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 plotly.express as px

Big Mac Index Countries

big_mac = pd.read_csv("data/big-mac-raw-index.csv")

big_mac.head()

	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

len(big_mac['name'].unique())

big_mac['name'].unique()

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.

https://en.wikipedia.org/wiki/List_of_countries_with_Burger_King_franchises

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.shape

countries = countries[countries['Whopper Index Countries'] == 'x']
countries

	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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