Country

Indicator

Value type

Values present

Goods imports (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	14,190,242,036	-5.93%	78
Albania	7,867,974,155	+14.1%	86
Argentina	57,400,134,287	-17.7%	47
Armenia	15,239,727,137	+36.5%	72
Antigua & Barbuda	725,601,862	-2.46%	106
Australia	296,670,368,021	+3.04%	22
Austria	196,483,640,270	-6.12%	28
Azerbaijan	17,167,084,000	+4.7%	70
Belgium	372,036,034,435	-3.85%	16
Bangladesh	63,646,940,434	+2.39%	44
Bulgaria	51,824,390,000	+0.997%	50
Bahrain	20,670,478,723	+1.77%	69
Bahamas	4,595,812,200	+12.8%	94
Bosnia & Herzegovina	14,900,927,506	+4.61%	76
Belarus	44,197,200,000	+6.33%	53
Belize	1,362,002,497	+7.63%	101
Brazil	274,014,105,305	+8.93%	24
Brunei	7,364,290,807	-0.954%	88
Bhutan	1,285,148,638	-11.2%	102
Canada	573,716,277,612	+0.496%	12
Switzerland	367,937,035,062	+1.09%	17
Chile	78,132,721,098	-1.3%	41
China	2,641,014,638,229	+2.16%	2
Colombia	60,246,456,266	+1.34%	46
Cape Verde	1,173,415,469	+2.02%	103
Costa Rica	23,165,812,492	+5.09%	67
Cyprus	11,798,185,081	-7.35%	80
Czechia	178,723,425,528	-2.6%	30
Germany	1,222,500,311,104	-2.96%	3
Djibouti	4,035,074,969	-9.97%	96
Dominica	230,750,580	-13.1%	113
Denmark	131,755,999,137	+1.07%	31
Dominican Republic	29,808,000,000	+3.45%	61
Ecuador	27,885,990,172	-4.75%	64
Spain	456,943,173,913	-0.0312%	14
Estonia	22,032,747,078	+0.429%	68
Finland	75,916,500,732	-5.7%	42
France	733,998,706,760	-3.68%	5
United Kingdom	756,074,589,754	+0.748%	4
Georgia	15,091,738,288	+6.3%	74
Gambia	1,388,324,740	+15.4%	100
Greece	91,195,123,262	+1.55%	40
Grenada	561,823,406	+5.09%	108
Guatemala	29,131,502,500	+6.26%	63
Hong Kong SAR China	633,117,545,750	+6.85%	8
Honduras	14,576,581,969	+1.65%	77
Croatia	41,510,922,436	+4.53%	55
Hungary	126,682,035,407	-7.01%	33
Indonesia	221,906,967,729	+4.96%	26
India	726,403,274,000	+6.65%	6
Iceland	9,295,876,486	+3.79%	83
Israel	96,529,300,000	+3.17%	39
Italy	554,479,564,416	-5.62%	13
Jamaica	6,067,023,925	-5.22%	92
Japan	718,736,498,496	-5.86%	7
Kazakhstan	61,196,257,533	+1.3%	45
Cambodia	31,247,259,146	+17.7%	60
St. Kitts & Nevis	404,074,074	+7.7%	111
South Korea	596,069,200,000	-1.63%	11
Kuwait	33,443,405,536	+1.26%	57
St. Lucia	848,067,224	+12.7%	104
Lesotho	1,667,729,170	+0.279%	98
Lithuania	43,808,713,592	-1.55%	54
Luxembourg	29,372,880,882	-0.834%	62
Latvia	23,318,933,769	-3.32%	66
Moldova	8,633,440,000	+4.09%	84
Maldives	3,459,112,657	+4.96%	97
Mexico	626,009,957,423	+4.49%	9
North Macedonia	10,634,426,651	-0.353%	81
Malta	7,305,121,656	+4.62%	89
Montenegro	4,287,309,623	+7.04%	95
Mozambique	8,375,147,256	-8.76%	85
Malaysia	222,719,569,106	+10.5%	25
Namibia	6,770,501,180	+9.1%	91
Nigeria	39,804,659,168	-16.6%	56
Nicaragua	10,131,300,000	+8.01%	82
Netherlands	616,112,851,118	-3.72%	10
Norway	98,256,342,559	+2.27%	38
Nepal	14,963,045,332	+26.5%	75
New Zealand	48,531,180,110	-2.88%	51
Pakistan	55,675,000,000	+15.2%	48
Panama	25,140,620,448	-16.3%	65
Peru	52,091,353,653	+4.27%	49
Philippines	123,755,972,628	+2.03%	34
Poland	366,769,000,000	+2.56%	18
Portugal	109,270,099,255	+1.46%	35
Paraguay	15,835,333,922	+3.21%	71
Palestinian Territories	6,873,746,159	-28.7%	90
Qatar	32,606,868,132	+10.8%	59
Romania	128,926,917,889	+3.23%	32
Russia	300,124,360,000	-0.977%	21
Saudi Arabia	215,347,005,775	+12.2%	27
Singapore	434,897,996,857	+8.12%	15
Solomon Islands	608,712,649	-3.3%	107
El Salvador	15,095,066,348	+5.01%	73
Suriname	1,650,652,915	+4.99%	99
Slovakia	107,225,915,359	-0.158%	36
Slovenia	44,992,287,131	+1.53%	52
Sweden	184,560,754,086	-2.82%	29
Thailand	277,775,025,939	+6.28%	23
Tajikistan	5,936,267,740	+14.5%	93
Timor-Leste	840,156,775	+8.71%	105
Tonga	231,822,618	-0.765%	112
Trinidad & Tobago	7,505,416,430	+13.4%	87
Turkey	313,765,000,000	-6.97%	20
Ukraine	69,291,000,000	+8.58%	43
Uruguay	12,911,731,047	+0.486%	79
United States	3,296,238,000,000	+6.04%	1
Uzbekistan	33,159,965,244	-3.82%	58
St. Vincent & Grenadines	444,863,515	+10.6%	110
Vietnam	362,489,000,000	+16.7%	19
Samoa	447,718,802	+4.29%	109
South Africa	99,828,489,498	-4.8%	37

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 = 'BM.GSR.MRCH.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 <- 'BM.GSR.MRCH.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

Goods imports (BoP, current US$)

Year: 2024

Acquire full dataset with Python or R from source