Explore and analyze the cost of living across the globe with this comprehensive EDA (Exploratory Data Analysis) project. This repository contains data-driven insights into the expenses associated with daily life in different cities worldwide. From housing and transportation to groceries and entertainment, delve into the factors influencing the cost of living. Leverage visualizations and statistical analyses to uncover trends, comparisons, and valuable information for anyone considering relocation or interested in global economic trends.
pip3 install folium
pip3 install geopandas
pip3 install opencage- Using OpenCage to Retrieve Latitude and Longitude
def color_producer(val):
if val <= df[item].quantile(.25):
return 'forestgreen'
elif val <= df[item].quantile(.50):
return 'goldenrod'
elif val <= df[item].quantile(.75):
return 'darkred'
else:
return 'black'- A Function called "color_producer" takes a numerical value val as its input and assigns a color based on its relationship to the quantiles of a DataFrame column (df[item]). The colors are chosen in a way that reflects different ranges of the data distribution.
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It looks like Switzerland, Iceland, and Norway are the most expensive of places. This can be confirmed by looking at the data below.
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It's also clear that Europe and North America are some of the most expensive places on Earth.
All_prices_normalized_for_each_Country
Price per Square Meter to Buy Apartment Outside of Centre
Average Monthly Net Salary (After Tax)
Group the cities by country using the mean of all the columns. This will give a much clearer overview when looking at the map, when trying to detect any trends.
Mapping Everything!
Some further areas I would like to expore in a future notebook would be:
- Grouping columns by similarity. Which places are more expensive for food vs. accomodation.
- Outliers. Which places are much cheaper or expensive for particular things, and exploring possible reasons for these.
- Exploring the correlation between how much things cost and their countries GDP.