Country

Indicator

Value type

Values present

Energy intensity level of primary energy (MJ/$2017 PPP GDP)

Source: worldbank.org, 01.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Aruba	2.9	-7.35%	70
Albania	2.06	-9.25%	93
Andorra	1.81	-4.23%	98
Argentina	3.29	-2.95%	56
Antigua & Barbuda	4.48	-7.25%	35
Australia	4.12	+0.488%	41
Austria	2.55	-10.8%	80
Azerbaijan	4.4	-6.98%	36
Burundi	7.4	-1.07%	11
Belgium	3.4	-11.9%	54
Burkina Faso	5.4	-0.552%	24
Bulgaria	4.72	0%	32
Bahamas	2.42	-15.7%	84
Bosnia & Herzegovina	5.53	-6.11%	23
Belize	4.29	-10.3%	38
Bermuda	1.51	-0.658%	100
Brazil	3.87	-2.27%	45
Barbados	3.76	-6.23%	47
Bhutan	9.04	-7%	7
Central African Republic	8.31	-1.19%	10
Canada	6.44	-1.68%	19
Switzerland	1.48	-3.27%	101
Chile	3.19	-7.8%	60
Colombia	2.18	-6.44%	89
Comoros	4.3	+0.703%	37
Cape Verde	2.17	-11.4%	90
Costa Rica	1.91	-4.02%	96
Cyprus	2.36	-3.67%	85
Czechia	3.98	-4.33%	43
Germany	2.47	-8.52%	82
Djibouti	2.03	-2.4%	95
Dominica	2.95	-2.64%	68
Denmark	1.83	-6.63%	97
Spain	2.53	-4.89%	81
Estonia	4.12	+10.5%	41
Finland	4.82	-6.41%	31
Fiji	2.15	-5.29%	91
France	2.83	-12.4%	72
Micronesia (Federated States of)	6.62	+2.48%	16
United Kingdom	2.04	-7.27%	94
Georgia	3.64	-5.7%	50
Guinea	4.95	-3.51%	30
Gambia	3.16	+1.61%	61
Guinea-Bissau	6.95	-2.66%	13
Equatorial Guinea	5.9	+13.9%	22
Greece	2.58	-4.8%	79
Grenada	2.67	-3.96%	76
Guyana	2.06	-26.4%	93
Croatia	2.75	-5.5%	74
Hungary	3.12	-10.3%	64
Ireland	0.97	-11%	104
Iceland	11.9	-3.24%	4
Israel	2.3	-3.77%	87
Italy	2.3	-8.37%	87
Japan	3.15	-3.08%	62
Kyrgyzstan	4.69	-8.93%	33
Kiribati	6.82	-0.438%	14
St. Kitts & Nevis	2.63	0%	77
South Korea	5.11	-3.95%	27
Laos	4.51	+6.12%	34
Liberia	13.8	-1.78%	2
St. Lucia	2.94	-12.2%	69
Lesotho	9.02	-13.4%	8
Lithuania	2.59	-11.9%	78
Luxembourg	1.71	-13.6%	99
Latvia	2.88	-7.1%	71
Macao SAR China	1.13	+32.9%	103
Morocco	3.05	-2.87%	66
Moldova	4.69	-4.67%	33
Madagascar	9	-3.74%	9
Maldives	2.79	-2.79%	73
Mexico	3	+0.334%	67
Marshall Islands	11	+6.2%	5
North Macedonia	3.22	-0.923%	58
Mali	6.26	-2.64%	20
Malta	1.18	-2.48%	102
Montenegro	3.43	-2.83%	53
Mauritania	3.74	+5.65%	48
Malawi	3.33	+9.54%	55
New Caledonia	13.5	+9.07%	3
Netherlands	2.53	-14.2%	81
Norway	3.21	-6.41%	59
Nauru	6.79	-2.16%	15
New Zealand	3.52	-6.63%	51
Palau	13.8	+3.99%	1
Papua New Guinea	6.47	-1.07%	18
Poland	3.13	-8.48%	63
Puerto Rico	0.5	-26.5%	105
Portugal	2.31	-4.55%	86
Palestinian Territories	3.1	-0.958%	65
French Polynesia	2.58	-0.769%	79
Romania	2.12	-11.7%	92
Rwanda	3.51	-2.23%	52
Solomon Islands	5.18	+2.17%	26
Sierra Leone	5.29	-3.64%	25
Somalia	6.51	-0.913%	17
Serbia	5.02	+1.01%	29
São Tomé & Príncipe	3.64	-8.31%	50
Slovakia	3.83	-7.49%	46
Slovenia	3.15	-3.37%	62
Sweden	3.24	-7.16%	57
Eswatini	3.89	-0.512%	44
Sint Maarten	7.12	-7.53%	12
Seychelles	2.69	-5.61%	75
Turks & Caicos Islands	5.95	0%	21
Chad	4.2	-3.23%	39
Timor-Leste	2.42	-1.22%	84
Tonga	4.03	-13.3%	42
Tunisia	3.7	-2.89%	49
Turkey	2.27	-8.47%	88
Tuvalu	2.79	0%	73
Uganda	9.86	-3.33%	6
United States	4.17	-1.65%	40
St. Vincent & Grenadines	2.43	-1.22%	83
Vanuatu	5.04	-2.51%	28
Samoa	5.4	+10.2%	24

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 = 'EG.EGY.PRIM.PP.KD'

# 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 <- 'EG.EGY.PRIM.PP.KD'

# 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

Energy intensity level of primary energy (MJ/$2017 PPP GDP)

Year: 2022

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