Country

Indicator

Value type

Values present

Primary education, teachers

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	9,584	-0.869%	61
Andorra	418	-0.948%	88
United Arab Emirates	37,392	+51.7%	43
Armenia	7,852	-0.165%	65
Azerbaijan	39,519	-1.02%	41
Burkina Faso	83,736	-5.08%	28
Bangladesh	384,513	+6.01%	6
Bahrain	8,963	+3.22%	62
Bahamas	2,145	+24.6%	78
Bosnia & Herzegovina	9,722	+3.95%	60
Belarus	22,480	-0.24%	54
Belize	2,519	+0.921%	76
Bolivia	76,473	+0.0824%	31
Barbados	1,628	+1.94%	79
Brunei	3,849	-0.491%	73
China	6,719,340	+0.229%	1
Côte d’Ivoire	105,502	+3.77%	23
Congo - Kinshasa	561,172	+9.6%	4
Congo - Brazzaville	23,019	-17.3%	52
Comoros	4,322	-8.18%	72
Cuba	86,806	-3.55%	25
Cayman Islands	368	+4.84%	90
Djibouti	2,739	+6.62%	75
Dominica	552	-0.541%	87
Dominican Republic	78,780	+27.6%	30
Algeria	212,345	+2.58%	14
Ecuador	79,602	+2.27%	29
Egypt	426,940	-3.25%	5
Ethiopia	358,079	+1.4%	8
Fiji	6,601	+4.41%	68
Georgia	32,059	-2.07%	45
Gibraltar	197	-12.8%	93
Guatemala	103,418	-11.4%	24
Guyana	5,370	-1.05%	71
Hong Kong SAR China	28,700	-1.63%	48
Indonesia	1,464,336	+1.21%	3
India	4,824,726	+3.62%	2
Jamaica	12,548	+12.8%	57
Jordan	63,027	+8.03%	35
Kazakhstan	112,623	+6.5%	21
Kyrgyzstan	25,366	+9.11%	50
Cambodia	44,905	-0.538%	39
Kiribati	742	+0.406%	85
Laos	26,032	-20.8%	49
Lebanon	36,172	+0.654%	44
St. Lucia	1,058	-2.13%	82
Lesotho	10,021	-0.00998%	59
Macao SAR China	2,836	+3.05%	74
Morocco	180,715	+3.09%	16
Monaco	184	+7.6%	94
Moldova	7,467	-0.771%	66
Madagascar	135,269	+1.17%	19
Mali	59,920	-12.8%	36
Mongolia	11,744	+2.04%	58
Mauritania	22,372	+34.5%	55
Mauritius	5,640	-4.99%	69
Malawi	85,449	+0.768%	26
Malaysia	254,669	+0.517%	12
Niger	68,237	+1.04%	33
Nicaragua	29,707	-1.35%	47
Nepal	158,842	+3.52%	17
Nauru	62	-1.59%	97
Oman	30,140	+7.23%	46
Peru	217,304	+0.651%	13
Palau	124	+2.48%	95
Palestinian Territories	25,038	+3.56%	51
Qatar	13,998	+6.38%	56
Russia	327,102	-0.452%	9
Rwanda	67,539	+7.13%	34
Senegal	72,008	-0.378%	32
Solomon Islands	5,562	+27.9%	70
Sierra Leone	46,007	-4.97%	38
El Salvador	22,803	-7.46%	53
San Marino	247	+1.65%	92
Suriname	1,403	-72.3%	81
Eswatini	8,880	-2.83%	63
Sint Maarten	387	+21.7%	89
Seychelles	692	+13.6%	86
Syria	146,383	+27.6%	18
Turks & Caicos Islands	266	+4.72%	91
Chad	51,345	+2.95%	37
Togo	38,768	-5.75%	42
Thailand	320,912	-5.21%	10
Tajikistan	40,467	-0.325%	40
Timor-Leste	7,144	-9.55%	67
Tonga	816	+0.99%	84
Trinidad & Tobago	8,219	+12%	64
Tunisia	84,596	+3.42%	27
Tuvalu	96	-3.03%	96
Tanzania	207,323	+2.8%	15
Ukraine	106,216	-8.5%	22
Uzbekistan	121,023	+1.29%	20
St. Vincent & Grenadines	993	+2.16%	83
Venezuela	283,420	+153%	11
Vietnam	379,191	-0.729%	7
Vanuatu	2,186	+1.58%	77
Samoa	1,558	+6.79%	80

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.TCHR'

# 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.TCHR'

# 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

Primary education, teachers

Year: 2023

Acquire full dataset with Python or R from source