BigCodeBench / BigCodeBench v0.1.0_hf 7c644bac-e86d-5ab5-b611-712f58165349

complete prompt

import pandas as pd
import numpy as np
from random import choice, seed as set_seed

def task_func(num_of_students, seed=42, name_list=None, gender_list=None, age_range=(15, 20), score_range=(50, 100)):
    """
    Generate a Pandas DataFrame with randomized student data. This function allows for specifying 
    the total number of students and the randomness seed for reproducible outcomes. Data attributes 
    include student names, ages, genders, and scores, each derived from provided parameters or defaults.

    Parameters:
    - num_of_students (int): The number of student records to generate. Must be a positive integer.
    - seed (int, optional): Seed for the random number generator to ensure reproducible data. Defaults to 42.
    - name_list (list of str, optional): A list of names from which student names are randomly selected. 
      If not provided, defaults to ['John', 'Mike', 'Sara', 'Emma', 'Nick'].
    - gender_list (list of str, optional): A list of genders from which student genders are randomly selected. 
      If not provided, defaults to ['Male', 'Female'].
    - age_range (tuple of int, optional): A tuple specifying the inclusive range of student ages. Defaults to (15, 20).
    - score_range (tuple of int, optional): A tuple specifying the inclusive range of student scores. Defaults to (50, 100).

    Returns:
    - pandas.DataFrame: A DataFrame object with columns ['Name', 'Age', 'Gender', 'Score'], containing 
      randomly generated data for the specified number of students. Names and genders are randomly selected 
      from the provided lists (or defaults). Ages and scores are randomly generated within the specified ranges.

    Raises:
    - ValueError: If num_of_students is non-positive.

    Notes:
    - The 'Name' column values are selected randomly from the 'name_list'.
    - The 'Age' column values are integers randomly generated within the 'age_range', inclusive.
    - The 'Gender' column values are selected randomly from the 'gender_list'.
    - The 'Score' column values are integers randomly generated within the 'score_range', inclusive.
    - Setting the same seed value ensures the reproducibility of the dataset across different function calls.

    Requirements:
    - pandas
    - numpy
    - random

    Example:
    >>> student_data = task_func(5, seed=123)
    >>> print(student_data.head())
       Name  Age  Gender  Score
    0  John   20  Female     52
    1  John   19  Female     84
    2  Sara   16    Male     69
    3  John   17  Female     72
    4  Nick   16  Female     82
    """

instruct prompt

Generate a Pandas DataFrame with randomized student data. This function allows for specifying the total number of students and the randomness seed for reproducible outcomes. Data attributes include student names, ages, genders, and scores, each derived from provided parameters or defaults.
Note that: Notes: The 'Name' column values are selected randomly from the 'name_list'. The 'Age' column values are integers randomly generated within the 'age_range', inclusive. The 'Gender' column values are selected randomly from the 'gender_list'. The 'Score' column values are integers randomly generated within the 'score_range', inclusive. Setting the same seed value ensures the reproducibility of the dataset across different function calls.
The function should raise the exception for: ValueError: If num_of_students is non-positive.
The function should output with:
    pandas.DataFrame: A DataFrame object with columns ['Name', 'Age', 'Gender', 'Score'], containing
    randomly generated data for the specified number of students. Names and genders are randomly selected
    from the provided lists (or defaults). Ages and scores are randomly generated within the specified ranges.
You should write self-contained code starting with:

Code

import pandas as pd
import numpy as np
from random import choice, seed as set_seed
def task_func(num_of_students, seed=42, name_list=None, gender_list=None, age_range=(15, 20), score_range=(50, 100)):

code prompt

Code

import pandas as pd
import numpy as np
from random import choice, seed as set_seed
def task_func(num_of_students, seed=42, name_list=None, gender_list=None, age_range=(15, 20), score_range=(50, 100)):

entry point

task_func

libs

• pandas
• numpy
• random