Country

Indicator

Value type

Values present

Lower secondary completion rate, male (% of relevant age group)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Aruba	117	+16.3%	4
Albania	99.2	-4.32%	27
Argentina	96.8	-10.3%	36
Armenia	103	-3.15%	8
Austria	95.5	-3.86%	41
Azerbaijan	86.2	-2.28%	74
Belgium	91.5	-1.35%	64
Benin	30.4	-18%	118
Burkina Faso	26.6	-21.8%	119
Bangladesh	74.8	+12.7%	89
Bulgaria	96.8	-2.05%	37
Bahrain	100	+3.97%	23
Bosnia & Herzegovina	88.4	-0.95%	70
Belarus	95	+3.45%	47
Belize	56.4	-23.7%	103
Bolivia	80.7	-7.7%	85
Barbados	87.1	-13.9%	72
Bhutan	55.3	-22.7%	104
Botswana	86	-3.87%	75
Switzerland	93.4	-1.3%	56
Chile	102	-0.6%	10
Côte d’Ivoire	60.2	-0.36%	100
Cameroon	35.2	-2.2%	114
Colombia	78.8	-2.67%	87
Costa Rica	65.5	-5.87%	96
Cuba	86.6	-0.307%	73
Cayman Islands	92	+6.49%	63
Cyprus	105	+0.957%	7
Czechia	93	-2.9%	57
Germany	61.4	-2.15%	98
Denmark	100	-0.977%	20
Dominican Republic	74	+14.3%	91
Algeria	60.8	-20.4%	99
Ecuador	92.3	-6.3%	61
Eritrea	50.4	+7.43%	107
Spain	88.8	-4.44%	69
Estonia	100	+1.48%	21
Finland	100	+0.326%	22
Fiji	95.4	-3.59%	43
France	100	+0.906%	18
Micronesia (Federated States of)	73.3	+1.13%	92
United Kingdom	99.4	+1.68%	26
Georgia	98.6	-4.15%	30
Gibraltar	126	-2.62%	3
Greece	95.1	+1.09%	45
Guatemala	49.8	-12.4%	108
Croatia	101	+1.28%	15
Hungary	95	-3.09%	46
Indonesia	103	+2.02%	9
India	88.9	-0.151%	68
Ireland	102	+1.66%	14
Israel	93.7	+0.467%	53
Italy	96	-4.08%	39
Jordan	85.6	-3.65%	76
Kazakhstan	92.4	+0.992%	59
Kyrgyzstan	94.6	-1.37%	48
Cambodia	57.1	+8.13%	102
Kiribati	88.3	-5.35%	71
South Korea	98.8	+3.22%	29
Laos	58.1	-4.85%	101
Liberia	44.2	+0.141%	110
St. Lucia	83	-5.96%	78
Sri Lanka	95.4	-0.487%	44
Lesotho	40.2	-1.3%	112
Lithuania	99.8	+6.26%	24
Luxembourg	97.9	-4.41%	34
Latvia	95.6	-0.739%	40
Macao SAR China	82.9	-5.12%	79
Morocco	66.8	-5.83%	93
Madagascar	30.9	-7.17%	117
Maldives	82.2	-9.66%	82
Mexico	92	+1.27%	62
Marshall Islands	47.6	-71.6%	109
North Macedonia	94.2	+6.83%	50
Malta	99.2	-1.15%	28
Montenegro	97.9	+2.68%	33
Mongolia	93.7	+1.73%	52
Mozambique	43.7	+24.1%	111
Malawi	22.5	+6.54%	120
Malaysia	81.6	+2.66%	83
Niger	15.9	-12.7%	121
Norway	99.6	-0.0495%	25
Nepal	102	+7.95%	12
Nauru	74.6	+7%	90
Oman	90.9	-7.3%	66
Pakistan	50.9	+0.099%	106
Panama	82.9	+0.268%	80
Peru	91.2	+1.47%	65
Philippines	94.1	+17.8%	51
Palau	94.5	-0.748%	49
Poland	131	+30.2%	2
Portugal	102	+1.4%	13
Paraguay	66.2	-2.32%	95
Palestinian Territories	89.7	+1.66%	67
Qatar	95.5	+9.18%	42
Romania	78.4	+11.7%	88
Rwanda	34.9	-10.4%	115
Saudi Arabia	116	+16.1%	5
Senegal	34.5	-7.36%	116
Singapore	101	+2.65%	17
El Salvador	65.5	-7.69%	97
San Marino	93.5	+1.36%	55
Serbia	96.5	-0.393%	38
Slovakia	82.5	-1.57%	81
Slovenia	93.6	-0.822%	54
Sweden	100	-0.108%	19
Seychelles	101	-21.9%	16
Syria	39.8	-4.76%	113
Turks & Caicos Islands	113	+6.83%	6
Togo	66.8	+5.71%	94
Thailand	135	-3.45%	1
Tonga	92.3	+40.1%	60
Turkey	92.4	-1.34%	58
Tuvalu	81.3	+1.14%	84
Uruguay	79.5	+33.6%	86
United States	102	+2.6%	11
Uzbekistan	97.1	+1.2%	35
Vietnam	98.4	+12.4%	31
Vanuatu	54.1	-9.8%	105
Samoa	98.3	+1.33%	32
South Africa	84.8	-10.6%	77

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.CMPT.LO.MA.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.CMPT.LO.MA.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

Lower secondary completion rate, male (% of relevant age group)

Year: 2022

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