Country

Indicator

Value type

Values present

Present value of external debt (% of GNI)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Rank
Afghanistan	15.7	87
Angola	59	5
Albania	23.1	63
Argentina	11.8	98
Armenia	25.6	53
Azerbaijan	17.9	82
Burundi	30.4	40
Benin	32.4	37
Burkina Faso	18.3	81
Bangladesh	12.8	95
Bosnia & Herzegovina	19.6	75
Belarus	26	52
Belize	41.8	21
Bolivia	25.5	55
Brazil	9.42	103
Botswana	9.06	105
Central African Republic	26.8	50
China	2.77	112
Côte d’Ivoire	35.1	32
Cameroon	23	64
Congo - Kinshasa	12.3	97
Congo - Brazzaville	43.7	18
Colombia	30	42
Comoros	19.7	74
Cape Verde	55.3	6
Costa Rica	19.4	77
Djibouti	63.2	4
Dominica	45.9	15
Dominican Republic	30.2	41
Algeria	1.95	113
Ecuador	34.1	34
Egypt	31	39
Ethiopia	15.6	89
Fiji	27.2	48
Gabon	35	33
Georgia	32	38
Ghana	38.8	28
Guinea	18.6	79
Gambia	38.2	29
Guinea-Bissau	43.8	17
Grenada	40.6	24
Guatemala	11.6	99
Guyana	17.3	85
Honduras	24.4	57
Haiti	9.39	104
Indonesia	16.9	86
India	6.05	111
Iran	1.67	114
Iraq	6.19	109
Jamaica	50.4	9
Jordan	41.7	22
Kazakhstan	10.9	101
Kenya	29.6	44
Kyrgyzstan	26.2	51
Cambodia	19.4	76
Laos	65.5	2
Liberia	33.7	35
St. Lucia	40.7	23
Sri Lanka	51.7	8
Lesotho	36	31
Morocco	29.7	43
Moldova	15.7	88
Madagascar	23	65
Maldives	53.5	7
Mexico	17.6	84
North Macedonia	37.7	30
Mali	20.7	71
Myanmar (Burma)	13.4	93
Montenegro	47.9	12
Mongolia	46.3	14
Mozambique	43.3	19
Mauritania	28.8	46
Mauritius	24.2	59
Malawi	18.4	80
Niger	23.4	61
Nigeria	12.8	96
Nicaragua	39.9	26
Nepal	13.8	91
Pakistan	26.8	49
Peru	15	90
Philippines	13	94
Papua New Guinea	24.3	58
Paraguay	33.1	36
Rwanda	40	25
Sudan	20	72
Senegal	50.3	10
Solomon Islands	11.1	100
Sierra Leone	22.8	66
El Salvador	39.7	27
Somalia	23.5	60
Serbia	28.2	47
São Tomé & Príncipe	47.7	13
Suriname	84.4	1
Eswatini	22.3	68
Chad	17.6	83
Togo	20.9	70
Thailand	7.36	107
Tajikistan	20	73
Turkmenistan	6.15	110
Timor-Leste	9.78	102
Tunisia	44.9	16
Turkey	13.5	92
Tanzania	22.6	67
Uganda	21.9	69
Ukraine	49	11
Uzbekistan	25.5	54
St. Vincent & Grenadines	43.2	20
Vietnam	8.45	106
Vanuatu	23.4	62
Samoa	29.2	45
Kosovo	7.35	108
South Africa	25	56
Zambia	63.2	3
Zimbabwe	19.2	78

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.PVLX.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.PVLX.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

Present value of external debt (% of GNI)

Year: 2023

Acquire full dataset with Python or R from source