Country

Indicator

Value type

Values present

Transport services (% of commercial service imports)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	37.3	+10.9%	28
Albania	10.5	-15.2%	110
Argentina	21.1	-13.1%	75
Armenia	32.7	+2.56%	39
Antigua & Barbuda	21.5	-5.2%	72
Australia	17	-5.06%	96
Austria	24.3	-3.36%	62
Azerbaijan	18.6	-9.05%	87
Belgium	19	-2.57%	86
Bangladesh	63.8	+1.73%	5
Bulgaria	28.9	-2.03%	47
Bahrain	33.4	-1.15%	36
Bahamas	24.3	-6.42%	61
Bosnia & Herzegovina	33.8	-7.95%	35
Belize	39.2	+0.434%	25
Brazil	22.3	-0.816%	68
Brunei	17.3	-4.41%	94
Bhutan	21.6	-19.1%	71
Canada	17.4	+1.48%	93
Switzerland	15.9	+3.86%	102
Chile	36	+0.928%	31
China	27.6	-7.6%	48
Colombia	25.1	+1.32%	59
Cape Verde	28.9	-10%	46
Costa Rica	23.4	-1.93%	64
Cyprus	17	-6.65%	95
Czechia	22	-6.77%	69
Germany	20	-2.7%	80
Djibouti	91.9	+1.05%	1
Dominica	23	-9.51%	65
Denmark	40.6	-1.29%	21
Dominican Republic	45.8	-4.07%	14
Ecuador	35.4	+2.76%	32
Spain	12.7	+9.89%	107
Estonia	26.5	-2.04%	56
Finland	16.5	-3.8%	101
France	18	-3.4%	91
United Kingdom	9.13	-16.1%	111
Georgia	60.5	-3.65%	6
Gambia	66.7	-9.96%	3
Greece	63.9	-3.21%	4
Grenada	21.7	-3.26%	70
Guatemala	46.4	+2.5%	13
Hong Kong SAR China	22.9	+0.74%	66
Honduras	54.6	-2.76%	8
Croatia	14.6	+0.257%	105
Hungary	27.1	-3.32%	51
Indonesia	24.9	-1.87%	60
India	17.6	+5.96%	92
Iceland	18.1	-3.1%	90
Israel	16.8	-9.88%	100
Italy	21.2	+4.2%	74
Jamaica	47.1	-1.33%	11
Japan	14.9	-1.47%	104
Kazakhstan	25.9	-9.12%	57
Cambodia	49.7	+2.78%	10
St. Kitts & Nevis	29.2	+29.9%	45
South Korea	22.4	-0.878%	67
Kuwait	20.8	+4.58%	77
St. Lucia	26.6	+2.78%	54
Lesotho	12.5	+1.47%	109
Lithuania	46.5	+0.409%	12
Luxembourg	16.9	-1.36%	98
Latvia	24.2	-4.63%	63
Moldova	39.9	+1.31%	23
Maldives	26.6	-10.3%	55
Mexico	35.2	-8.11%	33
North Macedonia	26.9	+8.36%	52
Malta	18.5	+2.5%	89
Montenegro	53.2	-9.42%	9
Mozambique	38.1	-12%	26
Malaysia	25.7	-1.97%	58
Namibia	13.4	+1.4%	106
Nigeria	36.9	-10.4%	30
Nicaragua	57.8	-0.365%	7
Netherlands	19.3	-3.52%	85
Norway	19.4	+0.581%	84
Nepal	19.7	-31.1%	82
New Zealand	19.8	-12.1%	81
Pakistan	44.2	-4.35%	18
Panama	45.4	-6.5%	16
Peru	33.1	+3.12%	37
Philippines	20.1	-19.6%	79
Poland	21	+0.361%	76
Portugal	21.3	-9.11%	73
Paraguay	45.8	+19.9%	15
Palestinian Territories	41.9	+24.2%	20
Qatar	37.2	+6.18%	29
Romania	16.9	+2.16%	99
Russia	18.5	-8.07%	88
Saudi Arabia	30.1	+0.886%	44
Singapore	34.6	+1.18%	34
Solomon Islands	32.8	-2.41%	38
El Salvador	31.6	-11.8%	40
Suriname	17	-10.2%	97
Slovakia	30.2	-2.6%	43
Slovenia	20.2	+4.36%	78
Sweden	12.5	-0.651%	108
Thailand	31.4	-5.92%	41
Tajikistan	70	-6.09%	2
Timor-Leste	27.5	+29.1%	49
Tonga	31.2	-10.4%	42
Trinidad & Tobago	27.3	+8.86%	50
Turkey	40	+3.89%	22
Ukraine	15.5	+21.9%	103
Uruguay	26.9	+10.2%	53
United States	19.5	-1.2%	83
Uzbekistan	44.3	-12.6%	17
St. Vincent & Grenadines	42.7	+2.25%	19
Samoa	39.8	-9.96%	24
South Africa	37.9	-7.24%	27

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.TRAN.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.TRAN.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

Transport services (% of commercial service imports)

Year: 2024

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