Make Country List

Building a Whopper index will require finding Whopper prices for a list of countries. These countries overlap quite a bit with the Big Mac Index, so we will go ahead and pull down that list as well.

We will be limited by the countries with an active Burger King website that have prices to collect.

import pandas as pd
import as px

Big Mac Index Countries

big_mac = pd.read_csv("data/big-mac-raw-index.csv")
date iso_a3 currency_code name local_price dollar_ex dollar_price USD EUR GBP JPY CNY
0 2000-04-01 ARG ARS Argentina 2.50 1.000000 2.500000 0.11607 0.05007 -0.16722 -0.09864 1.09091
1 2000-04-01 AUS AUD Australia 2.59 1.680000 1.541667 -0.31176 -0.35246 -0.48645 -0.44416 0.28939
2 2000-04-01 BRA BRL Brazil 2.95 1.790000 1.648045 -0.26427 -0.30778 -0.45102 -0.40581 0.37836
3 2000-04-01 GBR GBP Britain 1.90 0.632911 3.002000 0.34018 0.26092 0.00000 0.08235 1.51076
4 2000-04-01 CAN CAD Canada 2.85 1.470000 1.938776 -0.13448 -0.18566 -0.35417 -0.30099 0.62152
array(['Argentina', 'Australia', 'Brazil', 'Britain', 'Canada', 'Chile',
       'China', 'Czech Republic', 'Denmark', 'Euro area', 'Hong Kong',
       'Hungary', 'Indonesia', 'Israel', 'Japan', 'Malaysia', 'Mexico',
       'New Zealand', 'Poland', 'Russia', 'Singapore', 'South Africa',
       'South Korea', 'Sweden', 'Switzerland', 'Taiwan', 'Thailand',
       'United States', 'Philippines', 'Norway', 'Peru', 'Turkey',
       'Venezuela', 'Egypt', 'Colombia', 'Costa Rica', 'Pakistan',
       'Saudi Arabia', 'Sri Lanka', 'Ukraine', 'Uruguay', 'UAE', 'India',
       'Vietnam', 'Azerbaijan', 'Bahrain', 'Croatia', 'Guatemala',
       'Honduras', 'Jordan', 'Kuwait', 'Lebanon', 'Moldova', 'Nicaragua',
       'Oman', 'Qatar', 'Romania', 'United Arab Emirates'], dtype=object)

There are 58 unique countries on the Big Mac Index.

Make Whopper Index Map

The list of countries that have a Burger King franchise is available on Wikipedia. This is just a base list to work from. From here I will research each country to see if Burger King in that country has an activate website with prices.

We will pull the tables from this Wikipedia entry and concatenate them.

There are 114 countries that have Burger Kings. Let’s export this list and we will work on the two lists in Excel.

=== One Hour Later==

After determining the countries we will use for our Whopper Index, we will make a map showing the countries that will make up the Whopper Index.

countries = pd.read_csv("data/whopper_index_countries.csv")
FileNotFoundError: [Errno 2] No such file or directory: 'data/whopper_index_countries.csv'
countries = countries[countries['Whopper Index Countries'] == 'x']
Country/territory Big Mac Index Whopper Index Countries
3 Argentina Argentina x
6 Australia Australia x
7 Austria NaN x
8 Azerbaijan Azerbaijan x
10 Bahrain Bahrain x
... ... ... ...
121 United Arab Emirates United Arab Emirates x
123 United States United States x
124 Uruguay Uruguay x
125 Venezuela Venezuela x
126 Vietnam Vietnam x

79 rows × 3 columns

Whopper Index Countries

px.choropleth(countries, locations='Country/territory', locationmode='country names')

Big Mac Index Countries

px.choropleth(big_mac, locations='name', locationmode='country names')

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