BigCodeBench / BigCodeBench v0.1.0_hf 74545691-5418-58b3-afea-8cb14bb75255

complete prompt

import pandas as pd
import numpy as np
from random import choice

# Constants
DATA_TYPES = [str, int, float, list, tuple, dict, set]


def task_func(rows, columns):
    """
    Generates a DataFrame with a specified number of rows and columns, populated with randomly generated data.
    Each column's data type is randomly selected from a set of Python data types,
    including primitive and complex structures.

    Parameters:
    rows (int): Number of rows in the generated DataFrame.
    columns (int): Number of columns in the generated DataFrame. Each column is assigned a random data type.

    DataFrame: A DataFrame in which each column's data type could be one of the following,
    with random content generated accordingly:
    - str: Random strings of 5 lowercase alphabetic characters.
    - int: Random integers from 0 to 9.
    - float: Random floats derived by converting integers from 0 to 9 into float.
    - list: Lists of random length (1 to 5) containing integers from 0 to 9.
    - tuple: Tuples of random length (1 to 5) containing integers from 0 to 9.
    - dict: Dictionaries with a random number (1 to 5) of key-value pairs, keys and values are integers from 0 to 9.
    - set: Sets of random size (1 to 5) containing unique integers from 0 to 9.

    Returns:
    pd.DataFrame: A DataFrame with the specified number of rows and columns named 'col0', 'col1', etc., containing randomly generated data.

    Requirements:
    - pandas
    - numpy
    - random

    Example:
    >>> df = task_func(2, 3)
    >>> print(df.shape)
    (2, 3)
    >>> isinstance(df, pd.DataFrame)
    True
    """

instruct prompt

Generates a DataFrame with a specified number of rows and columns, populated with randomly generated data. Each column's data type is randomly selected from a set of Python data types, including primitive and complex structures. DataFrame: A DataFrame in which each column's data type could be one of the following, with random content generated accordingly: - str: Random strings of 5 lowercase alphabetic characters. - int: Random integers from 0 to 9. - float: Random floats derived by converting integers from 0 to 9 into float. - list: Lists of random length (1 to 5) containing integers from 0 to 9. - tuple: Tuples of random length (1 to 5) containing integers from 0 to 9. - dict: Dictionaries with a random number (1 to 5) of key-value pairs, keys and values are integers from 0 to 9. - set: Sets of random size (1 to 5) containing unique integers from 0 to 9.
The function should output with:
    pd.DataFrame: A DataFrame with the specified number of rows and columns named 'col0', 'col1', etc., containing randomly generated data.
You should write self-contained code starting with:

Code

import pandas as pd
import numpy as np
from random import choice
# Constants
DATA_TYPES = [str, int, float, list, tuple, dict, set]
def task_func(rows, columns):

code prompt

Code

import pandas as pd
import numpy as np
from random import choice
# Constants
DATA_TYPES = [str, int, float, list, tuple, dict, set]
def task_func(rows, columns):

entry point

task_func

libs

• pandas
• numpy
• random