Country

Indicator

Value type

Values present

Trade (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	62.3	-7.02%	92
Albania	79.1	-4.15%	71
Argentina	28.2	+5.7%	130
Armenia	152	+27.1%	14
Australia	47.3	-3.88%	111
Austria	111	-5.37%	34
Azerbaijan	82.7	-1.01%	64
Belgium	158	-6.22%	10
Benin	40.6	-9.01%	121
Burkina Faso	63.4	-2.15%	88
Bangladesh	26.8	-13.6%	132
Bulgaria	109	-8.67%	36
Bahamas	79.2	+3.52%	70
Bosnia & Herzegovina	100	+0.358%	45
Belarus	132	+0.253%	27
Bermuda	80.5	+9.41%	68
Brazil	35.5	+5.54%	128
Brunei	133	-2.66%	26
Botswana	67	-2.66%	83
Central African Republic	47.8	+9.99%	109
Canada	65.2	-2.18%	85
Switzerland	134	-0.905%	24
Chile	63.9	+5.16%	87
China	37.2	+3.03%	125
Côte d’Ivoire	54.7	+5.12%	100
Cameroon	35.9	-3.58%	127
Congo - Kinshasa	97.5	+7.83%	48
Congo - Brazzaville	93.2	-3.14%	56
Colombia	36.9	-8.66%	126
Comoros	44.4	-2.87%	114
Cape Verde	95.1	+0.544%	52
Costa Rica	71.3	-1.57%	77
Cyprus	190	-1.8%	7
Czechia	132	-0.783%	28
Germany	80.3	-2.96%	69
Djibouti	309	+8.62%	4
Denmark	129	+0.684%	30
Dominican Republic	51.8	+3.89%	105
Ecuador	57.2	+0.36%	96
Egypt	39.6	-2.1%	123
Spain	70.3	-2.61%	79
Estonia	152	-1.86%	15
Ethiopia	17.3	-15.9%	135
Finland	82.5	-3.98%	65
France	67.2	-4.73%	82
Micronesia (Federated States of)	96.6	-1.75%	49
Gabon	94.5	+3.83%	54
United Kingdom	62.4	-4.04%	91
Georgia	104	-2.63%	39
Ghana	69.4	+7.71%	81
Guinea	100	+4.45%	44
Gambia	43.7	+3.07%	115
Guinea-Bissau	40.7	-2.28%	120
Equatorial Guinea	60.5	-1.47%	95
Greece	89.3	-3.08%	59
Guatemala	47.3	-1.97%	110
Hong Kong SAR China	359	+1.82%	2
Honduras	91.1	-7.78%	58
Croatia	103	-4.63%	41
Haiti	22.2	-27.7%	134
Hungary	144	-8.48%	16
Indonesia	42.6	+2.99%	116
India	44.7	-0.715%	113
Ireland	253	+6.64%	5
Iran	49.7	-4.72%	108
Iraq	74.7	+7.59%	75
Iceland	84.3	-2.78%	62
Israel	54.5	-6.1%	101
Italy	63.2	-3.75%	89
Jordan	99.7	-0.666%	46
Kenya	30.3	-5.61%	129
Cambodia	143	+6.89%	17
Kiribati	101	-5.98%	42
Libya	134	-14%	25
Sri Lanka	42.4	-2.62%	117
Lithuania	143	-4.02%	18
Luxembourg	398	-1.54%	1
Latvia	132	-3.53%	29
Macao SAR China	135	-4.85%	23
Morocco	95.8	+2.1%	50
Moldova	88.7	-5.67%	60
Madagascar	55	-10.1%	98
Mexico	74.7	+1.28%	76
North Macedonia	138	-6.86%	20
Mali	50.9	-11.7%	107
Malta	230	-1.26%	6
Montenegro	112	-5.16%	32
Mongolia	139	-2.78%	19
Mozambique	95.7	-9.43%	51
Mauritius	104	-1.05%	40
Malaysia	137	+4.01%	21
Namibia	110	-1.03%	35
Niger	52	+11.8%	104
Nicaragua	98.5	-6.17%	47
Netherlands	156	-5.87%	13
Norway	81.2	+1.07%	67
Nepal	40.5	-2.5%	122
Nauru	158	+9.94%	11
Pakistan	27.5	-3.93%	131
Peru	51.5	+1.36%	106
Philippines	65.9	-2.28%	84
Poland	101	-8.52%	43
Puerto Rico	94.8	-6.86%	53
Portugal	91.1	-2.9%	57
Paraguay	76.8	-6.76%	74
Palestinian Territories	81.3	+2.24%	66
Romania	77.3	-6.98%	73
Russia	39.5	-3.25%	124
Rwanda	70	+7.78%	80
Saudi Arabia	54.8	+1.17%	99
Sudan	2.46	-0.493%	136
Senegal	71.2	-0.274%	78
Singapore	322	-1.02%	3
Sierra Leone	64.5	+15.4%	86
El Salvador	84.7	+3.6%	61
Somalia	94.4	+1.81%	55
Serbia	111	-2.65%	33
Slovakia	170	-5.96%	9
Slovenia	156	-2.26%	12
Sweden	105	-1.81%	38
Seychelles	188	+4.17%	8
Chad	45.4	-3.38%	112
Togo	62.5	+1.7%	90
Thailand	137	+6.17%	22
Tunisia	105	-7.02%	37
Turkey	55.8	-15.8%	97
Tanzania	41.5	+8.64%	118
Uganda	41.5	+24.4%	119
Ukraine	77.8	+0.314%	72
Uruguay	52.5	-0.843%	103
United States	24.9	-0.0457%	133
Uzbekistan	60.7	-8.14%	94
Samoa	83.1	-8.3%	63
Kosovo	114	+3.92%	31
South Africa	61.7	-5.34%	93
Zimbabwe	52.7	+3.7%	102

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 = 'NE.TRD.GNFS.ZS'

# 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 <- 'NE.TRD.GNFS.ZS'

# 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

Trade (% of GDP)

Year: 2024

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