BigCodeBench / BigCodeBench v0.1.0_hf d6ae4652-c761-5145-a1d0-fecf7d470573

complete prompt

import numpy as np
import pandas as pd


def task_func(country_dict):
    """
    Generates a DataFrame representing the GDP for a predefined set of countries based on their presence in the p
    rovided dictionary. The GDP values are simulated with random integers to model economic data.

    Parameters:
    country_dict (dict): A dictionary mapping individual names to country names. The country names must correspond to
    the predefined set of countries: ['USA', 'UK', 'China', 'Japan', 'Australia'].

    Returns:
    DataFrame: A pandas DataFrame with each country's name from the input as the index and a randomly generated GDP
    value as the column. GDP values range between 1,000,000,000 and 100,000,000,000.

    Requirements:
    - numpy
    - pandas

    Example:
    >>> np.random.seed(0)
    >>> country_dict = {'John': 'USA', 'Alice': 'UK', 'Bob': 'China', 'Charlie': 'Japan', 'David': 'Australia'}
    >>> df = task_func(country_dict)
    >>> df.loc['USA']
    GDP    55085855791
    Name: USA, dtype: int64
    """

instruct prompt

Generates a DataFrame representing the GDP for a predefined set of countries based on their presence in the p rovided dictionary. The GDP values are simulated with random integers to model economic data.
The function should output with:
    DataFrame: A pandas DataFrame with each country's name from the input as the index and a randomly generated GDP
    value as the column. GDP values range between 1,000,000,000 and 100,000,000,000.
You should write self-contained code starting with:

Code

import numpy as np
import pandas as pd
def task_func(country_dict):

code prompt

Code

import numpy as np
import pandas as pd
def task_func(country_dict):

entry point

task_func

libs

• pandas
• numpy