Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Albania	10.8	+6.4%	67
Andorra	4.25	+9.79%	89
Argentina	25.7	+1.03%	26
Armenia	2.94	+2.91%	92
Australia	47.5	+1.34%	10
Austria	11.3	+0.543%	64
Azerbaijan	5.23	-37.7%	85
Belgium	59.2	-1.01%	6
Benin	20.9	+6.93%	35
Burkina Faso	42.9	+2.15%	12
Bulgaria	3.2	-5.73%	91
Bahrain	26	-0.594%	24
Bosnia & Herzegovina	2.82	-0.386%	94
Belarus	0.652	+10.1%	103
Belize	68.5	-0.222%	3
Bolivia	10.4	+1.76%	68
Brazil	14.4	+5.05%	49
Barbados	6.67	+1.15%	81
Canada	8.78	+1.02%	73
Switzerland	11.6	-0.856%	60
Chile	62.8	+0.14%	4
China	15.4	+1.91%	46
Côte d’Ivoire	57.9	+15.2%	8
Cameroon	30.1	+5.89%	20
Colombia	20.8	+3.95%	36
Costa Rica	8.28	+10.1%	76
Cayman Islands	38.9	+3.12%	14
Cyprus	20.7	+2.43%	37
Czechia	12.5	+6.97%	57
Germany	10	+0.396%	71
Djibouti	13.5	+8.65%	51
Dominica	34.9	+1.51%	16
Denmark	15.5	+0.998%	45
Dominican Republic	16.7	+5.84%	43
Algeria	1.36	+400%	101
Ecuador	23.5	+4.43%	30
Spain	31.2	+0.43%	19
Estonia	4.84	+10.7%	87
Finland	16.2	-0.789%	44
France	25.9	+1.53%	25
United Kingdom	77	+0.763%	2
Georgia	9.83	-4.88%	72
Ghana	12.5	-20.1%	56
Gibraltar	11.6	-4.31%	59
Greece	5.42	+13.8%	84
Guatemala	62.3	+1.46%	5
Hong Kong SAR China	21.4	+1.14%	33
Honduras	26	+6.34%	23
Croatia	2.57	+6.3%	95
Hungary	25.4	+5.36%	27
Ireland	0.477	+4.27%	105
Iceland	15.3	+0.988%	47
Israel	12.5	+1.15%	55
Italy	7.73	+7.72%	78
Jamaica	2.29	+14.1%	96
Jordan	19.4	-0.285%	39
Japan	21.9	+1.41%	32
Kiribati	45.5	+2.29%	11
South Korea	28.4	-1.65%	22
Lebanon	58.2	+8.18%	7
St. Lucia	2.04	-49.7%	97
Lithuania	4.8	+6.44%	88
Luxembourg	19.2	-2.98%	40
Latvia	6.45	+16.2%	82
Macao SAR China	96.6	+0.0307%	1
Morocco	11	+3.69%	65
Monaco	33.3	+0.768%	17
Moldova	2.92	+16.2%	93
Mexico	11.5	-0.96%	61
Marshall Islands	23.8	+14.9%	29
North Macedonia	1.66	+17.9%	99
Malta	36.2	-0.258%	15
Montenegro	0.572	+18.1%	104
Nicaragua	21.2	-2.96%	34
Netherlands	8.46	+27%	74
Norway	7.81	+2.21%	77
New Zealand	11	-0.263%	66
Oman	11.3	-1.83%	63
Peru	24.2	+10.2%	28
Poland	10.3	+0.539%	69
Portugal	19.1	+2.69%	41
Paraguay	18.7	+0.848%	42
Palestinian Territories	28.9	+0.639%	21
Qatar	48.7	+2.43%	9
Romania	1.86	+4.73%	98
Russia	0.92	+10.4%	102
Rwanda	10.2	-11.9%	70
Saudi Arabia	13	+1.54%	53
Senegal	22.7	+1.3%	31
Singapore	5.69	+1.19%	83
El Salvador	15.2	-1.51%	48
Serbia	1.41	+8.5%	100
Slovakia	12.7	+2.06%	54
Slovenia	3.79	+3.12%	90
Sweden	19.5	+0.909%	38
Seychelles	13.6	+0.741%	50
Syria	7.64	+2.38%	80
Turks & Caicos Islands	39.8	+12.9%	13
Trinidad & Tobago	12.4	+25.2%	58
Turkey	7.67	+4.39%	79
Tuvalu	32	+16%	18
Uruguay	11.5	+5.59%	62
United States	8.45	-8.38%	75
Uzbekistan	0.315	-20.3%	106
British Virgin Islands	13.4	-41.4%	52
South Africa	4.92	+3.21%	86

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.SEC.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.SEC.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, secondary, private (% of total secondary)

Year: 2022

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