Country

Indicator

Value type

Values present

School enrollment, primary, private (% of total primary)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Albania	10.5	+13.2%	58
Andorra	4.68	+28.3%	84
Argentina	25.3	+0.845%	25
Armenia	2.89	+11.8%	92
Australia	30.9	+2.34%	17
Austria	6.3	-0.534%	75
Azerbaijan	1.06	+7.67%	105
Belgium	54.1	-0.105%	6
Benin	25.8	+3.06%	23
Burkina Faso	26.3	+7.91%	21
Bulgaria	2.09	+1.86%	95
Bahrain	38	+2.26%	12
Bosnia & Herzegovina	2.01	+5.61%	96
Belarus	0.269	+18.6%	112
Belize	81	-0.395%	2
Bolivia	9.33	+2.38%	61
Brazil	18.3	+4.64%	36
Barbados	13.3	-0.0347%	47
Canada	6.58	+2.85%	73
Switzerland	5.53	+0.916%	81
Chile	62.9	-0.162%	5
China	8.82	-1.63%	66
Côte d’Ivoire	18.1	+1.53%	37
Cameroon	25.8	+4.15%	22
Colombia	19.3	+5.17%	33
Costa Rica	9.26	+8.51%	62
Cayman Islands	48.8	+6.72%	7
Cyprus	11.9	+6.56%	54
Czechia	3.82	+9.92%	88
Germany	5.29	+0.39%	82
Djibouti	17.9	+0.339%	38
Dominica	40.9	-0.751%	11
Denmark	18.3	+1.68%	35
Dominican Republic	19.4	+17.3%	32
Algeria	1.48	+14.6%	101
Ecuador	23.7	+6.93%	27
Egypt	8.69	-2.29%	68
Spain	31.9	+0.539%	16
Estonia	7.37	+4.2%	71
Finland	1.75	+2.04%	99
France	15.2	+0.735%	45
United Kingdom	36.8	+3.91%	13
Georgia	9.97	-4.75%	59
Ghana	28.4	-8%	19
Gibraltar	9.46	+3.99%	60
Greece	6.91	+1.47%	72
Guatemala	11.2	-3.51%	55
Hong Kong SAR China	19.7	+1.99%	31
Honduras	11.1	+4.04%	56
Croatia	1.18	+10.6%	103
Hungary	20.5	+7.17%	29
Ireland	0.998	-5.56%	107
Iceland	3.38	+9.08%	91
Israel	24	+0.48%	26
Italy	6.15	+0.489%	77
Jamaica	12.2	+22.6%	52
Jordan	29.1	-1.14%	18
Japan	1.26	+2.06%	102
South Korea	1.63	+2.84%	100
Lebanon	71.4	+4.81%	3
St. Lucia	6.51	+8.85%	74
Lesotho	3.75	-56.7%	89
Lithuania	5.74	+9.87%	79
Luxembourg	11.1	-2.25%	57
Latvia	3.75	+24.9%	90
Macao SAR China	96.8	-0.287%	1
Morocco	17.1	+5.63%	40
Monaco	32.5	+3.15%	15
Moldova	2.51	+12.9%	93
Mexico	9.09	+1.86%	63
Marshall Islands	20	+3.32%	30
North Macedonia	0.734	+0.323%	108
Malta	41.9	-0.186%	10
Montenegro	0.516	+11.8%	110
Mozambique	1.97	+0.0609%	98
Niger	4.03	+5.68%	86
Nicaragua	15.6	+0.15%	43
Netherlands	0.318	-10.3%	111
Norway	3.92	+2.52%	87
New Zealand	2.16	+8.48%	94
Oman	15.2	+0.856%	44
Panama	13.1	-3.68%	48
Peru	25.6	+15.2%	24
Poland	7.5	+6.08%	70
Portugal	13	+0.871%	49
Paraguay	20.5	+1.41%	28
Palestinian Territories	43.6	-0.406%	8
Qatar	63.6	+0.228%	4
Romania	1.98	+14.1%	97
Russia	1.1	+12%	104
Rwanda	5.01	+2.73%	83
Saudi Arabia	12.7	-9.69%	50
Senegal	17.8	-0.6%	39
Singapore	4.51	+2.41%	85
El Salvador	12.3	-0.166%	51
Serbia	0.245	+14.6%	113
Slovakia	8.94	+10.5%	65
Slovenia	1.04	+2.11%	106
Sweden	12.1	+3.51%	53
Seychelles	14.9	+1.85%	46
Syria	6.21	+2.1%	76
Turks & Caicos Islands	43.2	+5.14%	9
Chad	18.6	+7.55%	34
Togo	33.1	+0.403%	14
Trinidad & Tobago	8.75	+21.2%	67
Tunisia	8.46	+9.05%	69
Turkey	5.74	+13.6%	80
Tuvalu	16.6	+0.042%	42
Uruguay	16.6	+0.734%	41
United States	9.07	-3.46%	64
Uzbekistan	0.726	+18.4%	109
British Virgin Islands	27.3	-7.93%	20
South Africa	5.79	+5.14%	78

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 = 'SE.PRM.PRIV.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 <- 'SE.PRM.PRIV.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

School enrollment, primary, private (% of total primary)

Year: 2022

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