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Exploring Global Socioeconomic Data with Python and Tableau
Exploring Global Socioeconomic Data with Python and Tableau
I was curious about how the cost of living varies across the world. So, I found a Kaggle dataset to explore.
I designed some interactive Tableau visualizations showing the comparative cost of living metrics of various countries.
https://public.tableau.com/app/profile/alind.vats/viz/CostofLivingDash/Dashboard1
I used Python to analyze and visualize various metrics related to the quality and cost of living in 135 countries. The metrics — Cost of Living, Rent, Cost of Living Plus Rent, Groceries, Restaurant Price, Local Purchasing Power, McMeal Cost — are valued relative to what they are in New York City, 100.
These are the countries with the highest Cost of Living Plus Rent Index. This index includes rent, groceries, restaurants, transportation, and utilities. These are the most expensive countries to live in.
These are the cheapest countries to live in.
These are the countries with the highest cost of living. This excludes rent. Barbados is a stand-out as it has a relatively low GDP per capita.
These are the countries where you’ll spend the most eating out. 100 here represents restaurant prices in NYC. Most of these countries are high-income European economies.
These are the countries with the cheapest restaurant prices. Most of these are low to middle-income developing countries in Asia or Africa.
Below are the countries with the lowest restaurant to groceries price ratio. If you don’t like cooking, these are places where you’d most rather eat out than cook. These are the places where you would save the least money if you decide to cook rather than eat out at a restaurant.
Below is the opposite. These are countries where you’d rather cook than eat out. These are countries where it is the most expensive to eat out in proportion to how much you will spend on groceries to cook meals at home.
Below are the countries with the highest rent. Apparently, NYC is the most expensive place to rent.
Rent often ends up being an individual’s major expenses if they’re frugal otherwise. These are the countries with the cheapest rent.
Local purchasing power shows relative purchasing power in buying goods and services in a given country for the average wage in that country. These are the countries you will feel the richest in.
These are the countries you’ll feel the poorest in. These are the countries where you can buy the fewest goods and services with the average wage.
These fields seem highly correlated as you can see in the correlation matrix below. A mild correlation worth noting is between the Local Purchasing Power and the other major metrics. That perhaps means that it provides some novel information, which could be its indirect measure of disposable income.
Different from other metrics, the McMeal values represent the cost in USD of a McMeal in the country. These are the countries with the most expensive McMeals if available.
These are the countries with the cheapest McMeals.
Check out my Kaggle notebook to view and play around with the Python code.
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