Country

Indicator

Value type

Values present

PPG, commercial banks (NFL, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Angola	-2,786,038,000	+4,657%	80
Albania	-34,965,000	+3.58%	59
Argentina	-59,364,000	+2,548%	63
Armenia	-4,150,000	+12.4%	46
Azerbaijan	-321,108,000	+10.6%	72
Benin	594,478,000	+746%	11
Burkina Faso	30,023,000	-1,832%	23
Bangladesh	-25,160,000	-161%	56
Bosnia & Herzegovina	-6,803,000	-7.72%	50
Belarus	-8,953,000	+2.64%	52
Belize	0		34
Bolivia	-9,244,000	+2.56%	53
Brazil	-1,875,111,000	-12.1%	79
Bhutan	-4,246,000	+2.63%	47
China	12,166,577,000	-27.6%	1
Côte d’Ivoire	424,419,000	-81.3%	13
Cameroon	3,922,000	-86.1%	29
Congo - Kinshasa	26,148,000	-779%	24
Congo - Brazzaville	0		34
Colombia	-1,209,379,000	-157%	77
Cape Verde	-27,701,000	+170%	58
Costa Rica	-226,078,000	+23.9%	71
Dominica	-1,298,000	+36.8%	40
Dominican Republic	-64,251,000	-6.89%	64
Ecuador	598,097,000	-756%	10
Egypt	2,573,198,000	+7.95%	2
Ethiopia	212,489,000	-149%	15
Gabon	473,768,000	-869%	12
Georgia	-5,384,000	-67.3%	49
Ghana	155,597,000	-56.7%	19
Guinea	178,184,000	+39.4%	17
Gambia	-3,358,000	0%	44
Grenada	0		34
Guatemala	-1,680,000	-394%	41
Guyana	-984,000	+6.38%	39
Honduras	-49,584,000	+155%	61
Haiti	-2,326,000	-33.3%	43
Indonesia	1,396,809,000	+273%	5
India	2,549,655,000	+108%	3
Iran	544,000	-98.8%	32
Iraq	-335,870,000	+60.9%	73
Jordan	-10,033,000	+123%	54
Kenya	187,412,000	-352%	16
Laos	-183,923,000	+55.4%	68
Lebanon	607,000	+43.8%	31
Liberia	0		34
Sri Lanka	48,289,000	-6.84%	22
Lesotho	-369,000	+73.2%	36
Morocco	-154,768,000	+789%	67
Moldova	-2,065,000	+2.63%	42
Madagascar	15,558,000	-189%	27
Maldives	-26,746,000	-130%	57
Mexico	-2,955,564,000	+211%	81
North Macedonia	-4,400,000	-102%	48
Myanmar (Burma)	-188,374,000	-14.4%	69
Montenegro	19,729,000	-114%	26
Mongolia	74,641,000	-22,718%	20
Mozambique	-113,505,000	+9.89%	65
Mauritius	52,399,000	-91.1%	21
Niger	-7,728,000	+2.64%	51
Nigeria	-480,729,000	+115%	74
Nicaragua	-753,000		38
Nepal	-256,000	+383%	35
Pakistan	-1,655,714,000	-50.3%	78
Philippines	-211,214,000	-12.3%	70
Papua New Guinea	-11,062,000	+39.6%	55
Rwanda	247,612,000	+91.3%	14
Sudan	0		34
Senegal	846,704,000	+524%	7
El Salvador	9,907,000	-1,097%	28
Serbia	915,991,000	+164%	6
Suriname	-4,147,000	+16.1%	45
Eswatini	0		34
Syria	0		34
Togo	23,952,000	-617%	25
Tajikistan	0		34
Turkmenistan	-55,807,000	-357%	62
Tunisia	2,535,000	-104%	30
Turkey	1,997,734,000	-5,263%	4
Tanzania	790,699,000	-577%	8
Uganda	613,323,000	-1,256%	9
Ukraine	-139,447,000	-125%	66
Uzbekistan	175,145,000	-61%	18
St. Vincent & Grenadines	281,000	-149%	33
Vietnam	-733,770,000	+11.7%	76
Kosovo	-421,000	-186%	37
South Africa	-680,659,000	+5.67%	75
Zambia	-48,226,000	-78.3%	60
Zimbabwe	0		34

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.PCBK.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.PCBK.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

PPG, commercial banks (NFL, current US$)

Year: 2023

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