Country

Indicator

Value type

Values present

Commercial banks and other lending (PPG + PNG) (NFL, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Angola	-3,782,837,000	-33.9%	85
Albania	253,336,000	+376%	30
Argentina	983,814,000	-390%	15
Armenia	205,389,000	+179%	32
Azerbaijan	-502,858,000	+21.4%	79
Benin	594,478,000	+128%	20
Burkina Faso	-37,237,000	-91.8%	59
Bangladesh	1,235,431,000	+287%	14
Bosnia & Herzegovina	467,324,000	+1,904%	25
Belarus	-1,688,028,000	+126%	82
Belize	-4,301,000	+80%	53
Bolivia	-87,929,000	-84%	66
Brazil	11,460,219,000	-65.1%	2
Bhutan	-4,246,000	+2.63%	52
China	-9,927,394,000	-52.4%	87
Côte d’Ivoire	281,002,000	-86.5%	29
Cameroon	-179,244,000	+183%	71
Congo - Kinshasa	3,472,000	-115%	45
Congo - Brazzaville	-208,407,000	-34.9%	72
Colombia	6,575,952,000	-26.8%	3
Cape Verde	-27,701,000	+170%	58
Costa Rica	479,585,000	-36.6%	23
Dominica	-5,029,000	+14.6%	54
Dominican Republic	582,082,000	+27.1%	22
Ecuador	640,590,000	-599%	19
Egypt	3,517,179,000	+31.1%	8
Ethiopia	105,841,000	-117%	35
Gabon	473,396,000	-867%	24
Georgia	349,254,000	-29.8%	26
Ghana	712,161,000	-170%	17
Guinea	163,184,000	-29.9%	33
Gambia	-3,358,000	-18%	51
Grenada	0		47
Guatemala	-65,262,000	-96.6%	62
Guyana	666,531,000	+2,875%	18
Honduras	-109,104,000	-40.2%	67
Haiti	-659,000	-63.8%	49
Indonesia	44,428,000	-99.1%	39
India	27,712,780,000	-423%	1
Iran	544,000	-98.8%	46
Iraq	-497,027,000	+33.9%	78
Jordan	13,229,000	-103%	42
Kenya	48,863,000	-134%	38
Laos	1,667,881,000	-482%	13
Lebanon	-2,292,385,000	-9.06%	84
Liberia	39,701,000		40
Sri Lanka	954,328,000	+185%	16
Lesotho	-48,949,000	+411%	60
Morocco	-253,857,000	-22.4%	74
Moldova	-1,694,000	-103%	50
Madagascar	8,116,000	-132%	43
Maldives	-7,464,000	-108%	55
Mexico	-5,511,859,000	+181%	86
North Macedonia	291,875,000	-14.3%	28
Myanmar (Burma)	-114,361,000	+346%	68
Montenegro	-71,526,000	-84.8%	64
Mongolia	1,992,539,000	+12.7%	11
Mozambique	2,310,240,000	-27.2%	10
Mauritius	-229,692,000	-113%	73
Niger	-7,728,000	+2.64%	56
Nigeria	-1,354,421,000	-184%	81
Nicaragua	5,158,000	-111%	44
Nepal	-73,505,000	-170%	65
Pakistan	-406,746,000	-84.8%	76
Philippines	3,967,923,000	+357%	7
Papua New Guinea	-1,795,617,000	-1.77%	83
Rwanda	588,020,000	+132%	21
Sudan	0		47
Senegal	4,607,446,000	+110%	6
El Salvador	-121,869,000	-128%	69
Serbia	2,637,655,000	+198%	9
Suriname	-300,811,000	+358%	75
Eswatini	-56,526,000	+108%	61
Syria	0		47
Togo	23,952,000	-617%	41
Tajikistan	72,297,000	+75.7%	37
Turkmenistan	-66,654,000	+3,329%	63
Tunisia	-439,364,000	-6.43%	77
Turkey	5,443,847,000	+357%	5
Tanzania	1,695,699,000	+936%	12
Uganda	-740,847,000	-263%	80
Ukraine	297,542,000	-110%	27
Uzbekistan	5,983,286,000	+85.6%	4
St. Vincent & Grenadines	-652,000	-61.4%	48
Vietnam	125,538,000	-97.9%	34
Kosovo	238,359,000	+12.1%	31
South Africa	103,968,000	-94.3%	36
Zambia	-153,896,000	-74.4%	70
Zimbabwe	-25,419,000	-89.9%	57

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.NFL.PCBO.CD'

# 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.NFL.PCBO.CD'

# 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

Commercial banks and other lending (PPG + PNG) (NFL, current US$)

Year: 2023

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