Country

Indicator

Value type

Values present

Bank nonperforming loans to total gross loans (%)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	4.56	-4.86%	38
United Arab Emirates	5.31	-19%	33
Argentina	3.55	+14.7%	48
Antigua & Barbuda	6.05	-12.4%	26
Austria	2.48	+24.4%	63
Azerbaijan	1.93	-24.8%	73
Belgium	1.82	-0.974%	75
Bangladesh	9.57	+9.68%	14
Bulgaria	3.62	-20.1%	46
Bosnia & Herzegovina	3.82	-15.9%	43
Belarus	5.05	+2.83%	37
Brazil	2.84	+7.63%	58
Botswana	3.67	-2.23%	45
Central African Republic	16.2	+11.8%	7
Canada	0.456	+33.2%	101
Switzerland	0.827	+20.7%	100
Chile	2.1	+26.4%	66
Cameroon	12.9	-1.13%	11
Congo - Kinshasa	6.56	-11.7%	24
Congo - Brazzaville	15.2	-9.54%	8
Colombia	3.47	+40.7%	49
Costa Rica	1.96	-6.87%	70
Cyprus	6.11	-20.2%	25
Czechia	1.21	-17.2%	92
Germany	1.54	+24.3%	83
Denmark	0.941	-11.8%	97
Dominican Republic	0.93	+12.6%	98
Ecuador	4.24	+13.8%	41
Spain	3.06	+0.0163%	55
Estonia	1.08	+35.2%	94
Finland	1.29	-4.69%	90
France	2.06	-0.812%	68
Gabon	7.58	-0.805%	20
United Kingdom	0.976	+3.04%	95
Georgia	1.48	-2.1%	87
Ghana	20.6	+39.1%	5
Equatorial Guinea	31.1	-43.8%	3
Greece	5.96	-27.1%	27
Guatemala	1.75	+39.6%	77
Hong Kong SAR China	1.2	+18.2%	93
Croatia	3.6	-16.9%	47
Hungary	3.27	-13.8%	52
Indonesia	1.96	-8.86%	71
India	1.72	-64.2%	78
Ireland	1.27	-20.9%	91
Iraq	14.7	-22.2%	9
Iceland	1.58	+12.8%	81
Israel	0.972	+36.6%	96
Jordan	6.8	+8.73%	22
Kazakhstan	2.89	-13.9%	57
Kenya	12.3	+14.7%	12
Kyrgyzstan	8.92	-28.4%	16
Cambodia	5.13	+89.2%	35
St. Kitts & Nevis	19.4	-11.1%	6
Kuwait	1.42	+0.893%	89
St. Lucia	14.5	+13.4%	10
Lesotho	3.84	-11%	42
Luxembourg	1.86	+17.2%	74
Latvia	2.41	+5.29%	64
Macao SAR China	3.47	+131%	50
Moldova	5.56	-13.8%	30
Madagascar	7.6	-5.32%	19
Maldives	8.35	-11.3%	17
Mexico	2.08	-0.36%	67
North Macedonia	2.67	-4.38%	61
Malta	3.14	-10.2%	54
Montenegro	5.78	-8.67%	29
Mauritius	5.31	+7.94%	34
Malawi	5.1	-7.69%	36
Malaysia	1.65	-3.81%	79
Namibia	5.79	+5.01%	28
Nicaragua	1.52	-1.14%	84
Netherlands	1.56	-2.98%	82
Norway	0.373	+36.9%	103
Nepal	3.78	+54.5%	44
Pakistan	6.63	+1.26%	23
Panama	2.57	+1.5%	62
Peru	4.48	+8.84%	40
Philippines	3.19	+1.66%	53
Papua New Guinea	5.42	+13.3%	31
Poland	2.33	-4.36%	65
Paraguay	3.01	+2.51%	56
Saudi Arabia	1.51	-16%	86
Solomon Islands	9.39	-1.31%	15
El Salvador	1.77	-1.55%	76
San Marino	21	-60.4%	4
Slovakia	1.99	+7.38%	69
Slovenia	1.52	-13.4%	85
Sweden	0.381	+25.3%	102
Eswatini	6.87	+3.25%	21
Chad	31.5	+13.7%	2
Thailand	2.76	-2.93%	60
Tonga	11.2	+35.4%	13
Trinidad & Tobago	2.8	-9.59%	59
Turkey	1.48	-25.2%	88
Uganda	4.5	-11.9%	39
Ukraine	37.4	-2.01%	1
Uruguay	1.65	+19%	80
United States	0.847	+18.2%	99
Uzbekistan	3.45	-2.2%	51
St. Vincent & Grenadines	8.11	-19.9%	18
Samoa	5.37	+7.8%	32
Kosovo	1.93	-0.113%	72

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 = 'FB.AST.NPER.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 <- 'FB.AST.NPER.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

Bank nonperforming loans to total gross loans (%)

Year: 2023

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