Project: Analyze International Debt Statistics

Humans not only take debts to manage necessities. A country may also take debt to manage its economy. For example, infrastructure spending is one costly ingredient required for a country's citizens to lead comfortable lives. The World Bank is the organization that provides debt to countries.

In this project, you are going to analyze international debt data collected by The World Bank. The dataset contains information about the amount of debt (in USD) owed by developing countries across several categories. You are going to find the answers to the following questions:

What is the number of distinct countries present in the database?
What country has the highest amount of debt?
What country has the lowest amount of repayments?

Below is a description of the table you will be working with:

`international_debt` table

Column	Definition	Data Type
country_name	Name of the country	`varchar`
country_code	Code representing the country	`varchar`
indicator_name	Description of the debt indicator	`varchar`
indicator_code	Code representing the debt indicator	`varchar`
debt	Value of the debt indicator for the given country (in current US dollars)	`float`

You will execute SQL queries to answer three questions, as listed in the instructions.

DataFrameas

num_distinct_countries

variable

-- num_distinct_countries 
SELECT COUNT(DISTINCT country_name) AS total_distinct_countries
FROM public.international_debt;

DataFrameas

highest_debt_country

variable

-- highest_debt_country 
SELECT 
	country_name, 
	SUM(debt) AS total_debt
FROM public.international_debt
GROUP BY country_name
ORDER BY total_debt DESC
LIMIT 1;

DataFrameas

lowest_principal_repayment

variable

-- lowest_principal_repayment 
SELECT 
	country_name, 
	indicator_name, 
	SUM(debt) AS lowest_repayment
FROM public.international_debt
WHERE indicator_code = 'DT.AMT.DLXF.CD'
GROUP BY 
	indicator_name, 
	country_name 
ORDER BY lowest_repayment ASC
LIMIT 1;

DataFrameas

df

variable

-- distinct debt indicators 
SELECT 
    DISTINCT(indicator_code) as distinct_debt_indicators
FROM international_debt
GROUP BY distinct_debt_indicators
ORDER BY distinct_debt_indicators;

DataFrameas

df1

variable

-- avg debt groupped by indicators
SELECT 
    indicator_code AS debt_indicator,
    ROUND(AVG(debt)/1000000,2) as average_debt,
    indicator_name
FROM international_debt
GROUP BY 
	debt_indicator, 
	indicator_name
ORDER BY average_debt DESC
LIMIT 10;

DataFrameas

df2

variable

-- the most common debt indicators
SELECT 
    indicator_name,
    COUNT(indicator_code) as indicator_count
FROM international_debt
GROUP BY 
	indicator_code, 
	indicator_name
ORDER BY indicator_count DESC
LIMIT 20;