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https://github.com/naemazam/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.
https://github.com/naemazam/analyze-international-debt-statistics

data-manipulation importing-and-cleaning-data jupyter-notebook python sql

Last synced: about 19 hours ago
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Host: GitHub
URL: https://github.com/naemazam/analyze-international-debt-statistics
Owner: naemazam
Created: 2021-06-06T09:07:32.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2021-06-06T09:15:13.000Z (over 3 years ago)
Last Synced: 2024-11-14T19:31:33.986Z (2 months ago)
Topics: data-manipulation, importing-and-cleaning-data, jupyter-notebook, python, sql
Language: Jupyter Notebook
Homepage: http://naemazam.github.io/
Size: 48.8 KB
Stars: 2
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

## 1. The World Bank's international debt data

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 owns 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?

The first line of code connects us to the international_debt database where the table international_debt is residing. Let's first SELECT all of the columns from the international_debt table. Also, we'll limit the output to the first ten rows to keep the output clean.

```python
%%sql
postgresql:///international_debt

select * from international_debt
limit 10;
```

10 rows affected.

country_name
country_code
indicator_name
indicator_code
debt

Afghanistan
AFG
Disbursements on external debt, long-term (DIS, current US$)
DT.DIS.DLXF.CD
72894453.700000003

Afghanistan
AFG
Interest payments on external debt, long-term (INT, current US$)
DT.INT.DLXF.CD
53239440.100000001

Afghanistan
AFG
PPG, bilateral (AMT, current US$)
DT.AMT.BLAT.CD
61739336.899999999

Afghanistan
AFG
PPG, bilateral (DIS, current US$)
DT.DIS.BLAT.CD
49114729.399999999

Afghanistan
AFG
PPG, bilateral (INT, current US$)
DT.INT.BLAT.CD
39903620.100000001

Afghanistan
AFG
PPG, multilateral (AMT, current US$)
DT.AMT.MLAT.CD
39107845

Afghanistan
AFG
PPG, multilateral (DIS, current US$)
DT.DIS.MLAT.CD
23779724.300000001

Afghanistan
AFG
PPG, multilateral (INT, current US$)
DT.INT.MLAT.CD
13335820

Afghanistan
AFG
PPG, official creditors (AMT, current US$)
DT.AMT.OFFT.CD
100847181.900000006

Afghanistan
AFG
PPG, official creditors (DIS, current US$)
DT.DIS.OFFT.CD
72894453.700000003

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' country_name country_code indicator_name indicator_code debt\n0 Afghanistan AFG Disbursements on external debt, long-term (DIS... DT.DIS.DLXF.CD 72894453.700000003\n1 Afghanistan AFG Interest payments on external debt, long-term ... DT.INT.DLXF.CD 53239440.100000001\n2 Afghanistan AFG PPG, bilateral (AMT, current US$) DT.AMT.BLAT.CD 61739336.899999999\n3 Afghanistan AFG PPG, bilateral (DIS, current US$) DT.DIS.BLAT.CD 49114729.399999999\n4 Afghanistan AFG PPG, bilateral (INT, current US$) DT.INT.BLAT.CD 39903620.100000001\n5 Afghanistan AFG PPG, multilateral (AMT, current US$) DT.AMT.MLAT.CD 39107845\n6 Afghanistan AFG PPG, multilateral (DIS, current US$) DT.DIS.MLAT.CD 23779724.300000001\n7 Afghanistan AFG PPG, multilateral (INT, current US$) DT.INT.MLAT.CD 13335820\n8 Afghanistan AFG PPG, official creditors (AMT, current US$) DT.AMT.OFFT.CD 100847181.900000006\n9 Afghanistan AFG PPG, official creditors (DIS, current US$) DT.DIS.OFFT.CD 72894453.700000003'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed

## 2. Finding the number of distinct countries

From the first ten rows, we can see the amount of debt owed by Afghanistan in the different debt indicators. But we do not know the number of different countries we have on the table. There are repetitions in the country names because a country is most likely to have debt in more than one debt indicator.

Without a count of unique countries, we will not be able to perform our statistical analyses holistically. In this section, we are going to extract the number of unique countries present in the table.

```python
%%sql
SELECT COUNT(DISTINCT(country_name))
AS total_distinct_countries
FROM international_debt;
```

* postgresql:///international_debt
1 rows affected.

total_distinct_countries

124

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' total_distinct_countries\n0 124'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed

## 3. Finding out the distinct debt indicators

We can see there are a total of 124 countries present on the table. As we saw in the first section, there is a column called indicator_name that briefly specifies the purpose of taking the debt. Just beside that column, there is another column called indicator_code which symbolizes the category of these debts. Knowing about these various debt indicators will help us to understand the areas in which a country can possibly be indebted to.

```python
%%sql
SELECT
DISTINCT indicator_code AS distinct_debt_indicators
FROM international_debt
ORDER BY distinct_debt_indicators;
```

* postgresql:///international_debt
25 rows affected.

distinct_debt_indicators

DT.AMT.BLAT.CD

DT.AMT.DLXF.CD

DT.AMT.DPNG.CD

DT.AMT.MLAT.CD

DT.AMT.OFFT.CD

DT.AMT.PBND.CD

DT.AMT.PCBK.CD

DT.AMT.PROP.CD

DT.AMT.PRVT.CD

DT.DIS.BLAT.CD

DT.DIS.DLXF.CD

DT.DIS.MLAT.CD

DT.DIS.OFFT.CD

DT.DIS.PCBK.CD

DT.DIS.PROP.CD

DT.DIS.PRVT.CD

DT.INT.BLAT.CD

DT.INT.DLXF.CD

DT.INT.DPNG.CD

DT.INT.MLAT.CD

DT.INT.OFFT.CD

DT.INT.PBND.CD

DT.INT.PCBK.CD

DT.INT.PROP.CD

DT.INT.PRVT.CD

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' distinct_debt_indicators\n0 DT.AMT.BLAT.CD\n1 DT.AMT.DLXF.CD\n2 DT.AMT.DPNG.CD\n3 DT.AMT.MLAT.CD\n4 DT.AMT.OFFT.CD\n5 DT.AMT.PBND.CD\n6 DT.AMT.PCBK.CD\n7 DT.AMT.PROP.CD\n8 DT.AMT.PRVT.CD\n9 DT.DIS.BLAT.CD\n10 DT.DIS.DLXF.CD\n11 DT.DIS.MLAT.CD\n12 DT.DIS.OFFT.CD\n13 DT.DIS.PCBK.CD\n14 DT.DIS.PROP.CD\n15 DT.DIS.PRVT.CD\n16 DT.INT.BLAT.CD\n17 DT.INT.DLXF.CD\n18 DT.INT.DPNG.CD\n19 DT.INT.MLAT.CD\n20 DT.INT.OFFT.CD\n21 DT.INT.PBND.CD\n22 DT.INT.PCBK.CD\n23 DT.INT.PROP.CD\n24 DT.INT.PRVT.CD'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed

## 4. Totaling the amount of debt owed by the countries

As mentioned earlier, the financial debt of a particular country represents its economic state. But if we were to project this on an overall global scale, how will we approach it?

Let's switch gears from the debt indicators now and find out the total amount of debt (in USD) that is owed by the different countries. This will give us a sense of how the overall economy of the entire world is holding up.

```python
%%sql
SELECT
ROUND(SUM(debt)/1000000, 2) AS total_debt
FROM international_debt;
```

* postgresql:///international_debt
1 rows affected.

total_debt

3079734.49

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' total_debt\n0 3079734.49'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed

## 5. Country with the highest debt

"Human beings cannot comprehend very large or very small numbers. It would be useful for us to acknowledge that fact." - Daniel Kahneman. That is more than 3 million million USD, an amount which is really hard for us to fathom.

Now that we have the exact total of the amounts of debt owed by several countries, let's now find out the country that owns the highest amount of debt along with the amount. Note that this debt is the sum of different debts owed by a country across several categories. This will help to understand more about the country in terms of its socio-economic scenarios. We can also find out the category in which the country owns its highest debt. But we will leave that for now.

```python
%%sql
SELECT country_name,
sum(debt) as total_debt
FROM international_debt
GROUP BY country_name
ORDER BY total_debt desc
Limit 1;
```

* postgresql:///international_debt
1 rows affected.

country_name
total_debt

China
285793494734.200001568

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' country_name total_debt\n0 China 285793494734.200001568'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed

## 6. Average amount of debt across indicators

So, it was China. A more in-depth breakdown of China's debts can be found here.

We now have a brief overview of the dataset and a few of its summary statistics. We already have an idea of the different debt indicators in which the countries owe their debts. We can dig even further to find out on an average how much debt a country owes? This will give us a better sense of the distribution of the amount of debt across different indicators.

```python
%%sql
SELECT
indicator_code AS debt_indicator,
indicator_name, AVG(debt) as average_debt

FROM international_debt
GROUP BY debt_indicator,indicator_name
ORDER BY average_debt desc
Limit 10;
```

* postgresql:///international_debt
10 rows affected.

debt_indicator
indicator_name
average_debt

DT.AMT.DLXF.CD
Principal repayments on external debt, long-term (AMT, current US$)
5904868401.499193612

DT.AMT.DPNG.CD
Principal repayments on external debt, private nonguaranteed (PNG) (AMT, current US$)
5161194333.812658349

DT.DIS.DLXF.CD
Disbursements on external debt, long-term (DIS, current US$)
2152041216.890243888

DT.DIS.OFFT.CD
PPG, official creditors (DIS, current US$)
1958983452.859836046

DT.AMT.PRVT.CD
PPG, private creditors (AMT, current US$)
1803694101.963265321

DT.INT.DLXF.CD
Interest payments on external debt, long-term (INT, current US$)
1644024067.650806481

DT.DIS.BLAT.CD
PPG, bilateral (DIS, current US$)
1223139290.398230108

DT.INT.DPNG.CD
Interest payments on external debt, private nonguaranteed (PNG) (INT, current US$)
1220410844.421518983

DT.AMT.OFFT.CD
PPG, official creditors (AMT, current US$)
1191187963.083064523

DT.AMT.PBND.CD
PPG, bonds (AMT, current US$)
1082623947.653623188

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' debt_indicator indicator_name average_debt\n0 DT.AMT.DLXF.CD Principal repayments on external debt, long-te... 5904868401.499193612\n1 DT.AMT.DPNG.CD Principal repayments on external debt, private... 5161194333.812658349\n2 DT.DIS.DLXF.CD Disbursements on external debt, long-term (DIS... 2152041216.890243888\n3 DT.DIS.OFFT.CD PPG, official creditors (DIS, current US$) 1958983452.859836046\n4 DT.AMT.PRVT.CD PPG, private creditors (AMT, current US$) 1803694101.963265321\n5 DT.INT.DLXF.CD Interest payments on external debt, long-term ... 1644024067.650806481\n6 DT.DIS.BLAT.CD PPG, bilateral (DIS, current US$) 1223139290.398230108\n7 DT.INT.DPNG.CD Interest payments on external debt, private no... 1220410844.421518983\n8 DT.AMT.OFFT.CD PPG, official creditors (AMT, current US$) 1191187963.083064523\n9 DT.AMT.PBND.CD PPG, bonds (AMT, current US$) 1082623947.653623188'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed

## 7. The highest amount of principal repayments

We can see that the indicator DT.AMT.DLXF.CD tops the chart of average debt. This category includes repayment of long term debts. Countries take on long-term debt to acquire immediate capital. More information about this category can be found here.

An interesting observation in the above finding is that there is a huge difference in the amounts of the indicators after the second one. This indicates that the first two indicators might be the most severe categories in which the countries owe their debts.

We can investigate this a bit more so as to find out which country owes the highest amount of debt in the category of long term debts (DT.AMT.DLXF.CD). Since not all the countries suffer from the same kind of economic disturbances, this finding will allow us to understand that particular country's economic condition a bit more specifically.

```python
%%sql
SELECT
country_name, indicator_name
FROM international_debt
WHERE debt = (SELECT
max(debt)
FROM international_debt
WHERE indicator_code='DT.AMT.DLXF.CD');
```

* postgresql:///international_debt
1 rows affected.

country_name
indicator_name

China
Principal repayments on external debt, long-term (AMT, current US$)

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' country_name indicator_name\n0 China Principal repayments on external debt, long-te...'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed

## 8. The most common debt indicator

China has the highest amount of debt in the long-term debt (DT.AMT.DLXF.CD) category. This is verified by The World Bank. It is often a good idea to verify our analyses like this since it validates that our investigations are correct.

We saw that long-term debt is the topmost category when it comes to the average amount of debt. But is it the most common indicator in which the countries owe their debt? Let's find that out.

```python
%%sql
SELECT
indicator_code,
COUNT(indicator_code) AS indicator_count
FROM international_debt
GROUP BY indicator_code
ORDER BY indicator_count DESC, indicator_code DESC
LIMIT 20;
```

* postgresql:///international_debt
20 rows affected.

indicator_code
indicator_count

DT.INT.OFFT.CD
124

DT.INT.MLAT.CD
124

DT.INT.DLXF.CD
124

DT.AMT.OFFT.CD
124

DT.AMT.MLAT.CD
124

DT.AMT.DLXF.CD
124

DT.DIS.DLXF.CD
123

DT.INT.BLAT.CD
122

DT.DIS.OFFT.CD
122

DT.AMT.BLAT.CD
122

DT.DIS.MLAT.CD
120

DT.DIS.BLAT.CD
113

DT.INT.PRVT.CD
98

DT.AMT.PRVT.CD
98

DT.INT.PCBK.CD
84

DT.AMT.PCBK.CD
84

DT.INT.DPNG.CD
79

DT.AMT.DPNG.CD
79

DT.INT.PBND.CD
69

DT.AMT.PBND.CD
69

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' indicator_code indicator_count\n0 DT.INT.OFFT.CD 124\n1 DT.INT.MLAT.CD 124\n2 DT.INT.DLXF.CD 124\n3 DT.AMT.OFFT.CD 124\n4 DT.AMT.MLAT.CD 124\n5 DT.AMT.DLXF.CD 124\n6 DT.DIS.DLXF.CD 123\n7 DT.INT.BLAT.CD 122\n8 DT.DIS.OFFT.CD 122\n9 DT.AMT.BLAT.CD 122\n10 DT.DIS.MLAT.CD 120\n11 DT.DIS.BLAT.CD 113\n12 DT.INT.PRVT.CD 98\n13 DT.AMT.PRVT.CD 98\n14 DT.INT.PCBK.CD 84\n15 DT.AMT.PCBK.CD 84\n16 DT.INT.DPNG.CD 79\n17 DT.AMT.DPNG.CD 79\n18 DT.INT.PBND.CD 69\n19 DT.AMT.PBND.CD 69'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed

## 9. Other viable debt issues and conclusion

There are a total of six debt indicators in which all the countries listed in our dataset have taken debt. The indicator DT.AMT.DLXF.CD is also there in the list. So, this gives us a clue that all these countries are suffering from a common economic issue. But that is not the end of the story, but just a part of the story.

Let's change tracks from debt_indicators now and focus on the amount of debt again. Let's find out the maximum amount of debt for an indicator that each country has. With this, we will be in a position to identify the other plausible economic issues a country might be going through.

In this notebook, we took a look at debt owed by countries across the globe. We extracted a few summary statistics from the data and unraveled some interesting facts and figures. We also validated our findings to make sure the investigations are correct.

```python
%%sql
SELECT
country_name,
MAX(debt) AS maximum_debt
FROM international_debt
GROUP BY country_name
ORDER BY maximum_debt DESC
LIMIT 10;
```

* postgresql:///international_debt
10 rows affected.

country_name
maximum_debt

China
96218620835.699996948

Brazil
90041840304.100006104

Russian Federation
66589761833.5

Turkey
51555031005.800003052

South Asia
48756295898.199996948

Least developed countries: UN classification
40160766261.599998474

IDA only
34531188113.199996948

India
31923507000.799999237

Indonesia
30916112653.799999237

Kazakhstan
27482093686.400001526

```python
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = ' country_name maximum_debt\n0 China 96218620835.699996948\n1 Brazil 90041840304.100006104\n2 Russian Federation 66589761833.5\n3 Turkey 51555031005.800003052\n4 South Asia 48756295898.199996948\n5 Least developed countries: UN classification 40160766261.599998474\n6 IDA only 34531188113.199996948\n7 India 31923507000.799999237\n8 Indonesia 30916112653.799999237\n9 Kazakhstan 27482093686.400001526'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."
```

1/1 tests passed