Country

Indicator

Value type

Values present

GNI per capita (constant 2015 US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	2,537	-0.334%	63
Argentina	12,244	-5.34%	25
Armenia	5,119	+0.264%	47
Australia	62,511	-1.3%	4
Benin	1,306	+5.21%	76
Burkina Faso	825	+9.27%	89
Bangladesh	2,063	+3.3%	70
Bulgaria	10,136	+2.54%	28
Bosnia & Herzegovina	6,744	+3.64%	39
Belarus	6,820	+4.7%	38
Bermuda	121,176	+5.69%	1
Brazil	9,353	+2.54%	30
Brunei	31,823	+1.46%	12
Central African Republic	498	-1.86%	94
Canada	44,850	-1.51%	9
Chile	14,975	+2.97%	22
Côte d’Ivoire	2,368	+8.42%	65
Cameroon	1,458	-0.507%	75
Congo - Kinshasa	1,143	+8.89%	80
Colombia	7,048	+1.5%	35
Comoros	1,498	+1.48%	73
Cape Verde	4,747	+8.61%	48
Costa Rica	13,505	+3.82%	24
Cyprus	29,300	+0.363%	14
Germany	45,790	+0.726%	8
Djibouti	3,247	+2.47%	61
Denmark	62,962	+3.43%	3
Dominican Republic	8,743	+2.99%	32
Ecuador	6,107	-0.059%	42
Egypt	3,823	-1.11%	59
Gabon	9,525	+0.184%	29
Georgia	6,605	+14.4%	40
Ghana	2,235	+8.94%	68
Guinea	1,018	+8.71%	84
Gambia	712	-2.65%	91
Guinea-Bissau	779	+2.63%	90
Equatorial Guinea	4,137	-3.56%	55
Guatemala	4,614	+4.02%	49
Hong Kong SAR China	49,831	+5.14%	7
Honduras	2,397	+4.22%	64
Croatia	17,914	+5.54%	19
Haiti	1,273	-3.73%	77
Indonesia	4,205	+4%	54
India	2,364	+3.22%	66
Iraq	5,575	-3.91%	46
Israel	42,262	+0.53%	10
Italy	34,141	+1.16%	11
Kenya	1,864	+2.83%	71
Cambodia	2,186	+1.06%	69
Libya	7,214	+3.94%	34
Sri Lanka	4,024	+7.84%	57
Morocco	3,466	+3.75%	60
Moldova	4,209	+6.86%	52
Madagascar	430	+0.362%	96
Mexico	10,585	+0.205%	27
North Macedonia	6,832	+5.84%	36
Mali	967	+2.86%	86
Malta	29,540	+1.36%	13
Montenegro	9,128	+2.02%	31
Mongolia	5,774	+2.94%	43
Mozambique	590	+0.347%	93
Malaysia	11,528	+3.48%	26
Namibia	4,397	+3.37%	51
Niger	691	+9.8%	92
Nicaragua	2,260	+5.79%	67
Nepal	1,238	+4.2%	79
Pakistan	1,728	+2.46%	72
Peru	6,825	+4.77%	37
Philippines	4,206	+6.67%	53
Poland	17,329	+3.23%	20
Portugal	22,699	+3.02%	16
Paraguay	6,344	+1.61%	41
Romania	13,691	+3.13%	23
Rwanda	1,060	+11.6%	83
Saudi Arabia	26,483	-5.94%	15
Senegal	1,472	+2.74%	74
Singapore	61,988	+4.08%	5
Sierra Leone	1,073	-4.83%	82
El Salvador	4,113	+1.47%	56
Somalia	495	+2.36%	95
Serbia	8,307	+4.63%	33
Slovakia	18,169	+2.41%	18
Sweden	56,419	+0.733%	6
Seychelles	16,218	+1.43%	21
Chad	1,012	-2.83%	85
Togo	895	+2.81%	88
Tunisia	3,893	+0.714%	58
Tanzania	1,121	+3.05%	81
Uganda	921	+0.912%	87
Ukraine	2,885	+2.77%	62
Uruguay	18,549	+2.61%	17
United States	66,880	+1.63%	2
Samoa	4,518	+11.6%	50
Kosovo	5,745	+16%	45
South Africa	5,752	-1.1%	44
Zimbabwe	1,261	+0.281%	78

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.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.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 (constant 2015 US$)

Year: 2024

Acquire full dataset with Python or R from source