Country

Indicator

Value type

Values present

Total reserves (includes gold, current US$)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	14,242,892,505	+2.16%	68
Albania	6,515,581,528	+0.932%	82
United Arab Emirates	237,931,339,945	+25.6%	13
Argentina	29,559,610,351	+28.1%	55
Armenia	3,685,220,143	+2.15%	98
Antigua & Barbuda	358,440,632	-1.63%	130
Australia	60,404,058,269	-2.11%	38
Austria	35,405,646,262	+13.4%	52
Azerbaijan	12,699,124,300	-7.63%	71
Belgium	41,449,463,816	+1.56%	48
Bangladesh	21,394,795,733	-2.13%	61
Bulgaria	43,698,277,896	-5.69%	46
Bahrain	4,948,702,759	-3.3%	92
Bosnia & Herzegovina	9,418,858,050	+2.32%	74
Belarus	8,912,194,598	+9.79%	76
Belize	498,087,321	+5.14%	125
Bolivia	1,977,025,889	+9.84%	109
Brazil	329,732,443,813	-7.12%	10
Brunei	4,414,307,817	-1.53%	94
Bhutan	941,017,689	+43.8%	118
Botswana	3,455,661,776	-27.3%	99
Canada	119,778,473,397	+1.89%	22
Switzerland	909,365,744,096	+5.26%	4
Chile	44,403,009,394	-4.26%	44
China	3,456,024,808,739	+0.188%	1
Colombia	61,897,968,642	+4.84%	37
Comoros	323,945,586	-0.19%	132
Cape Verde	783,105,558	-6.54%	119
Costa Rica	14,177,018,014	+7.2%	69
Cyprus	2,087,536,372	+16.7%	107
Czechia	146,281,239,762	-1.41%	21
Germany	377,935,574,356	+17.1%	9
Djibouti	348,725,427	-30.5%	131
Dominica	155,971,242	-15%	135
Denmark	108,405,100,883	-0.883%	24
Dominican Republic	13,471,257,813	-13.3%	70
Algeria	83,007,108,429	+2.2%	30
Ecuador	6,907,743,955	+55.5%	80
Egypt	44,921,341,018	+35.8%	43
Spain	107,774,094,169	+4.54%	25
Estonia	2,074,707,140	-20%	108
Ethiopia	3,784,387,425	+86.6%	96
Finland	17,992,736,653	+6.28%	64
Fiji	1,599,606,710	+3.31%	112
France	282,857,002,814	+17.5%	12
United Kingdom	174,597,843,325	-1.86%	18
Georgia	4,447,216,641	-11.1%	93
Greece	15,221,540,695	+11.9%	66
Grenada	423,263,144	+4.73%	127
Guatemala	24,412,418,012	+14.6%	57
Guyana	1,009,772,164	+12.8%	116
Honduras	8,036,257,489	+6.53%	78
Croatia	3,335,632,833	+5.02%	101
Haiti	2,717,628,883	+5.07%	105
Hungary	46,422,357,554	+1.54%	41
Indonesia	155,708,275,548	+6.39%	19
India	643,042,560,626	+2.43%	5
Ireland	12,697,664,212	-1.61%	72
Iraq	100,690,954,782	-10.3%	27
Iceland	6,402,950,469	+10.2%	83
Israel	214,543,676,670	+4.83%	17
Italy	290,547,246,726	+17.4%	11
Jordan	21,939,099,362	+15.1%	60
Japan	1,230,666,979,042	-4.94%	2
Kazakhstan	45,807,523,139	+27.4%	42
Kenya	10,066,607,177	+37.1%	73
Kyrgyzstan	5,088,723,594	+57.2%	91
Cambodia	22,505,568,573	+12.6%	58
St. Kitts & Nevis	294,747,745	+3.03%	134
South Korea	418,218,648,902	-0.644%	7
Kuwait	50,727,942,998	-3.59%	40
Lebanon	33,301,277,510	+21.1%	53
Libya	92,893,708,178	+0.505%	28
St. Lucia	406,063,791	-4.3%	128
Sri Lanka	6,093,946,546	+38.3%	85
Lesotho	1,007,862,057	+18%	117
Lithuania	7,406,410,382	+20.1%	79
Luxembourg	2,789,495,616	-6.31%	103
Latvia	5,141,113,061	+3.72%	90
Macao SAR China	29,391,736,000	+5.84%	56
Morocco	37,133,907,723	+2.22%	51
Moldova	5,483,515,867	+0.558%	88
Madagascar	2,784,591,093	+5.8%	104
Maldives	673,885,913	+14.1%	122
Mexico	232,035,392,465	+8.27%	15
North Macedonia	5,251,936,214	+4.72%	89
Malta	1,417,638,672	+15.9%	113
Montenegro	1,741,494,537	+10.7%	110
Mongolia	5,507,944,031	+12.1%	87
Mozambique	3,842,855,301	+5.66%	95
Mauritius	8,506,134,285	+17.4%	77
Malaysia	116,229,205,258	+2.44%	23
Namibia	3,355,524,015	+13.5%	100
Nigeria	38,612,493,467	+20.5%	50
Nicaragua	6,104,867,094	+12.1%	84
Netherlands	79,129,152,086	+13.3%	33
Norway	81,241,559,662	+0.972%	31
New Zealand	22,065,156,554	+42.5%	59
Oman	18,286,526,118	+4.77%	63
Pakistan	18,407,745,861	+34.1%	62
Panama	6,855,505,288	+1.46%	81
Peru	79,246,042,708	+11%	32
Philippines	106,195,050,244	+2.36%	26
Poland	223,114,909,804	+15.1%	16
Portugal	42,434,182,604	+20.4%	47
Palestinian Territories	1,328,000,000	+0.363%	114
Qatar	53,987,415,590	+4.75%	39
Romania	73,391,248,091	+0.536%	34
Rwanda	2,406,450,702	+31.2%	106
Saudi Arabia	463,869,589,071	+1.29%	6
Singapore	383,945,953,551	+6.7%	8
El Salvador	3,704,746,691	+20.3%	97
Serbia	30,484,164,151	+10.6%	54
Suriname	1,632,317,813	+21.3%	111
Slovakia	14,451,734,758	+28%	67
Slovenia	2,832,429,185	+19.5%	102
Sweden	62,569,356,007	+2.8%	36
Seychelles	773,677,576	+13.3%	120
Thailand	236,933,974,075	+5.55%	14
Timor-Leste	736,966,607	-5.76%	121
Tonga	377,298,749	-4.85%	129
Trinidad & Tobago	5,601,120,709	-10.5%	86
Tunisia	9,343,597,098	+1.12%	75
Turkey	154,773,780,178	+9.87%	20
Ukraine	43,780,549,773	+8.07%	45
Uruguay	17,377,698,943	+6.9%	65
United States	910,036,546,652	+17.7%	3
Uzbekistan	41,236,890,133	+19.3%	49
St. Vincent & Grenadines	316,823,949	+12.9%	133
Vietnam	83,081,854,928	-9.93%	29
Vanuatu	614,649,779	-4.52%	123
Samoa	507,740,281	+13.6%	124
Kosovo	1,310,096,687	+5.19%	115
South Africa	65,434,957,159	+4.71%	35
Zimbabwe	484,972,805	+320%	126

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 = 'FI.RES.TOTL.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 <- 'FI.RES.TOTL.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

Total reserves (includes gold, current US$)

Year: 2024

Acquire full dataset with Python or R from source