Visualizing a bivariate distribution

We should bear in mind that the Big Mac index is not directly comparable between countries. Normally, we would expect commodities in poor countries to be cheaper than those in rich ones. To represent a fairer picture of the index, it would be better to show the relationship between Big Mac pricing and Gross Domestic Product (GDP) per capita.

We are going to acquire GDP per capita from Quandl's World Bank World Development Indicators (WWDI) dataset. Based on the previous code example of acquiring JSON data from Quandl, can you try to adapt it to download the GDP per capita dataset?

For those who are impatient, here is the full code:

import urllib
import json
import pandas as pd
import time
from urllib.request import urlopen


def get_gdp_dataset(api_key, country_code):
    """Obtain and parse a quandl GDP dataset in Pandas DataFrame format
    Quandl returns dataset in JSON format, where data is stored as a 
    list of lists in response['dataset']['data...