Country

Indicator

Value type

Values present

Container port traffic (TEU: 20 foot equivalent units)

Source: worldbank.org, 01.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Angola	654,590	+8.2%	70
United Arab Emirates	20,300,000	+5.83%	8
Argentina	1,667,161	-7.32%	50
Australia	9,375,992	+1.32%	22
Belgium	12,303,822	-6.04%	16
Bangladesh	3,200,236	-2.19%	39
Bulgaria	254,854	+3.95%	81
Bahamas	1,724,000	-3.84%	49
Brazil	11,683,239	-1%	17
Barbados	97,000	+3.3%	86
Canada	7,010,704	-2.22%	28
Chile	4,158,260	-4.09%	34
China	268,990,000	+4.69%	1
Congo - Brazzaville	999,999	-0.372%	63
Colombia	4,480,670	-8.59%	33
Cape Verde	82,871	+3.91%	88
Costa Rica	1,594,323	+4.67%	53
Cuba	261,080	+5.98%	80
Curaçao	96,000	+3.63%	87
Cayman Islands	40,829	-34.1%	89
Cyprus	371,740	+16.5%	78
Germany	13,733,867	-6.64%	13
Djibouti	635,000	-8.24%	71
Denmark	1,079,649	+2.38%	60
Dominican Republic	2,043,326	-6.47%	47
Ecuador	2,682,600	+1.92%	45
Egypt	7,765,482	+7.46%	27
Spain	17,161,676	-3.11%	10
Estonia	288,456	+27.1%	79
Finland	1,446,004	+2.62%	56
France	6,467,242	+1.07%	30
United Kingdom	9,738,676	-5.36%	21
Ghana	1,244,245	-22.5%	57
Greece	5,166,629	-15%	32
Guatemala	1,660,504	-4.32%	51
Hong Kong SAR China	16,573,000	-6.75%	11
Honduras	923,321	+11.9%	65
Croatia	544,015	+43.3%	72
Indonesia	12,380,584	-4.11%	14
India	19,717,168	+0.794%	9
Ireland	1,154,803	+1.03%	58
Israel	2,951,000	-4.06%	42
Italy	11,577,495	+2.43%	18
Jamaica	2,137,500	+8.21%	46
Japan	22,515,870	+0.724%	6
Kenya	1,450,000	+1.01%	55
South Korea	28,502,044	-4.31%	4
Sri Lanka	6,860,000	-5.38%	29
Lithuania	1,047,941	+57.2%	61
Latvia	471,684	+8.65%	74
Morocco	8,835,181	+4.47%	24
Mexico	8,307,884	+5.8%	26
Malta	2,993,799	-2.67%	41
Myanmar (Burma)	846,426	+22%	66
Mozambique	420,500	-4.56%	76
Mauritius	407,825	-6.55%	77
Malaysia	27,293,935	-3.42%	5
Namibia	160,368	-3.71%	83
Nigeria	1,566,109	0%	54
Nicaragua	153,390	-11.1%	84
Netherlands	14,929,333	-5.4%	12
Norway	933,300	+5.91%	64
New Zealand	3,426,030	+5.44%	37
Oman	5,228,456	-0.134%	31
Pakistan	3,383,580	-4.63%	38
Panama	8,518,425	-1.22%	25
Peru	2,865,239	-0.309%	43
Philippines	9,249,451	+6.62%	23
Poland	3,061,848	-3.96%	40
Portugal	2,794,454	-15.2%	44
Romania	772,046	+22.2%	67
Russia	3,914,304	-25.6%	36
Saudi Arabia	10,459,765	+5.91%	20
Senegal	738,000	-4.08%	68
Singapore	37,289,600	-0.75%	3
El Salvador	242,307	-17.7%	82
Suriname	103,874	-5.22%	85
Slovenia	1,017,798	+2.09%	62
Sweden	1,631,248	-0.241%	52
Togo	1,952,879	-1.67%	48
Thailand	10,497,112	+0.579%	19
Trinidad & Tobago	498,544	+21.5%	73
Tunisia	464,823	-1.03%	75
Turkey	12,366,172	-1.79%	15
Tanzania	700,000	0%	69
Uruguay	1,080,445	+10.5%	59
United States	62,214,119	+0.0847%	2
Vietnam	20,518,926	+3.52%	7
South Africa	4,053,350	-8.2%	35

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 = 'IS.SHP.GOOD.TU'

# 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 <- 'IS.SHP.GOOD.TU'

# 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

Container port traffic (TEU: 20 foot equivalent units)

Year: 2022

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