Country

Indicator

Value type

Values present

Gross national expenditure deflator (base year varies by country)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	5,096	+29.1%	4
Argentina	78,930	+216%	3
Armenia	143	+1.76%	48
Australia	105	+4.57%	118
Austria	121	+2.19%	80
Belgium	120	+1.45%	82
Benin	116	+1.24%	102
Burkina Faso	123	+4.55%	75
Bangladesh	153	+7.3%	39
Bulgaria	144	+7%	47
Bahamas	112	+1.65%	111
Bosnia & Herzegovina	135	+2.69%	53
Belarus	119	+9.33%	87
Bermuda	123	+1.96%	73
Brazil	253	+3.8%	17
Brunei	101	+0.154%	120
Botswana	140	+3.64%	51
Central African Republic	182	-18.2%	25
Canada	118	+3.3%	92
Switzerland	106	+1.9%	116
Chile	142	+6.13%	49
Côte d’Ivoire	113	-2.38%	110
Cameroon	121	-0.38%	78
Congo - Kinshasa	485	+8.19%	8
Colombia	164	+5.5%	36
Comoros	119	+2.11%	86
Cape Verde	113	-0.76%	109
Costa Rica	110	-0.711%	113
Cyprus	117	+3.66%	95
Czechia	127	+2.95%	64
Germany	120	+2.65%	84
Djibouti	123	+7.97%	74
Denmark	115	+2.1%	105
Dominican Republic	140	+4.34%	50
Ecuador	108	+1.69%	115
Egypt	170	+35.9%	35
Spain	118	+2.82%	93
Estonia	134	+3.02%	57
Ethiopia	428	+22.8%	9
Finland	115	+1.06%	103
France	114	+1.91%	107
Gabon	100	+0.593%	121
United Kingdom	117	+2.78%	96
Georgia	135	+1.86%	55
Ghana	589	+22.7%	6
Guinea	231	+2.96%	21
Gambia	239	+17.8%	19
Guinea-Bissau	128	-2.64%	63
Equatorial Guinea	177	+4.4%	29
Greece	114	+1.83%	108
Guatemala	140	+2.44%	52
Honduras	379	+2.39%	10
Croatia	127	+4.55%	65
Haiti	515	+22.4%	7
Hungary	152	+6.68%	41
Indonesia	172	+2.43%	32
India	179	+3.45%	27
Ireland	121	+3.98%	79
Iran	1,286	+35.3%	5
Iraq	199	+5.01%	23
Iceland	129	+5.44%	62
Israel	118	+4.09%	94
Italy	115	+1.56%	106
Kenya	155	+5.88%	37
Cambodia	118	+4.3%	88
Libya	327	+2.19%	11
Sri Lanka	243	+2.25%	18
Lithuania	146	+1.76%	46
Luxembourg	121	+3.61%	81
Latvia	124	+2.23%	70
Macao SAR China	100	+0.165%	122
Morocco	117	+0.0403%	97
Moldova	183	+7.84%	24
Madagascar	316	+8.59%	12
Mexico	132	+4.32%	58
North Macedonia	172	+1.52%	31
Mali	112	+2.43%	112
Malta	126	+3.06%	68
Montenegro	154	+3.38%	38
Mongolia	177	+7.11%	28
Mozambique	116	+0.783%	100
Malaysia	116	+0.394%	101
Namibia	134	+2.12%	56
Niger	106	+3.01%	117
Nicaragua	305	+3.47%	14
Netherlands	122	+4.17%	77
Norway	119	+3.89%	85
Nepal	214	+5%	22
Pakistan	235	+20.1%	20
Peru	172	+2.08%	33
Philippines	123	+3.02%	76
Poland	148	+4.16%	44
Portugal	118	+2.99%	90
Paraguay	146	+4.3%	45
Palestinian Territories	127	+7.41%	66
Romania	293	+7.21%	15
Russia	179	+9.22%	26
Rwanda	149	-1.38%	43
Saudi Arabia	103	+2.92%	119
Sudan	615,642	+247%	2
Senegal	117	+0.274%	98
Singapore	118	+2.93%	91
Sierra Leone	286	+28.6%	16
El Salvador	126	+2.29%	67
Somalia	110	+7.57%	114
Serbia	130	+4.04%	61
Slovakia	131	+3.18%	59
Slovenia	125	+2.33%	69
Sweden	120	+2.82%	83
Seychelles	116	+0.547%	99
Chad	115	+1.39%	104
Togo	118	+2.9%	89
Tunisia	173	+9.56%	30
Tanzania	135	+1.88%	54
Uganda	124	+1.88%	71
Ukraine	307	+10.1%	13
Uruguay	172	+5.5%	34
United States	123	+2.34%	72
Samoa	130	+0.119%	60
Kosovo	149	+1.01%	42
South Africa	153	+3.86%	40
Zimbabwe	676,305	+1,092%	1

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.DAB.DEFL.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.DAB.DEFL.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

Gross national expenditure deflator (base year varies by country)

Year: 2024

Acquire full dataset with Python or R from source