Country

Indicator

Value type

Values present

Automated teller machines (ATMs) (per 100,000 adults)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Aruba	109	-2.46%	17
Angola	17.5	+7.36%	111
Albania	40.1	+9.26%	79
Andorra	92.7	-3.72%	23
United Arab Emirates	57.7	+4.3%	50
Argentina	74.5	+0.49%	32
Armenia	74.6	-0.441%	31
Antigua & Barbuda	55.7	-9.43%	53
Australia	111	-5.22%	14
Austria	172	-4.64%	4
Azerbaijan	39.9	+0.99%	81
Benin	4.12	+3.79%	136
Burkina Faso	4.38	-0.936%	134
Bangladesh	13.6	+7.96%	116
Bulgaria	92.7	+3.56%	22
Bahamas	117	+0.455%	13
Bosnia & Herzegovina	62.7	+4.45%	44
Belarus	54.4	-1.57%	55
Belize	45.5	+0.968%	70
Bolivia	40.1	-10.1%	78
Brazil	111	+13.3%	15
Botswana	35.6	+0.396%	88
Canada	189	-8.49%	3
Switzerland	85.4	-4.56%	26
Chile	47.6	+1.15%	62
China	72	-6.06%	35
Colombia	39	-0.755%	82
Comoros	7.54	+15%	128
Cape Verde	49	+1.27%	60
Costa Rica	63	-0.838%	43
Cyprus	31.6	-2.53%	93
Czechia	55.6	-4.15%	54
Djibouti	18.6	+20%	110
Dominica	25.5	-21.6%	102
Denmark	34.4	-12.2%	90
Dominican Republic	40.1	+4.06%	80
Algeria	12	+47.1%	120
Ecuador	36.5	-0.0284%	85
Egypt	30.6	+3.92%	96
Spain	87.1	-3.24%	25
Estonia	58.4	-1.58%	48
Ethiopia	10.2	+10.4%	124
Finland	35.3	-1.92%	89
Micronesia (Federated States of)	12.4	-1.47%	118
United Kingdom	84.3	-6.21%	27
Georgia	96.2	-3.1%	20
Ghana	10.6	-1.07%	122
Guinea	2.67	-0.656%	142
Gambia	8.35	-2.49%	126
Guinea-Bissau	7.09	-4.94%	130
Greece	67.6	+1.53%	38
Grenada	36.4	-6.15%	86
Guatemala	36.3	-0.547%	87
Hong Kong SAR China	46.7	-5.75%	65
Honduras	25.4	+1.29%	103
Croatia	129	+2.18%	8
Hungary	59.2	+3.09%	47
Indonesia	46	-3.76%	69
India	25	+1.28%	105
Ireland	30.8	-8.82%	94
Iraq	14.1	+75.7%	113
Iceland	54.1	-18.6%	57
Israel	138	+7.4%	6
Italy	88.8	-1.84%	24
Jamaica	38	+4.63%	83
Japan	110	-2.8%	16
Kenya	6.6	-3.67%	132
Kyrgyzstan	48.4	+8.53%	61
Cambodia	46.5	+22.3%	66
St. Kitts & Nevis	117	-0.544%	12
Kuwait	67.5	-1.52%	39
Laos	28.7	+4.39%	99
Lebanon	31.9	-16%	92
Liberia	3.88	+0.204%	138
St. Lucia	45.2	+2.49%	71
Lesotho	13.9	+2.06%	114
Lithuania	46.8	-1.02%	64
Luxembourg	75.9	-14.4%	30
Latvia	56.2	-0.718%	51
Macao SAR China	260	-1.26%	2
Moldova	61.7	+8.49%	46
Madagascar	3.49	+12.1%	141
Maldives	46.1	+3.83%	68
Mexico	68.7	+3.55%	37
Marshall Islands	10.5	-1.75%	123
North Macedonia	67.1	+1.98%	41
Mali	4.36	-2.13%	135
Malta	41.4	-2.47%	75
Montenegro	83.4	+2.14%	28
Mongolia	30.8	-25.3%	95
Mozambique	7.66	-9.29%	127
Mauritania	10.7	-6.11%	121
Mauritius	41.8	-0.907%	73
Malaysia	51.8	+0.289%	58
Namibia	67.4	-10.8%	40
Niger	1.94	+1.83%	143
Nigeria	13.6	-3.92%	115
Nicaragua	22.7	+7.31%	107
Netherlands	33.5	-1.93%	91
Norway	25.3	-6.5%	104
Nepal	22	+3.81%	108
Nauru	128	+10.1%	10
New Zealand	43.7	-14.2%	72
Pakistan	12	+2.38%	119
Panama	72.6	+0.139%	33
Peru	128	+5.48%	9
Philippines	28.4	-2.27%	100
Poland	70.8	+3.77%	36
Portugal	161	+0.711%	5
Paraguay	28.8	+1.83%	98
Palestinian Territories	23.2	-1.14%	106
Qatar	62.4	+2.67%	45
Romania	63.8	+0.734%	42
Russia	121	-16.9%	11
Rwanda	3.95	-3.45%	137
Saudi Arabia	58	-3.71%	49
Senegal	7.09	-1.57%	129
Singapore	47.5	-6.78%	63
Solomon Islands	12.8	-5.78%	117
Sierra Leone	1.33	+7.67%	144
El Salvador	46.1	+21.6%	67
San Marino	136	-0.454%	7
Serbia	54.4	+1.07%	56
South Sudan	0.933	+9.9%	145
São Tomé & Príncipe	29.7	-2.8%	97
Suriname	19.5	-30.5%	109
Slovakia	72.4	+19.5%	34
Eswatini	41.4	+5.47%	76
Togo	6.97	-1.48%	131
Thailand	97.9	-2%	19
Timor-Leste	16.4	+4.22%	112
Trinidad & Tobago	40.4	+0.595%	77
Turkey	80.5	+0.947%	29
Tanzania	5.57	-0.868%	133
Uganda	3.64	-0.637%	140
Ukraine	93.1	+5.77%	21
Uruguay	315	+4.42%	1
Uzbekistan	105	+28%	18
St. Vincent & Grenadines	36.9	-3.27%	84
Vietnam	27.3	-1.29%	101
Samoa	56	+2.24%	52
Kosovo	41.8	+8.92%	74
South Africa	50.5	+0.142%	59
Zambia	8.5	+4.86%	125
Zimbabwe	3.71	-3.95%	139

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 = 'FB.ATM.TOTL.P5'

# 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 <- 'FB.ATM.TOTL.P5'

# 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

Automated teller machines (ATMs) (per 100,000 adults)

Year: 2023

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