Country

Indicator

Value type

Values present

External debt stocks (% of GNI)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	19.8	-15.2%	103
Angola	74.3	+18.4%	21
Albania	48.9	-12.3%	51
Argentina	42.1	-2.1%	63
Armenia	67.6	-19.4%	27
Azerbaijan	21	+0.96%	100
Burundi	39.4	+38.3%	72
Benin	64.1	+2.83%	30
Burkina Faso	53.4	-2.34%	43
Bangladesh	22.3	+9.96%	99
Bosnia & Herzegovina	51.2	-7.36%	48
Belarus	52.9	-6.15%	45
Belize	51.1	-5.6%	49
Bolivia	37.2	-0.254%	76
Brazil	28.8	-5.67%	90
Botswana	10.7	+8.69%	110
Central African Republic	37.7	-6.75%	74
China	13.7	-0.671%	107
Côte d’Ivoire	48.3	+3.09%	54
Cameroon	31.7	-9.24%	85
Congo - Kinshasa	17.2	+10.4%	106
Congo - Brazzaville	53.5	-4.09%	42
Colombia	54.9	+0.589%	40
Comoros	28.2	-2.87%	92
Cape Verde	97.9	-9.9%	9
Costa Rica	48.5	-19.4%	53
Djibouti	85.6	+1.02%	16
Dominica	91.2	-7.23%	11
Dominican Republic	45	+1.99%	58
Algeria	3	-7.34%	113
Ecuador	52.1	-1.5%	47
Egypt	44.4	+25.4%	60
Ethiopia	20.4	-16%	102
Fiji	65.3	+2.74%	29
Gabon	41.2	-1.97%	65
Georgia	86.2	-16.5%	15
Ghana	58	-0.797%	37
Guinea	25.5	+1.67%	94
Gambia	56	+3.23%	38
Guinea-Bissau	55.2	-7.18%	39
Grenada	58.9	-3.68%	35
Guatemala	24.7	-8.36%	97
Guyana	28.3	+22%	91
Honduras	40.2	-7.83%	69
Haiti	13.3	+5.15%	108
Indonesia	30.4	-1.54%	86
India	18.4	-1.14%	105
Iran	2.45	+1.97%	114
Iraq	8.07	+1.5%	111
Jamaica	80.2	-15%	19
Jordan	88.4	+3.52%	13
Kazakhstan	69	-14.3%	24
Kenya	40.4	+9.62%	68
Kyrgyzstan	73.3	-10.7%	22
Cambodia	54.5	-6.86%	41
Laos	139	+3.68%	3
Liberia	52.4	+3.2%	46
St. Lucia	49	+15.7%	50
Sri Lanka	75.6	-7.04%	20
Lesotho	68.9	+7.68%	25
Morocco	48.7	-3.36%	52
Moldova	63.3	-5.14%	32
Madagascar	41.8	+3.89%	64
Maldives	68.7	-5.91%	26
Mexico	34.2	-16.5%	82
North Macedonia	84.3	-3.41%	17
Mali	32.8	-5.28%	83
Myanmar (Burma)	18.6	-9.46%	104
Montenegro	113	-17.6%	7
Mongolia	190	-14.6%	2
Mozambique	350	-5.16%	1
Mauritania	43.2	-10.5%	62
Mauritius	128	-2.02%	6
Malawi	29.2	+5.34%	88
Niger	34.7	-10.2%	81
Nigeria	29	+30.8%	89
Nicaragua	89.5	-12.7%	12
Nepal	24.1	+8.64%	98
Pakistan	39.4	+14%	71
Peru	35.5	-8.07%	79
Philippines	25	-3.69%	95
Papua New Guinea	53.2	-19.2%	44
Paraguay	62.8	+2.93%	33
Rwanda	82.4	+10.8%	18
Sudan	20.9	-53%	101
Senegal	134	+11.1%	4
Solomon Islands	31.8	+4.98%	84
Sierra Leone	37.5	+10.1%	75
El Salvador	71.3	+0.468%	23
Somalia	27.7	-31.3%	93
Serbia	63.5	-9.1%	31
São Tomé & Príncipe	66.2	-13.4%	28
Suriname	129	+7.23%	5
Eswatini	30	+3.86%	87
Chad	24.8	-8.76%	96
Togo	36.6	-6.56%	78
Thailand	38.5	-7.89%	73
Tajikistan	45.4	-4.08%	56
Turkmenistan	6.52	-18.4%	112
Timor-Leste	12.6	+33.2%	109
Tunisia	87.4	-7.03%	14
Turkey	45.2	-11.4%	57
Tanzania	44.6	+9.22%	59
Uganda	40.6	-11.3%	67
Ukraine	96.1	+14.3%	10
Uzbekistan	58.7	+9.04%	36
St. Vincent & Grenadines	59.4	+2.81%	34
Vietnam	34.8	-7.26%	80
Vanuatu	36.7	-6.49%	77
Samoa	46.8	-19.3%	55
Kosovo	39.7	+2.8%	70
South Africa	44.1	+2.44%	61
Zambia	110	+6.98%	8
Zimbabwe	40.8	-5.4%	66

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'DT.DOD.DECT.GN.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'DT.DOD.DECT.GN.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

Compare

External debt stocks (% of GNI)

Year: 2023

Acquire full dataset with Python or R from source