Country

Indicator

Value type

Values present

School enrollment, primary (% gross)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	93.7	-2.76%	71
Andorra	97.6	+8.29%	53
United Arab Emirates	106	+13.3%	29
Armenia	93.8	+0.975%	68
Azerbaijan	102	+2.18%	38
Burkina Faso	72.3	-12.2%	101
Bangladesh	112	-5.15%	19
Bahrain	93.7	+1.51%	70
Bahamas	77.9	-16.9%	99
Bosnia & Herzegovina	87.2	-0.666%	87
Belarus	94.7	+0.0251%	64
Belize	97	-2.99%	57
Bermuda	86.3	+1.03%	91
Bolivia	98.9	-0.667%	48
Barbados	93.4	-2.23%	74
Brunei	93.5	-1.14%	73
China	99.3	-0.846%	46
Côte d’Ivoire	102	+7.4%	40
Cameroon	113	+1.74%	16
Congo - Kinshasa	120	-2.01%	13
Congo - Brazzaville	89	+1.52%	85
Comoros	94.7	+11.1%	66
Cuba	98.4	-1.47%	50
Curaçao	109	-3.69%	23
Cayman Islands	88	+0.34%	86
Dominica	89.9	-2.17%	84
Dominican Republic	94.7	-5.75%	65
Algeria	109	+0.386%	24
Ecuador	97.3	-2.3%	55
Egypt	90.3	-1.4%	83
Ethiopia	84.5	-1.24%	93
Fiji	108	-0.367%	26
Georgia	103	-1.16%	34
Gibraltar	123	-2.98%	7
Gambia	93.7	+1.54%	69
Guatemala	103	-0.828%	35
Guyana	99	-0.771%	47
Honduras	86.6	+0.799%	88
Indonesia	100	-0.414%	45
India	112	+0.852%	17
Jamaica	84.5	-6.92%	94
Jordan	98.3	-0.245%	51
Kazakhstan	101	-0.385%	44
Kyrgyzstan	96.2	+2.11%	59
Cambodia	111	+1.3%	20
Kiribati	93.4	-5.21%	76
Laos	96.8	-0.474%	58
Lebanon	79.8	-4.51%	97
St. Lucia	101	-2.89%	41
Lesotho	86.4	-2.53%	90
Macao SAR China	86.4	+1.71%	89
Morocco	114	+0.265%	14
Madagascar	136	-1.74%	4
Maldives	97.5	+2.28%	54
Mali	74.4	+2.43%	100
Montenegro	106	+0.462%	30
Mongolia	95.8	+0.223%	61
Mozambique	120	-0.968%	12
Mauritania	112	+28.9%	18
Mauritius	111	+11%	21
Malawi	135	+7.12%	5
Malaysia	98.8	+1.11%	49
Niger	68.5	+0.285%	102
Nicaragua	106	+0.448%	31
Nepal	123	+3.52%	9
Nauru	101	+6%	42
Oman	95.6	+6.07%	62
Panama	94.6	-6.27%	67
Peru	107	-0.161%	27
Philippines	93.4	+1.67%	75
Palau	95.9	+8.63%	60
Puerto Rico	83.1	-17.3%	95
Paraguay	92.4	-0.915%	79
Palestinian Territories	92.1	+0.349%	80
Russia	97.7	-3.35%	52
Rwanda	152	+2.11%	3
Senegal	82.6	-0.826%	96
Solomon Islands	84.7	-11.2%	92
Sierra Leone	153	-2.37%	2
El Salvador	91.1	+1.93%	82
San Marino	95.1	+1.26%	63
Somalia	21.2	+151%	104
Suriname	66	-32.4%	103
Eswatini	114	-6.06%	15
Sint Maarten	257		1
Seychelles	97.1	-0.644%	56
Syria	79.6	+7.1%	98
Turks & Caicos Islands	126	+2.9%	6
Chad	91.8	+1.56%	81
Togo	120	-1.82%	11
Thailand	103	-1.53%	33
Tajikistan	101	-0.716%	43
Timor-Leste	123	+2.06%	8
Tonga	103	-3.66%	36
Trinidad & Tobago	92.8	-5%	78
Tunisia	104	-0.368%	32
Tuvalu	102	+3.54%	39
Tanzania	93.1	+5.46%	77
Uzbekistan	93.5	-0.448%	72
St. Vincent & Grenadines	110	-0.771%	22
Venezuela	108	+13.6%	25
Vietnam	122	-0.519%	10
Vanuatu	107	-3.61%	28
Samoa	102	-1.76%	37

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.ENRR'

# 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.ENRR'

# 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 (% gross)

Year: 2023

Acquire full dataset with Python or R from source