30 values, computes their mean and standard deviation, and plots a histogram with a normal distribution curve.
Note that: Notes: The function uses numpy for random sampling and statistical calculations. The matplotlib library is used to plot the histogram and the normal distribution curve. The function includes exception handling for file input/output errors, ensuring that any issues with reading the CSV file are properly communicated. The function plots a histogram of the sample using matplotlib, with the number of bins determined automatically ('auto').
The function should output with:
    Tuple (float, float, matplotlib.axes._axes.Axes): The function returns a tuple containing
    three elements:
    Sample mean (float): The mean of the sample.
    Sample standard deviation (float): The standard deviation of the sample, calculated with a
    degrees of freedom (ddof) of 1.
    Matplotlib subplot (matplotlib.axes._axes.Axes): An object representing the
    generated histogram plot with the normal distribution curve.
You should write self-contained code starting with:

Code

import csv
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
def task_func(file_path):

code prompt

Code

import csv
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
def task_func(file_path):

entry point

task_func

libs

  • csv
  • numpy
  • matplotlib
  • scipy

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Source and history

task_id
cdace57f-016f-5f8e-86fa-b029856d2545
task_key
default--v0~2e1~2e0~5fhf--cdace57f-016f-5f8e-86fa-b029856d2545
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import