Country

Indicator

Value type

Values present

Logistics performance index: Overall (1=low to 5=high)

Source: worldbank.org, 01.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	1.9	-2.56%	25
Angola	2.1	+2.44%	23
Albania	2.5	-6.02%	19
United Arab Emirates	4	+1.01%	4
Argentina	2.8	-3.11%	16
Armenia	2.5	-4.21%	19
Antigua & Barbuda	2.9		15
Australia	3.7	-1.33%	7
Austria	4	-0.744%	4
Belgium	4	-0.99%	4
Benin	2.9	+5.45%	15
Burkina Faso	2.3	-12.2%	21
Bangladesh	2.6	+0.775%	18
Bulgaria	3.2	+5.61%	12
Bahrain	3.5	+19.5%	9
Bahamas	2.7	+6.72%	17
Bosnia & Herzegovina	3	+6.76%	14
Belarus	2.7	+5.06%	17
Bolivia	2.4	+1.69%	20
Brazil	3.2	+7.02%	12
Bhutan	2.5	+15.2%	19
Botswana	3.1	+1.79%	13
Central African Republic	2.5	+16.3%	19
Canada	4	+7.24%	4
Switzerland	4.1	+5.13%	3
Chile	3	-9.64%	14
China	3.7	+2.49%	7
Cameroon	2.1	-19.2%	23
Congo - Kinshasa	2.5	+2.88%	19
Congo - Brazzaville	2.6	+4.42%	18
Colombia	2.9	-1.36%	15
Costa Rica	2.9	+3.94%	15
Cuba	2.2	0%	22
Cyprus	3.2	+1.59%	12
Czechia	3.3	-10.3%	11
Germany	4.1	-2.38%	3
Djibouti	2.7	+2.66%	17
Denmark	4.1	+2.76%	3
Dominican Republic	2.6	-2.26%	18
Algeria	2.5	+2.04%	19
Egypt	3.1	+9.93%	13
Spain	3.9	+1.83%	5
Estonia	3.6	+8.76%	8
Finland	4.2	+5.79%	2
Fiji	2.3	-2.13%	21
France	3.9	+1.56%	5
Gabon	2.4	+11.1%	20
United Kingdom	3.7	-7.27%	7
Georgia	2.7	+10.7%	17
Ghana	2.5	-2.72%	19
Guinea	2.5	+13.6%	19
Gambia	2.3	-4.17%	21
Guinea-Bissau	2.6	+8.79%	18
Greece	3.7	+15.6%	7
Grenada	2.5		19
Guatemala	2.6	+7.88%	18
Guyana	2.4	+1.69%	20
Hong Kong SAR China	4	+2.04%	4
Honduras	2.9	+11.5%	15
Croatia	3.3	+6.45%	11
Haiti	2.1	-0.474%	23
Hungary	3.2	-6.43%	12
Indonesia	3	-4.76%	14
India	3.4	+6.92%	10
Ireland	3.6	+2.56%	8
Iran	2.3	-19.3%	21
Iraq	2.4	+10.1%	20
Iceland	3.6	+11.5%	8
Israel	3.6	+8.76%	8
Italy	3.7	-1.07%	7
Jamaica	2.5	-0.794%	19
Japan	3.9	-3.23%	5
Kazakhstan	2.7	-3.91%	17
Kyrgyzstan	2.3	-9.8%	21
Cambodia	2.4	-6.98%	20
South Korea	3.8	+5.26%	6
Kuwait	3.2	+11.9%	12
Laos	2.4	-11.1%	20
Liberia	2.4	+7.62%	20
Libya	1.9	-9.95%	25
Sri Lanka	2.8	+7.69%	16
Lithuania	3.4	+12.6%	10
Luxembourg	3.6	-0.826%	8
Latvia	3.5	+24.6%	9
Moldova	2.5	+1.63%	19
Madagascar	2.3	-3.77%	21
Mexico	2.9	-4.92%	15
North Macedonia	3.1	+14.8%	13
Mali	2.6	+0.386%	18
Malta	3.3	+17.4%	11
Montenegro	2.8	+1.82%	16
Mongolia	2.5	+5.49%	19
Mauritania	2.3	-1.29%	21
Mauritius	2.5	-8.42%	19
Malaysia	3.6	+11.8%	8
Namibia	2.9	+5.66%	15
Nigeria	2.6	+2.77%	18
Nicaragua	2.5	-1.23%	19
Netherlands	4.1	+1.99%	3
Norway	3.7	0%	7
New Zealand	3.6	-7.22%	8
Oman	3.3	+3.12%	11
Panama	3.1	-5.49%	13
Peru	3	+11.5%	14
Philippines	3.3	+13.8%	11
Papua New Guinea	2.7	+24.4%	17
Poland	3.6	+1.69%	8
Portugal	3.4	-6.59%	10
Paraguay	2.7	-2.88%	17
Qatar	3.5	+0.865%	9
Romania	3.2	+2.56%	12
Russia	2.6	-5.8%	18
Rwanda	2.8	-5.72%	16
Saudi Arabia	3.4	+13%	10
Sudan	2.4	-1.23%	20
Singapore	4.3	+7.5%	1
Solomon Islands	2.8	+8.95%	16
El Salvador	2.7	+4.65%	17
Somalia	2	-9.5%	24
Serbia	2.8	-1.41%	16
Slovakia	3.3	+8.91%	11
Slovenia	3.3	-0.302%	11
Sweden	4	-1.23%	4
Syria	2.3	0%	21
Togo	2.5	+2.04%	19
Thailand	3.5	+2.64%	9
Tajikistan	2.5	+6.84%	19
Trinidad & Tobago	2.5	+3.31%	19
Turkey	3.4	+7.94%	10
Ukraine	2.7	-4.59%	17
Uruguay	3	+11.5%	14
United States	3.8	-2.31%	6
Uzbekistan	2.6	+0.775%	18
Venezuela	2.3	+3.14%	21
Vietnam	3.3	+0.917%	11
Yemen	2.2	-3.08%	22
South Africa	3.7	+9.47%	7
Zimbabwe	2.5	+17.9%	19

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 = 'LP.LPI.OVRL.XQ'

# 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 <- 'LP.LPI.OVRL.XQ'

# 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

Logistics performance index: Overall (1=low to 5=high)

Year: 2022

Acquire full dataset with Python or R from source