Country

Indicator

Value type

Values present

GNI per capita, PPP (constant 2021 international $)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	6,254	-0.334%	69
Argentina	25,284	-5.34%	29
Armenia	18,726	+0.264%	37
Australia	58,523	-1.3%	10
Benin	3,823	+5.21%	76
Burkina Faso	2,569	+9.27%	90
Bangladesh	8,855	+3.3%	62
Bulgaria	32,273	+2.54%	22
Bosnia & Herzegovina	19,913	+3.64%	35
Belarus	28,074	+4.7%	27
Bermuda	110,442	+5.69%	1
Brazil	18,932	+2.54%	36
Brunei	80,707	+1.46%	3
Central African Republic	1,180	-1.86%	98
Canada	55,319	-1.51%	11
Chile	28,707	+2.97%	24
Côte d’Ivoire	6,792	+8.42%	66
Cameroon	4,776	-0.507%	74
Congo - Kinshasa	2,366	+8.89%	92
Colombia	18,663	+1.5%	38
Comoros	3,554	+1.48%	79
Cape Verde	10,015	+8.61%	59
Costa Rica	24,653	+3.82%	30
Cyprus	47,251	+0.363%	14
Germany	65,408	+0.726%	7
Djibouti	6,708	+2.47%	67
Denmark	72,120	+3.43%	6
Dominican Republic	23,476	+2.99%	33
Ecuador	13,946	-0.059%	47
Egypt	15,805	-1.11%	44
Ethiopia	3,000	+8.22%	82
Gabon	17,981	+0.184%	39
Georgia	24,167	+14.4%	31
Ghana	6,966	+8.94%	65
Guinea	3,798	+8.71%	77
Gambia	2,864	-2.65%	85
Guinea-Bissau	2,699	+2.63%	89
Equatorial Guinea	11,774	-3.56%	55
Guatemala	12,682	+4.02%	51
Hong Kong SAR China	72,526	+5.14%	5
Honduras	6,183	+4.22%	70
Croatia	43,089	+5.54%	16
Haiti	2,985	-3.73%	83
Indonesia	13,851	+4%	48
India	9,744	+3.22%	60
Iraq	12,195	-3.91%	54
Israel	46,968	+0.53%	15
Italy	52,778	+1.16%	12
Kenya	5,695	+2.83%	71
Cambodia	6,569	+1.06%	68
Libya	12,256	+3.94%	53
Sri Lanka	13,379	+7.84%	49
Morocco	8,955	+3.75%	61
Moldova	17,330	+6.86%	40
Madagascar	1,574	+0.362%	95
Mexico	22,443	+0.205%	34
North Macedonia	23,758	+5.84%	32
Mali	2,855	+2.86%	86
Malta	51,843	+1.36%	13
Montenegro	28,425	+2.02%	26
Mongolia	16,830	+2.94%	42
Mozambique	1,450	+0.347%	97
Malaysia	33,000	+3.48%	21
Namibia	10,709	+3.37%	58
Niger	2,090	+9.8%	93
Nicaragua	7,786	+5.79%	63
Nepal	5,071	+4.2%	73
Pakistan	5,606	+2.46%	72
Peru	15,022	+4.77%	45
Philippines	11,580	+6.67%	56
Poland	43,044	+3.23%	17
Portugal	42,037	+3.02%	18
Paraguay	14,943	+1.61%	46
Romania	40,954	+3.13%	19
Rwanda	3,278	+11.6%	81
Saudi Arabia	61,973	-5.94%	9
Sudan	1,831	-15.3%	94
Senegal	4,264	+2.74%	75
Singapore	110,344	+4.08%	2
Sierra Leone	2,887	-4.83%	84
El Salvador	10,826	+1.47%	57
Somalia	1,460	+2.36%	96
Serbia	25,763	+4.63%	28
Slovakia	38,089	+2.41%	20
Sweden	65,071	+0.733%	8
Seychelles	28,579	+1.43%	25
Chad	2,451	-2.83%	91
Togo	2,777	+2.81%	87
Tunisia	12,626	+0.714%	52
Tanzania	3,646	+3.05%	78
Uganda	2,733	+0.912%	88
Ukraine	16,973	+2.77%	41
Uruguay	28,851	+2.61%	23
United States	75,644	+1.63%	4
Samoa	7,191	+11.6%	64
Kosovo	16,174	+16%	43
South Africa	12,984	-1.1%	50
Zimbabwe	3,403	+0.281%	80

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 = 'NY.GNP.PCAP.PP.KD'

# 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 <- 'NY.GNP.PCAP.PP.KD'

# 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

GNI per capita, PPP (constant 2021 international $)

Year: 2024

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