Country

Indicator

Value type

Values present

People using safely managed sanitation services, urban (% of urban population)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Albania	49.6	+4.74%	58
Andorra	100	0%	1
United Arab Emirates	98.5	0%	8
Argentina	46.2	-0.426%	59
Armenia	0		114
Austria	100	0%	3
Azerbaijan	63	+2.29%	48
Benin	3.64	0%	113
Burkina Faso	12.1	+0.41%	109
Bangladesh	28.8	+1.2%	87
Bulgaria	77.2	+1.39%	34
Bosnia & Herzegovina	58.1	0%	51
Belarus	81.5	+0.0155%	31
Brazil	51.4	0%	56
Bhutan	41	0%	68
Central African Republic	23.4	0%	91
Canada	84	0%	29
Switzerland	99.9	0%	4
Chile	99.2	0%	5
China	84.7	+0.516%	25
Côte d’Ivoire	20.1	+0.592%	100
Congo - Kinshasa	15.2	-0.661%	105
Colombia	16.8	+0.714%	103
Costa Rica	23.1	+1.21%	92
Cuba	37.1	+0.545%	72
Cyprus	86.2	0%	23
Germany	98.5	0%	7
Djibouti	44.8	0%	60
Dominican Republic	42.3	-0.875%	66
Algeria	64.6	0%	43
Ecuador	30.9	-1.99%	84
Egypt	72.6	0%	38
Estonia	97.7	+0.202%	9
Ethiopia	17.4	0%	101
Fiji	42.7	-0.329%	65
United Kingdom	99	0%	6
Georgia	14.1	-0.722%	106
Ghana	14	+2.41%	107
Gambia	31	-0.565%	83
Guinea-Bissau	21.3	0%	97
Greece	97.4	0%	12
Guyana	34.1	-1.24%	80
Hong Kong SAR China	96.5	+0.945%	14
Honduras	40.5	+0.0191%	70
Hungary	90.5	0%	19
India	42.7	+2.88%	64
Ireland	84.8	0%	24
Iran	76.2	-0.737%	35
Iraq	54.7	+1.65%	53
Israel	96.4	+0.793%	16
Italy	79.4	0%	32
Jordan	84.4	0%	27
Kazakhstan	84.3	-0.104%	28
Kenya	27.9	+1.19%	89
Kyrgyzstan	86.3	+0.289%	22
Cambodia	44.7	+1.35%	61
Kiribati	24.6	+1.12%	90
Kuwait	100	+0.00001%	2
Laos	63.3	0%	47
Lesotho	39.4	0%	71
Lithuania	97.5	+0.193%	10
Luxembourg	96.4	+0.328%	15
Macao SAR China	68.1	+1.5%	41
Morocco	34.4	0%	78
Monaco	100	0%	1
Moldova	84.6	+0.807%	26
Madagascar	16.3	+2.68%	104
Mexico	65.1	+4.06%	42
North Macedonia	8.19	0%	110
Mali	7.5	+1.12%	111
Myanmar (Burma)	52.7	0%	55
Montenegro	64.1	+4.1%	45
Mongolia	70.3	+0.0755%	39
Malawi	41.2	+3.1%	67
Niger	20.6	+1.55%	99
Nigeria	36.6	+2.72%	74
Netherlands	97.5	0%	11
Nepal	44.6	0%	62
Panama	50	0%	57
Peru	61.6	+4.4%	49
Philippines	56.2	+1.06%	52
Papua New Guinea	28.3	0%	88
Portugal	95.9	+0.272%	18
Paraguay	53	+0.124%	54
Palestinian Territories	74.6	+1.63%	36
Russia	64.2	+0.0463%	44
Saudi Arabia	82.4	0%	30
Senegal	13.9	+0.219%	108
Singapore	100	0%	1
Sierra Leone	22.5	+4.01%	93
El Salvador	17.4	-0.768%	102
Somalia	44.5	0%	63
Serbia	22.4	-0.867%	94
São Tomé & Príncipe	34.8	0%	75
Suriname	20.7	0%	98
Slovakia	88.5	-0.009%	21
Sweden	96	0%	17
Turks & Caicos Islands	33.3	0%	81
Chad	32	0%	82
Togo	7.13	0%	112
Thailand	30	0%	85
Tonga	22.3	-0.109%	96
Tunisia	88.9	0%	20
Turkey	78	+2.73%	33
Tuvalu	34.7	-0.645%	77
Tanzania	34.4	-0.011%	79
Uganda	22.4	+0.249%	95
Ukraine	68.9	0%	40
United States	97.2	-0.00282%	13
Uzbekistan	63.4	-0.186%	46
Vietnam	40.8	+0.131%	69
Vanuatu	29.8	-2.2%	86
Samoa	36.7	-0.701%	73
Yemen	60.5	0%	50
South Africa	73.1	+0.446%	37
Zimbabwe	34.7	0%	76

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 = 'SH.STA.SMSS.UR.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 <- 'SH.STA.SMSS.UR.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

People using safely managed sanitation services, urban (% of urban population)

Year: 2022

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