Country

Indicator

Value type

Values present

School enrollment, primary, female (% gross)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	90.2	-3.15%	82
Andorra	97.5	+8.4%	56
United Arab Emirates	107	+14.8%	27
Armenia	93.4	+1.18%	71
Azerbaijan	102	+2.15%	38
Burkina Faso	73.5	-11.7%	100
Bangladesh	116	-5.44%	14
Bahrain	94	+1.39%	67
Bahamas	77.5	-17.2%	98
Bosnia & Herzegovina	87	-0.294%	88
Belarus	93.9	+1.18%	68
Belize	96.4	-2.5%	60
Bermuda	86.3	+0.81%	89
Bolivia	98.9	-0.749%	48
Barbados	91.7	-2.45%	78
Brunei	93.9	-1.59%	69
China	100	-0.742%	44
Côte d’Ivoire	99.4	+7.43%	47
Cameroon	108	+2.05%	25
Congo - Kinshasa	117	-1.68%	13
Congo - Brazzaville	88	+1.88%	85
Comoros	91.6	+10.1%	79
Cuba	97.6	-1.53%	55
Curaçao	107	-3.97%	26
Cayman Islands	76.6	+0.0276%	99
Dominica	89.2	-1.09%	84
Dominican Republic	93.2	-5.53%	72
Algeria	108	+0.77%	24
Ecuador	98.6	-2.41%	50
Egypt	90.6	-1.32%	81
Ethiopia	81.5	-0.922%	95
Fiji	105	-0.319%	30
Georgia	103	-1.47%	32
Gibraltar	125	-3.01%	7
Gambia	100	+1.83%	43
Guatemala	102	-0.544%	36
Guyana	98.5	-1.39%	52
Honduras	87.1	+0.604%	87
Indonesia	98.7	-0.149%	49
India	111	+0.53%	18
Jamaica	84.5	-5.82%	91
Jordan	98.5	+0.378%	51
Kazakhstan	101	-0.487%	41
Kyrgyzstan	95.7	+2.28%	63
Cambodia	110	-0.88%	20
Kiribati	92.9	-5.9%	74
Laos	95.9	-0.342%	62
Lebanon	79	-3.72%	97
St. Lucia	101	-3.89%	42
Lesotho	84.4	-2.48%	92
Macao SAR China	87.5	+0.475%	86
Morocco	113	+0.384%	15
Madagascar	138	-1.5%	4
Maldives	98.5	+2.42%	53
Mali	70.9	+2.36%	101
Montenegro	106	+0.699%	28
Mongolia	96.3	+0.329%	61
Mozambique	118	-0.235%	12
Mauritania	112	+24.3%	17
Mauritius	112	+10.8%	16
Malawi	137	+6.58%	5
Malaysia	99.6	+2.06%	46
Niger	66.5	+1.31%	102
Nicaragua	104	+0.528%	31
Nepal	121	+4.74%	10
Nauru	101	+4.47%	39
Oman	96.4	+5.95%	59
Panama	94.5	-6.2%	66
Peru	108	-0.0132%	22
Philippines	93.1	+1.47%	73
Palau	94.8	+9.02%	65
Puerto Rico	83.2	-16.4%	94
Paraguay	92	-0.787%	76
Palestinian Territories	91.8	+0.372%	77
Russia	98	-3.02%	54
Rwanda	150	+1.39%	3
Senegal	89.7	-0.487%	83
Solomon Islands	84.6	-10.1%	90
Sierra Leone	158	-2.42%	2
El Salvador	91.2	+1.44%	80
San Marino	96.7	+1.87%	58
Somalia	19.2	+14.3%	104
Suriname	26.5	-72.1%	103
Eswatini	110	-4.96%	21
Sint Maarten	645		1
Seychelles	97	-1.22%	57
Syria	79.7	+7.19%	96
Turks & Caicos Islands	124	+7.38%	8
Chad	83.7	+2.37%	93
Togo	119	-1.88%	11
Thailand	101	-2.73%	40
Tajikistan	100	-0.818%	45
Timor-Leste	127	+2.6%	6
Tonga	103	-4.79%	33
Trinidad & Tobago	92.3	-4.15%	75
Tunisia	102	-0.41%	34
Tuvalu	102	+5.04%	37
Tanzania	95.5	+5.41%	64
Uzbekistan	93.5	-0.206%	70
St. Vincent & Grenadines	110	-0.913%	19
Venezuela	108	+15.6%	23
Vietnam	124	-0.522%	9
Vanuatu	105	-3.19%	29
Samoa	102	-1.64%	35

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.FE'

# 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.FE'

# 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, female (% gross)

Year: 2023

Acquire full dataset with Python or R from source