Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	36.4	+0.0946%	75
Australia	99.5	-0.0207%	5
Azerbaijan	92.3	0%	20
Bangladesh	54.2	+0.0459%	62
Brazil	88.9	+0.549%	24
Bhutan	58.9	+3.48%	55
Botswana	72.9	0%	44
Central African Republic	11.3	0%	87
Chile	99.4	0%	7
China	97.9	+0.193%	10
Côte d’Ivoire	58.8	-0.157%	56
Congo - Kinshasa	24.2	+0.504%	82
Colombia	81.2	-0.0611%	33
Costa Rica	80.4	0%	35
Czechia	98	0%	9
Germany	100	0%	2
Dominican Republic	46.9	-0.0162%	67
Algeria	73.1	0%	43
Ecuador	74.7	0%	42
Spain	99.8	0%	4
Ethiopia	38.6	+0.365%	74
Fiji	52.5	+0.0139%	63
France	100	+0.0679%	1
Georgia	88	+0.0384%	27
Ghana	62.6	+0.257%	52
Gibraltar	100	0%	1
Gambia	67.8	+0.144%	48
Guinea-Bissau	36.3	0%	76
Guatemala	65.5	+0.116%	49
Hong Kong SAR China	100	0%	1
Honduras	78.4	+0.0703%	37
Croatia	96.6	0%	16
Hungary	100	0%	1
Indonesia	34.6	+0.422%	78
Iran	96.2	+0.0297%	17
Iraq	64.6	0%	50
Israel	99.5	-0.0151%	6
Kyrgyzstan	91.8	0%	21
Cambodia	57.5	+1.27%	57
Kiribati	20.4	+0.823%	84
Kuwait	100	0%	1
Laos	27	0%	80
Sri Lanka	83	-2.63%	30
Lesotho	72.6	+0.16%	45
Lithuania	99.3	0%	8
Luxembourg	99.8	0%	3
Macao SAR China	100	0%	1
Saint Martin (French part)	96.6	0%	15
Morocco	90.3	0%	22
Monaco	100	0%	1
Madagascar	41.3	+2.83%	71
North Macedonia	84.6	0%	29
Myanmar (Burma)	72.4	0%	46
Montenegro	87.3	0%	28
Mongolia	51.3	+1.9%	65
Malawi	52.4	+1.42%	64
Nigeria	35.7	+0.731%	77
Nepal	23.2	0%	83
Pakistan	56.8	+1.04%	58
Peru	59.8	+0.265%	54
Philippines	61.9	+0.157%	53
Palau	97.7	+1.3%	11
North Korea	77	0%	40
Portugal	97.1	-0.00543%	14
Paraguay	72.2	0%	47
Palestinian Territories	81.5	+0.345%	32
Romania	95	0%	18
Rwanda	54.6	+0.563%	61
Senegal	41.3	+0.263%	72
Singapore	100	0%	1
Sierra Leone	12.2	+0.56%	86
El Salvador	78.6	-0.201%	36
Serbia	81.5	0%	31
São Tomé & Príncipe	40.3	0%	73
Suriname	63.4	0%	51
Eswatini	77.5	+0.435%	38
Turks & Caicos Islands	46.5	0%	68
Chad	17.6	0%	85
Togo	33.9	+0.222%	79
Turkmenistan	97.1	0%	13
Tonga	50.8	+0.0877%	66
Tunisia	77.4	+0.0418%	39
Tuvalu	10.5	+0.02%	88
Tanzania	25.5	+1.05%	81
Uganda	45.1	+3.13%	70
Ukraine	88.3	-0.0348%	26
Uruguay	94.6	0%	19
United States	97.6	+0.0906%	12
Uzbekistan	88.8	+0.0785%	25
Vietnam	75.8	+0.139%	41
Vanuatu	56.3	+0.129%	59
Samoa	90.2	0%	23
South Africa	80.5	-0.675%	34
Zambia	45.5	0%	69
Zimbabwe	55.5	0%	60

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.H2O.SMDW.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.H2O.SMDW.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 drinking water services, urban (% of urban population)

Year: 2022

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