Country

Indicator

Value type

Values present

Public and publicly guaranteed debt service (% of GNI)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	0.0767	-7.19%	114
Angola	13.8	+54.2%	1
Albania	1.97	-4.09%	56
Argentina	1.59	+2.92%	70
Armenia	3.04	+37.9%	36
Azerbaijan	2.18	+13.4%	53
Burundi	1.02	+34.3%	88
Benin	4.07	+54.6%	20
Burkina Faso	1.18	+15.8%	81
Bangladesh	0.973	+36.8%	93
Bosnia & Herzegovina	3.1	+58.6%	35
Belarus	6.32	+25.4%	6
Belize	3.6	+36.3%	27
Bolivia	3.4	-19.6%	31
Brazil	1.49	-8.9%	73
Bhutan	4.74	+0.769%	15
Botswana	1.1	+41.3%	86
Central African Republic	0.242	-15.3%	111
China	0.299	-25.5%	110
Côte d’Ivoire	3.62	+50.5%	26
Cameroon	2.86	-24.5%	41
Congo - Kinshasa	0.615	+10.3%	104
Congo - Brazzaville	5.18	-15.8%	12
Colombia	3.81	+44.2%	23
Comoros	0.727	+462%	101
Cape Verde	5.38	+23%	10
Costa Rica	3.15	+74.8%	34
Djibouti	1.22	-14.5%	79
Dominica	4.66	+1.45%	17
Dominican Republic	3.64	+25.6%	25
Algeria	0.047	-17.3%	115
Ecuador	2.83	+0.0613%	42
Egypt	4.01	+26.1%	21
Fiji	2.75	+24.9%	44
Gabon	5.27	+100%	11
Georgia	2.43	+13.9%	50
Ghana	0.712	-73.5%	102
Guinea	1	+19.4%	90
Gambia	1.67	+2.78%	68
Guinea-Bissau	2.15	-27.8%	55
Grenada	4	-2.06%	22
Guatemala	0.935	-44.1%	95
Guyana	0.73	-22.3%	100
Honduras	2.73	+12.6%	46
Haiti	0.204	+36.7%	112
Indonesia	1.95	-36.8%	58
India	0.614	+15%	105
Iran	0.0186	+24%	116
Iraq	1.59	+7.38%	71
Jamaica	7.32	+12.9%	4
Jordan	4.68	-37.5%	16
Kazakhstan	1.37	-10.3%	75
Kenya	3.21	+22.1%	33
Kyrgyzstan	1.84	-10.3%	64
Cambodia	1.2	-0.526%	80
Laos	4.48	+38.1%	18
Lebanon	0.985	+38.6%	92
Liberia	0.844	+1.49%	98
St. Lucia	2.78	+22%	43
Sri Lanka	1.9	-31.7%	62
Lesotho	2.69	+19.8%	47
Morocco	2.49	-31.7%	49
Moldova	3.02	+225%	37
Madagascar	1.14	+25.7%	83
Maldives	7.1	-35.8%	5
Mexico	1.55	-9.64%	72
North Macedonia	5.96	+131%	9
Mali	1.29	-1.2%	77
Myanmar (Burma)	1.17	-20.6%	82
Montenegro	4.99	-20.9%	13
Mongolia	10.6	-23.4%	2
Mozambique	2.92	-14.9%	40
Mauritania	3.37	-1.75%	32
Mauritius	8.59	+560%	3
Malawi	0.891	+5.79%	97
Niger	0.896	-51.1%	96
Nigeria	1.01	+61.5%	89
Nicaragua	3.44	+16%	29
Nepal	0.844	+13.2%	99
Pakistan	3.56	-4.32%	28
Peru	1.81	+83.5%	65
Philippines	1.26	-1.68%	78
Papua New Guinea	1.67	+34.4%	69
Paraguay	3.02	+23.7%	38
Rwanda	2.17	+69.8%	54
Sudan	0.41	+21.9%	107
Senegal	4.34	+15.3%	19
Solomon Islands	0.366	-5.19%	108
Sierra Leone	1.1	+16.1%	85
El Salvador	6.01	+0.805%	8
Somalia	0.147	-3.14%	113
Serbia	2.73	+14.7%	45
São Tomé & Príncipe	0.627	-50.2%	103
Suriname	4.79	+86.9%	14
Eswatini	1.74	+42.1%	66
Syria	0		117
Chad	1.94	-12.9%	59
Togo	1.91	-15%	61
Thailand	1	+155%	91
Tajikistan	1.73	-8.47%	67
Turkmenistan	1.89	-0.553%	63
Timor-Leste	0.942	+98.7%	94
Tonga	2.99	+51.6%	39
Tunisia	6.12	-3.59%	7
Turkey	2.19	-8.05%	52
Tanzania	2.41	+15.3%	51
Uganda	1.96	+29%	57
Ukraine	1.03	-27.1%	87
Uzbekistan	1.94	+40.6%	60
St. Vincent & Grenadines	3.41	-0.815%	30
Vietnam	1.13	+1.03%	84
Vanuatu	1.41	-8.94%	74
Samoa	3.78	-9.74%	24
Kosovo	0.468	-8.55%	106
South Africa	2.68	-22%	48
Zambia	1.34	-16.3%	76
Zimbabwe	0.309	+178%	109

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.TDS.DPPG.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.TDS.DPPG.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

Public and publicly guaranteed debt service (% of GNI)

Year: 2023

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