Country

Indicator

Value type

Values present

Commercial service imports (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	8,234,605,089	-0.751%	64
Albania	3,765,288,082	+9.54%	76
Argentina	22,389,228,211	+0.506%	43
Armenia	3,804,276,891	+14%	75
Antigua & Barbuda	556,625,705	+5.11%	100
Australia	107,265,673,951	+7.35%	19
Austria	87,771,909,918	+4.25%	23
Azerbaijan	10,059,496,000	+18.2%	61
Belgium	158,903,891,769	-0.336%	13
Bangladesh	10,931,048,370	+1.93%	57
Bulgaria	8,195,170,000	+4.59%	65
Bahrain	12,373,936,170	+2.58%	56
Bahamas	2,200,681,164	+11.5%	86
Bosnia & Herzegovina	1,294,378,895	+15.8%	92
Belarus	6,481,623,115	+9.97%	67
Belize	332,416,874	+17.5%	103
Brazil	101,356,219,622	+17.3%	20
Brunei	1,716,262,788	+6.47%	91
Bhutan	220,693,128	-30%	107
Canada	158,674,010,334	+5.14%	14
Switzerland	214,434,138,636	+11.6%	9
Chile	20,640,602,016	+0.937%	46
China	609,936,333,483	+14.1%	2
Colombia	18,224,323,591	+8.08%	49
Cape Verde	280,786,679	+7.34%	105
Costa Rica	7,255,536,449	+14.3%	66
Cyprus	21,915,214,314	+11%	44
Czechia	37,954,627,700	+6.8%	34
Germany	549,751,651,762	+5.85%	3
Djibouti	694,213,143	-7.91%	98
Dominica	156,768,691	+3.35%	111
Denmark	121,031,591,668	+7.15%	17
Dominican Republic	6,156,500,000	+12%	70
Ecuador	5,992,132,506	-1.3%	71
Spain	111,558,518,699	+16.4%	18
Estonia	10,303,420,799	+4.97%	60
Finland	46,687,293,373	+2.32%	32
France	340,400,928,893	+2.7%	6
United Kingdom	398,926,373,685	+11%	4
Georgia	3,760,067,888	+5.72%	77
Gambia	160,904,229	+7.13%	110
Greece	30,809,193,030	+5.48%	38
Grenada	424,380,947	+9.43%	101
Guatemala	6,301,472,420	+14.4%	68
Hong Kong SAR China	90,279,136,204	+14.5%	22
Honduras	3,610,668,929	+2.1%	78
Croatia	8,297,070,066	+17.7%	63
Hungary	27,189,929,339	+1.75%	41
Indonesia	57,432,000,333	+12.1%	29
India	195,431,413,497	+10.2%	10
Iceland	4,978,773,363	+7.04%	74
Israel	43,617,700,000	-6.36%	33
Italy	161,485,111,975	+6.94%	11
Jamaica	3,395,848,724	-0.2%	79
Japan	243,363,950,040	+5.6%	8
Kazakhstan	12,872,858,597	+5.85%	55
Cambodia	3,038,667,313	+7.51%	81
St. Kitts & Nevis	236,660,129	-18.9%	106
South Korea	161,441,300,000	+6.69%	12
Kuwait	27,015,428,741	-3.02%	42
St. Lucia	597,609,900	+10.8%	99
Lesotho	407,858,725	+0.287%	102
Lithuania	14,573,097,266	+9.53%	53
Luxembourg	130,392,465,855	+2.68%	15
Latvia	5,890,292,808	+2.74%	72
Moldova	1,741,520,000	+15.3%	90
Maldives	1,869,380,242	+11.2%	89
Mexico	70,158,470,103	-6.08%	27
North Macedonia	1,972,054,571	-3.34%	88
Malta	17,178,678,497	+9.86%	51
Montenegro	1,174,135,088	+2.33%	95
Mozambique	2,042,086,286	+4.57%	87
Malaysia	56,149,019,149	+8.04%	30
Namibia	2,395,331,496	+9.12%	85
Nigeria	17,291,350,788	+0.227%	50
Nicaragua	1,279,000,000	+15.9%	93
Netherlands	267,773,237,151	+5.84%	7
Norway	64,120,137,433	+6.97%	28
Nepal	2,781,089,285	+36.8%	82
New Zealand	19,320,700,170	+5.61%	48
Pakistan	10,541,574,086	+12.6%	58
Panama	5,649,890,538	-2.8%	73
Peru	14,863,962,407	+9.12%	52
Philippines	37,021,799,002	+24.3%	35
Poland	75,043,000,000	+13.7%	25
Portugal	27,637,206,771	+6.95%	40
Paraguay	2,522,036,755	+1.52%	84
Palestinian Territories	1,231,266,408	-33.1%	94
Qatar	32,090,934,066	-15%	37
Romania	30,524,525,800	+7.31%	39
Russia	80,167,620,000	+6.04%	24
Saudi Arabia	93,652,543,273	+5.97%	21
Singapore	350,835,283,259	+7.61%	5
Solomon Islands	219,269,515	-3.39%	108
El Salvador	3,202,942,676	+22.7%	80
Suriname	919,333,748	+45.9%	97
Slovakia	13,061,923,693	+5.85%	54
Slovenia	9,479,493,300	+7.07%	62
Sweden	124,691,209,652	+9.36%	16
Thailand	73,330,899,098	+12.2%	26
Tajikistan	964,123,779	+30%	96
Timor-Leste	331,302,793	-14.5%	104
Tonga	152,317,479	+7.77%	112
Trinidad & Tobago	2,613,177,885	+2.13%	83
Turkey	52,004,000,000	+8.06%	31
Ukraine	21,547,000,000	-7.04%	45
Uruguay	6,161,013,934	-3.2%	69
United States	786,596,000,000	+8.84%	1
Uzbekistan	10,428,155,997	+28.1%	59
St. Vincent & Grenadines	193,752,662	+5.21%	109
Vietnam	36,183,000,000	+24.4%	36
Samoa	119,754,141	-3.17%	113
South Africa	19,523,988,884	+6.66%	47

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 = 'TM.VAL.SERV.CD.WT'

# 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 <- 'TM.VAL.SERV.CD.WT'

# 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

Commercial service imports (current US$)

Year: 2024

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