Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	63.3	+6.54%	24
Albania	21.4	-12.4%	84
Argentina	57.6	+11.5%	36
Armenia	36.5	+42.2%	63
Antigua & Barbuda	11.5	+99.1%	95
Australia	31.1	+14.6%	70
Austria	48.5	+7.45%	47
Azerbaijan	35.5	+22.8%	64
Belgium	72.1	+14.8%	12
Bangladesh	64	-5.68%	23
Bulgaria	57.9	+15.3%	35
Bahrain	54.1	+121%	40
Bahamas	3.76	-20.8%	108
Bosnia & Herzegovina	30	+4.07%	72
Belize	22	+6.02%	83
Brazil	68.8	+7.03%	18
Brunei	11	-46.6%	98
Bhutan	10.2	+1,122%	101
Canada	57.9	+16%	34
Switzerland	76	+45.6%	9
Chile	37.6	+29%	62
China	60.1	-5.17%	32
Colombia	34.4	+2.89%	66
Cape Verde	9.13	+0.295%	105
Costa Rica	63	-0.199%	25
Cyprus	69.8	+46.3%	16
Czechia	56.9	+3.06%	37
Germany	70.2	+23.2%	15
Djibouti	9.56	-28.2%	104
Dominica	33.1	+2.42%	68
Denmark	39.2	+5.61%	59
Dominican Republic	16.3	+33.2%	91
Ecuador	11	+44.5%	97
Spain	42.5	+12.3%	57
Estonia	66.8	+3.66%	20
Finland	76.8	+4.55%	8
France	64.2	+24.6%	22
United Kingdom	80.4	+47.7%	4
Georgia	19.1	+18.4%	86
Gambia	1.21	+21.3%	111
Greece	16.5	+42.9%	90
Grenada	12.2	+12.1%	93
Guatemala	49	+2.26%	46
Hong Kong SAR China	46.4	+127%	52
Honduras	75.1	-2.16%	10
Croatia	26.8	+0.547%	79
Hungary	53.9	+6.4%	41
Indonesia	42.7	+11.4%	56
India	81.7	+4.46%	3
Iceland	23.5	+13.7%	80
Israel	87.4	+1.25%	1
Italy	51.1	+16.1%	43
Jamaica	11.9	+3.86%	94
Japan	61.7	+4.91%	28
Kazakhstan	29.7	+54.5%	73
Cambodia	11.5	-9.75%	96
St. Kitts & Nevis	9.68	+94.4%	102
South Korea	58.7	+7.35%	33
Kuwait	64.5	+19.7%	21
St. Lucia	4.24	+2.49%	107
Lesotho	28.9	+14.6%	76
Lithuania	44.7	+19.5%	55
Luxembourg	84.1	+200%	2
Latvia	51.7	+3.66%	42
Moldova	49.7	+0.984%	44
Maldives	1.3	-7.7%	110
Mexico	31.2	+51.8%	69
North Macedonia	60.5	+4.69%	31
Malta	68.3	+2.26%	19
Montenegro	20.3	+15.6%	85
Mozambique	3.51	+88.5%	109
Malaysia	46.1	-1.13%	53
Namibia	38.7	+0.626%	60
Nigeria	38.6	+476%	61
Nicaragua	49.1	+15.8%	45
Netherlands	77.6	+10.5%	6
Norway	48.4	+22.1%	48
Nepal	30.2	+2,870%	71
New Zealand	34.8	+2.23%	65
Pakistan	77.2	+11.1%	7
Panama	16.6	+223%	89
Peru	17.9	+19.7%	87
Philippines	74.4	+0.634%	11
Poland	60.8	+8.43%	30
Portugal	33.4	+7.49%	67
Paraguay	47.5	+3.2%	51
Palestinian Territories	70.4	+37.5%	14
Qatar	10.9	+97.7%	99
Romania	63	+2.85%	26
Russia	47.8	+9.38%	49
Saudi Arabia	9.62	+145%	103
Singapore	61.2	+29.8%	29
Solomon Islands	28.3	-10.9%	77
El Salvador	29	+2.17%	75
Suriname	62.5	+4.84%	27
Slovakia	56.4	+3.57%	39
Slovenia	45.2	+9.54%	54
Sweden	78.3	+8.02%	5
Thailand	29.5	-13.7%	74
Tajikistan	40.7	-9.62%	58
Timor-Leste	6.25	+166%	106
Tonga	28.1	-5.37%	78
Trinidad & Tobago	23	+110%	82
Turkey	16.3	+26.5%	92
Ukraine	68.8	-0.0728%	17
Uruguay	56.8	+11.4%	38
United States	70.4	+38.2%	13
Uzbekistan	17.5	-8.34%	88
St. Vincent & Grenadines	10.4	+12.3%	100
Samoa	23.2	+29%	81
South Africa	47.6	+25.6%	50

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 = 'TX.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 <- 'TX.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 exports)

Year: 2024

Acquire full dataset with Python or R from source