Country

Indicator

Value type

Values present

Secondary education, pupils

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	216,158	-4.97%	55
Andorra	5,063	+3.5%	80
United Arab Emirates	860,281	+27.3%	34
Armenia	274,915	+0.928%	51
Azerbaijan	1,066,209	+3.48%	29
Burkina Faso	1,185,480	-5.18%	26
Bangladesh	15,661,732	-2.54%	4
Bahrain	110,478	-0.17%	60
Bahamas	29,409	+18.8%	72
Bosnia & Herzegovina	226,755	-1.06%	54
Belarus	725,721	+2.09%	39
Belize	37,444	-5.01%	69
Bermuda	3,484	-5.33%	83
Bolivia	1,292,286	+1.23%	23
Barbados	18,515	-2.33%	73
Brunei	40,924	+0.442%	68
China	96,322,800	+2.75%	2
Côte d’Ivoire	3,098,275	+22.8%	14
Cameroon	2,007,361	+1.21%	19
Congo - Kinshasa	7,928,331	+7.88%	7
Comoros	67,764	-8.05%	63
Cuba	684,680	-7.55%	40
Curaçao	12,675	-13.9%	76
Cayman Islands	4,036	+3.65%	82
Djibouti	69,362	-2.17%	62
Dominica	4,483	-3.3%	81
Dominican Republic	856,499	-1.66%	35
Algeria	5,580,053	+1.44%	8
Ecuador	1,822,516	-1.96%	20
Fiji	113,605	+4.16%	59
Georgia	302,590	+4.74%	50
Gibraltar	2,429	+3.23%	86
Guatemala	1,078,266	+3.32%	28
Guyana	63,477	+0.422%	64
Hong Kong SAR China	333,774	-1.53%	49
Honduras	580,717	+1.19%	42
Indonesia	26,883,110	-0.742%	3
India	134,342,141	-3.42%	1
Jamaica	184,800	+0.215%	56
Jordan	1,007,080	+1.72%	30
Kazakhstan	2,375,914	+5.44%	18
Kyrgyzstan	840,456	+3.38%	36
Cambodia	1,132,622	+3.53%	27
Kiribati	16,945	+11.5%	74
Laos	568,402	-3.76%	43
Lebanon	405,438	-1.16%	48
St. Lucia	10,805	-0.616%	77
Macao SAR China	30,274	+4.53%	71
Morocco	3,434,273	+5.85%	12
Monaco	3,478	+1.84%	84
Moldova	227,896	-0.743%	53
Maldives	33,002	+13.6%	70
Mali	1,305,140	+26.3%	22
Montenegro	56,561	+0.396%	67
Mongolia	412,070	+8.6%	47
Malaysia	2,629,154	-0.169%	17
Niger	910,184	+15.7%	32
Nicaragua	463,660	+5.92%	45
Nepal	3,695,560	+5.2%	11
Nauru	1,438	+2.64%	88
Oman	513,406	+4.91%	44
Peru	3,209,642	+7.08%	13
Philippines	12,594,216	+0.152%	5
Palau	1,398	+10.2%	89
Puerto Rico	236,258	+7.92%	52
Paraguay	581,143	-2.64%	41
Palestinian Territories	868,232	+1.6%	33
Qatar	144,410	+7.18%	57
Russia	10,798,220	+1.48%	6
Rwanda	819,262	+2.35%	37
Senegal	1,283,240	-0.224%	24
El Salvador	442,494	-0.454%	46
San Marino	1,715	-1.15%	87
Somalia	115,828	-11.1%	58
Suriname	58,269	+15.4%	65
Seychelles	7,861	-2.2%	79
Syria	1,353,625	+2.46%	21
Turks & Caicos Islands	2,846	+4.9%	85
Chad	750,180	+13%	38
Togo	915,214	+7.37%	31
Thailand	5,139,185	+6.43%	9
Tajikistan	1,257,135	+1.53%	25
Tonga	15,981	+2.02%	75
Trinidad & Tobago	88,408	-0.928%	61
Tuvalu	1,252	-3.99%	90
Ukraine	2,645,164	+1.1%	16
Uzbekistan	4,112,120	+2.22%	10
St. Vincent & Grenadines	9,707	-0.828%	78
Venezuela	2,695,688	+12.7%	15
Vanuatu	58,028	+62.1%	66

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.ENRL'

# 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.ENRL'

# 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

Secondary education, pupils

Year: 2023

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