BigCodeBench / BigCodeBench v0.1.0_hf c39432e7-f946-5e1a-8510-e8aad8838e71

complete prompt

import numpy as np
import pandas as pd
import statistics

def task_func(rows, columns=['A', 'B', 'C', 'D', 'E', 'F'], seed=42):
    """
    Create a Pandas DataFrame with a specified number of rows and six columns (default A-F), 
    each filled with random numbers between 1 and 100, using a specified seed for reproducibility. 
    Additionally, calculate the mean and median for each column.

    Parameters:
        - rows (int): The number of rows in the DataFrame. Must be a positive integer greater than 0.
        - columns (list, optional): Column names for the DataFrame. Defaults to ['A', 'B', 'C', 'D', 'E', 'F'].
        - seed (int, optional): Seed for the random number generator. Defaults to 42.

    Returns:
        - DataFrame: A pandas DataFrame with the generated data.
        - dict: A dictionary containing the calculated mean and median for each column. 
                The dictionary format is:
                {
                    'ColumnName': {
                        'mean': MeanValue,
                        'median': MedianValue
                    }, ...
                }
                where 'ColumnName' is each of the specified column names, 'MeanValue' is the calculated mean, 
                and 'MedianValue' is the calculated median for that column.

    Raises:
        - ValueError: If 'rows' is not a positive integer greater than 0.

    Requirements:
        - numpy
        - pandas
        - statistics

    Example:
        >>> df, stats = task_func(10)
        >>> print(df)
            A   B   C   D   E    F
        0  52  93  15  72  61   21
        1  83  87  75  75  88  100
        2  24   3  22  53   2   88
        3  30  38   2  64  60   21
        4  33  76  58  22  89   49
        5  91  59  42  92  60   80
        6  15  62  62  47  62   51
        7  55  64   3  51   7   21
        8  73  39  18   4  89   60
        9  14   9  90  53   2   84
        >>> print(stats)
        {'A': {'mean': 47, 'median': 42.5}, 'B': {'mean': 53, 'median': 60.5}, 'C': {'mean': 38.7, 'median': 32.0}, 'D': {'mean': 53.3, 'median': 53.0}, 'E': {'mean': 52, 'median': 60.5}, 'F': {'mean': 57.5, 'median': 55.5}}
    """

instruct prompt

Create a Pandas DataFrame with a specified number of rows and six columns (default A-F), each filled with random numbers between 1 and 100, using a specified seed for reproducibility. Additionally, calculate the mean and median for each column.
The function should raise the exception for: ValueError: If 'rows' is not a positive integer greater than 0.
The function should output with:
    DataFrame: A pandas DataFrame with the generated data.
    dict: A dictionary containing the calculated mean and median for each column.
    The dictionary format is:
    {
    'ColumnName': {
    'mean': MeanValue,
    'median': MedianValue
    }, ...
    }
    where 'ColumnName' is each of the specified column names, 'MeanValue' is the calculated mean,
    and 'MedianValue' is the calculated median for that column.
You should write self-contained code starting with:

Code

import numpy as np
import pandas as pd
import statistics
def task_func(rows, columns=['A', 'B', 'C', 'D', 'E', 'F'], seed=42):

code prompt

Code

import numpy as np
import pandas as pd
import statistics
def task_func(rows, columns=['A', 'B', 'C', 'D', 'E', 'F'], seed=42):

entry point

task_func

libs

• statistics
• pandas
• numpy