Project: Analyze International Debt Statistics

It's not that we humans only take debts to manage our 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 notebook, we 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. We are going to find the answers to questions like:

What is the total amount of debt that is owed by the countries listed in the dataset?
Which country owes the maximum amount of debt and what does that amount look like?
What is the average amount of debt owed by countries across different debt indicators?

Below is a snapshot of the database you will be working with:

country_name	country_code	indicator_name	indicator_code	debt
Afghanistan	AFG	"Disbursements on external debt, long-term (DIS, current US$)"	DT.DIS.DLXF.CD	72894453.7
Afghanistan	AFG	"Interest payments on external debt, long-term (INT, current US$)"	DT.INT.DLXF.CD	53239440.1
Afghanistan	AFG	"PPG, bilateral (AMT, current US$)"	DT.AMT.BLAT.CD	61739336.9
Afghanistan	AFG	"PPG, bilateral (DIS, current US$)"	DT.DIS.BLAT.CD	49114729.4
Afghanistan	AFG	"PPG, bilateral (INT, current US$)"	DT.INT.BLAT.CD	39903620.1
Afghanistan	AFG	"PPG, multilateral (AMT, current US$)"	DT.AMT.MLAT.CD	39107845
Afghanistan	AFG	"PPG, multilateral (DIS, current US$)"	DT.DIS.MLAT.CD	23779724.3
Afghanistan	AFG	"PPG, multilateral (INT, current US$)"	DT.INT.MLAT.CD	13335820
Afghanistan	AFG	"PPG, official creditors (AMT, current US$)"	DT.AMT.OFFT.CD	100847181.9
Afghanistan	AFG	"PPG, official creditors (DIS, current US$)"	DT.DIS.OFFT.CD	72894453.7

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

DataFrameas

num_distinct_countries

variable

--num_distinct_countries
WITH num_distinct_countries AS (
	SELECT COUNT(DISTINCT country_name) AS total_distinct_countries
	FROM international_debt)

SELECT *
FROM num_distinct_countries;

DataFrameas

distinct_debt_indicators

variable

--distinct_debt_indicators
WITH distinct_debt_indicators AS (
	SELECT DISTINCT indicator_name AS distinct_debt_indicators
	FROM public.international_debt
	ORDER BY distinct_debt_indicators)
	
SELECT *
FROM distinct_debt_indicators;

DataFrameas

total_debt

variable

--total_debt
WITH total_debt AS (
	SELECT ROUND(SUM(debt)*0.000001,2) AS total_debt
	FROM international_debt)
	
SELECT * 
FROM total_debt;

DataFrameas

highest_debt_country

variable

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

DataFrameas

avg_debt_per_indicator

variable

--avg_debt_per_indicator
WITH avg_debt_per_indicator AS (
	SELECT 
	   indicator_code AS debt_indicator, 
	   indicator_name, 
	   AVG(debt) AS average_debt
	FROM international_debt 
	GROUP BY indicator_code, indicator_name
    ORDER BY average_debt DESC
    LIMIT 10)
	
SELECT *
FROM avg_debt_per_indicator;

DataFrameas

highest_principal_repayment

variable

--highest_principal_repayment
WITH highest_principal_repayment AS(
	SELECT country_name, indicator_name
	FROM international_debt 
	WHERE indicator_code IN ('DT.AMT.DLXF.CD')
	ORDER BY debt DESC
	LIMIT 1)
	
SELECT *
FROM highest_principal_repayment;