Country

Indicator

Value type

Values present

Computer, communications and other services (% of commercial service imports)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	36.7	-13.7%	62
Albania	8.2	-16.4%	108
Argentina	40.4	+3.02%	52
Armenia	13.1	+25.3%	103
Antigua & Barbuda	51.7	+1.48%	24
Australia	37.8	+1.6%	59
Austria	52.4	-0.516%	23
Azerbaijan	62.4	-0.698%	11
Belgium	56.1	-2.88%	15
Bangladesh	13.5	+1.89%	102
Bulgaria	37.9	-5.61%	56
Bahrain	20.9	-2.08%	92
Bahamas	50.7	+2.38%	27
Bosnia & Herzegovina	22.3	-3.68%	91
Belize	22.6	-9.72%	89
Brazil	53.6	+2.63%	20
Brunei	47.8	-9.97%	35
Bhutan	20.1	+53.9%	93
Canada	46.3	-3.73%	40
Switzerland	69.3	-0.0568%	4
Chile	36.3	-1.14%	63
China	28.3	-5.68%	79
Colombia	32.4	-8.74%	74
Cape Verde	37.2	+1.09%	60
Costa Rica	39.2	+3.9%	54
Cyprus	54	+1.56%	19
Czechia	48	-0.256%	34
Germany	50.5	+1.98%	28
Djibouti	2.38	-0.416%	111
Dominica	43.2	-0.0365%	46
Denmark	48.3	+1.59%	32
Dominican Republic	23.5	+40.5%	85
Ecuador	26.9	-13.9%	82
Spain	53.5	-0.613%	21
Estonia	56.4	-0.501%	14
Finland	65.8	-0.942%	5
France	54.6	-1.7%	17
United Kingdom	51	-0.426%	25
Georgia	19.1	+18.4%	94
Gambia	6.47	+135%	110
Greece	17.4	+10.7%	96
Grenada	62.4	+0.886%	10
Guatemala	18.1	-9.92%	95
Hong Kong SAR China	33.2	-7.75%	72
Honduras	22.3	+2.43%	90
Croatia	49	-9.28%	31
Hungary	54.2	+1.49%	18
Indonesia	41.4	-0.429%	49
India	60.6	+0.363%	12
Iceland	41.4	+0.728%	50
Israel	62.4	+5.71%	9
Italy	47	-0.774%	37
Jamaica	37.8	-0.755%	58
Japan	65.8	-2.13%	6
Kazakhstan	37.9	-1.37%	57
Cambodia	14.6	-3.51%	100
St. Kitts & Nevis	34.6	-30.6%	67
South Korea	55.9	+1.5%	16
Kuwait	23	+30.1%	87
St. Lucia	52.5	-3.58%	22
Lesotho	9.68	-5.52%	106
Lithuania	35.9	-2.86%	64
Luxembourg	34.3	-2.55%	68
Latvia	49.4	+0.383%	29
Moldova	23.7	+1.16%	84
Maldives	38.9	-2.01%	55
Mexico	31.1	-4.61%	76
North Macedonia	50.8	-4.01%	26
Malta	74.8	-0.603%	2
Montenegro	37.1	+10.9%	61
Mozambique	47.5	+21.5%	36
Malaysia	45.5	-1.35%	41
Namibia	80.1	-1.63%	1
Nigeria	28.2	-1.47%	80
Nicaragua	9.64	-11.3%	107
Netherlands	64.2	+0.0135%	7
Norway	48.2	+0.453%	33
Nepal	7.52	+7,258%	109
New Zealand	43.2	+1.56%	47
Pakistan	23.3	+12.5%	86
Panama	12.6	-0.816%	104
Peru	32.8	-1.59%	73
Philippines	33.4	-17.6%	71
Poland	58.1	+0.00858%	13
Portugal	46.8	+5.6%	38
Paraguay	22.9	+16.7%	88
Palestinian Territories	29.2	+19.9%	78
Qatar	14.6	-18.5%	99
Romania	45.3	-3.36%	43
Russia	31.3	-2.73%	75
Saudi Arabia	34	-6.26%	70
Singapore	49.3	-3.45%	30
Solomon Islands	44.8	+10.7%	44
El Salvador	16.1	-7.81%	97
Suriname	62.7	+3.68%	8
Slovakia	45.4	-0.843%	42
Slovenia	46.6	-1.76%	39
Sweden	73.5	+1.37%	3
Thailand	40.4	-0.452%	53
Tajikistan	14.2	-7.02%	101
Timor-Leste	27.8	-25.2%	81
Tonga	29.2	-6.4%	77
Trinidad & Tobago	35.8	-4.77%	65
Turkey	35.3	-2.13%	66
Ukraine	15.7	+53%	98
Uruguay	43.4	+0.311%	45
United States	40.6	-0.0468%	51
Uzbekistan	10.5	+40%	105
St. Vincent & Grenadines	25.3	-7.89%	83
Samoa	34.2	-8.74%	69
South Africa	43	+3.75%	48

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.OTHR.ZS.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.OTHR.ZS.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

Computer, communications and other services (% of commercial service imports)

Year: 2024

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