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BigCodeBench / BigCodeBench v0.1.0_hf 41e559ed-4d21-5589-9623-a5330a018a20

Problem

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

complete prompt

import pandas as pd
from scipy.stats import pearsonr


def task_func(data):
    """
    Calculates the Pearson correlation coefficient between numerical scores and categorical grades.

    This function performs three main tasks:
    1. Converts scores from string format to floats.
    2. Encodes categorical grades into numerical values based on their rank order.
    3. Computes the Pearson correlation coefficient between the numerical scores and the encoded grades.

    Parameters:
    - data (dict): A dictionary containing two keys:
                 - 'Score_String': A list of scores in string format.
                 - 'Grade': A list of corresponding grades in string format.
                 Each list under these keys must have the same length.

    Returns:
    - correlation (float): The Pearson correlation coefficient between the converted numerical scores and encoded grades.
           Returns NaN if the input data frame has less than 2 rows, as the correlation coefficient cannot be calculated in this case.

    Requirements:
    - pandas
    - scipy

    Example:
    >>> round(task_func({'Score_String': ['80.5', '85.7', '90.2'], 'Grade': ['B', 'B+', 'A-']}),2)
    -0.46
    """

instruct prompt

Calculates the Pearson correlation coefficient between numerical scores and categorical grades. This function performs three main tasks: 1. Converts scores from string format to floats. 2. Encodes categorical grades into numerical values based on their rank order. 3. Computes the Pearson correlation coefficient between the numerical scores and the encoded grades.
The function should output with:
    correlation (float): The Pearson correlation coefficient between the converted numerical scores and encoded grades.
    Returns NaN if the input data frame has less than 2 rows, as the correlation coefficient cannot be calculated in this case.
You should write self-contained code starting with:

Code

import pandas as pd
from scipy.stats import pearsonr
def task_func(data):

code prompt

Code

import pandas as pd
from scipy.stats import pearsonr
def task_func(data):

entry point

task_func

libs

  • pandas
  • scipy

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Source and history

task_id
41e559ed-4d21-5589-9623-a5330a018a20
task_key
default--v0~2e1~2e0~5fhf--41e559ed-4d21-5589-9623-a5330a018a20
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import