Country

Indicator

Value type

Values present

Total reserves in months of imports

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	5.51	+8.34%	37
Albania	6.04	-10.2%	28
Argentina	3.59	+45.1%	61
Armenia	2.13	-21.2%	77
Antigua & Barbuda	3.04	-4.16%	68
Australia	1.37	-4.83%	90
Austria	1.28	+14.8%	94
Azerbaijan	4.66	-14.7%	47
Belgium	0.79	+3.95%	97
Bangladesh	3.21	-5.25%	64
Bulgaria	7.59	-5.93%	14
Bahrain	1.42	-7.9%	87
Bosnia & Herzegovina	6.42	-3.44%	25
Belarus	1.98	+4.33%	81
Belize	3.19	-4.41%	65
Brazil	8.11	-14.1%	11
Brunei	5.51	+0.734%	38
Bhutan	6.55	+62.8%	23
Canada	1.57	-0.411%	86
Switzerland	13.2	+1.85%	3
Chile	4.15	-5.19%	56
China	11.2	-3.53%	5
Colombia	7.3	+1.91%	19
Cape Verde	5.98	-10.9%	29
Costa Rica	4.26	-1.89%	55
Cyprus	0.365	+13.4%	102
Czechia	7.03	-0.874%	20
Germany	2.18	+16.8%	76
Djibouti	0.85	-23%	96
Dominica	4.73	-8.43%	46
Denmark	4.51	-5.1%	50
Dominican Republic	3.65	-19.2%	59
Ecuador	2.2	+59.4%	75
Spain	1.83	+0.0346%	85
Estonia	0.679	-21.4%	100
Finland	1.4	+8.08%	88
France	2.38	+15.4%	72
United Kingdom	1.22	-3.63%	95
Georgia	2.33	-14.2%	74
Greece	1.31	+8.18%	93
Grenada	4.39	-1.46%	53
Guatemala	7.39	+6.71%	17
Honduras	4.46	+3.54%	51
Croatia	0.706	-2.29%	99
Hungary	2.9	+5.89%	69
Indonesia	5.74	+0.268%	32
India	7.52	-5.67%	15
Iceland	4.92	+0.325%	43
Israel	15.7	+3.83%	2
Italy	4	+18.9%	57
Japan	13	-3.48%	4
Kazakhstan	5.49	+31.4%	39
Cambodia	7.65	-1.53%	13
St. Kitts & Nevis	5.05	+6.61%	42
South Korea	6.25	-0.868%	26
Kuwait	8.95	-2.35%	10
St. Lucia	2.78	-16%	71
Lesotho	5.52	+17.3%	36
Lithuania	1.39	+18.8%	89
Luxembourg	0.0596	-10.1%	104
Latvia	1.86	+5.64%	84
Moldova	5.77	-5.87%	30
Maldives	1.32	+7.68%	92
Mexico	3.6	+3.66%	60
North Macedonia	4.58	+4.66%	48
Malta	0.352	+12.8%	103
Montenegro	3.47	+1.98%	62
Mozambique	3.44	+5.21%	63
Malaysia	4.45	-6.85%	52
Namibia	3.91	+3.81%	58
Nigeria	6.79	+38.5%	21
Nicaragua	5.72	+2.01%	34
Netherlands	0.727	+13.9%	98
Norway	4.56	-2.37%	49
New Zealand	3.12	+40.7%	67
Pakistan	2.86	+14.2%	70
Panama	2.07	+12.1%	79
Peru	10.5	+2.89%	7
Philippines	7.33	-3.79%	18
Poland	5.38	+10.7%	40
Portugal	3.15	+17.8%	66
Palestinian Territories	1.91	+41.3%	83
Qatar	6.72	+3.75%	22
Romania	4.91	-3.53%	44
Saudi Arabia	15.9	-8.69%	1
Singapore	4.29	-2.08%	54
El Salvador	2.11	+12.1%	78
Suriname	6.51	+3.57%	24
Slovakia	1.35	+27%	91
Slovenia	0.578	+16.5%	101
Sweden	1.98	-0.0549%	80
Thailand	7.44	-1.7%	16
Timor-Leste	5.73	-9.17%	33
Tonga	11	-8.11%	6
Trinidad & Tobago	6.07	-19.8%	27
Turkey	4.73	+13.7%	45
Ukraine	5.21	+3.28%	41
Uruguay	7.76	+7.46%	12
United States	1.97	+9.49%	82
Uzbekistan	10.3	+16.4%	8
St. Vincent & Grenadines	5.76	+2.3%	31
Vietnam	2.37	-21.3%	73
Samoa	9.8	+10.9%	9
South Africa	5.68	+6.41%	35

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.MO'

# 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.MO'

# 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 in months of imports

Year: 2024

Acquire full dataset with Python or R from source