Country

Indicator

Value type

Values present

Commercial bank branches (per 100,000 adults)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Aruba	10	-0.47%	82
Angola	7.16	-5.63%	99
Albania	16.9	-2.03%	43
United Arab Emirates	7.71	-2.38%	97
Argentina	12.4	-3.3%	69
Armenia	24.7	-5.06%	24
Antigua & Barbuda	16.9	-14.2%	45
Australia	16.9	-12.7%	44
Austria	11	-3.91%	75
Azerbaijan	6.39	-3.16%	104
Bangladesh	8.81	-0.334%	90
Bulgaria	92	+16.4%	2
Bahrain	14.6	-13.2%	58
Bahamas	21.7	-7.66%	31
Bosnia & Herzegovina	29.2	-0.934%	19
Belize	14.7	+0.277%	57
Bolivia	62.6	-15.5%	3
Brazil	16	-3.17%	51
Barbados	13.2	+2.83%	65
Botswana	8.16	+5.6%	94
Canada	19	-3.77%	39
Switzerland	34.1	-2.67%	9
Chile	9.48	-5.28%	85
China	8.77	-0.263%	91
Colombia	12.8	+0.149%	66
Cape Verde	29.9	-1.51%	17
Cyprus	21.1	-3.38%	34
Czechia	15.4	-7.25%	54
Germany	6.62	-5.75%	102
Djibouti	9.69	+3.51%	84
Dominica	10.2	-25.5%	81
Denmark	16.7	-4.75%	46
Dominican Republic	10.8	+0.0374%	76
Algeria	5.26	-0.505%	111
Ecuador	8.84	-1%	89
Egypt	6.21	-0.788%	105
Spain	32.4	-3.4%	12
Estonia	6.56	-4%	103
Ethiopia	14.5	+20.3%	59
Finland	5.87	-1.51%	107
France	31.3	-2.75%	14
Micronesia (Federated States of)	12.4	-1.47%	70
Georgia	31.2	-0.55%	15
Ghana	4.44	-22.8%	116
Guinea	2.8	+2.37%	123
Gambia	5.72	-2.14%	108
Greece	15.7	-3.92%	52
Grenada	16.6	-16.5%	48
Guatemala	22.8	-1.95%	29
Hong Kong SAR China	18.2	-4.38%	41
Honduras	15.6	+1.9%	53
Croatia	24.7	-5.79%	23
Hungary	16.6	-4.48%	47
Indonesia	11.7	-5.44%	74
India	14.5	+1.06%	60
Iraq	3.2	-6.24%	120
Iceland	23.3	-8.06%	27
Israel	14.2	-2.12%	64
Italy	31.5	-4.78%	13
Jamaica	6.68	+3.64%	101
Jordan	12.2	-2.44%	72
Japan	33.7	+0.0283%	11
Kenya	4.48	-0.561%	115
Kyrgyzstan	7.35	-1.42%	98
Cambodia	14.3	+13.7%	62
St. Kitts & Nevis	33.7	-7.65%	10
South Korea	12.7	-1.38%	67
Kuwait	12.3	-0.987%	71
Lebanon	19	-8.46%	38
Liberia	3.05	+0.0553%	122
St. Lucia	14.8	-4.9%	56
Lesotho	3.5	-1.27%	118
Lithuania	10.3	-2.84%	80
Luxembourg	48.4	-7.53%	6
Latvia	4.65	-10%	113
Macao SAR China	44.3	-0.316%	7
Morocco	20.9	-3.12%	36
Moldova	28.2	-2.03%	20
Maldives	14.9	+0.52%	55
Mexico	12.2	-1.44%	73
Marshall Islands	28	-1.75%	21
North Macedonia	24.7	-0.606%	25
Malta	21.7	-2.05%	32
Montenegro	35.6	-21.8%	8
Mongolia	59.8	-1.44%	4
Mozambique	3.48	-3.56%	119
Mauritius	14.3	-2.18%	63
Malaysia	8.07	-1.72%	95
Nicaragua	7.71	-0.878%	96
Netherlands	4.58	-11.5%	114
Nepal	22.9	-0.815%	28
New Zealand	16	-5.17%	50
Pakistan	10.8	+0.627%	77
Panama	18.6	-2.68%	40
Peru	4.76	-8.04%	112
Poland	21.8	-3.63%	30
Portugal	29.2	-2.17%	18
Paraguay	10	+1.48%	83
Palestinian Territories	12.5	-1.28%	68
Romania	21.1	-1.74%	35
Russia	21.6	+1.22%	33
Saudi Arabia	7.02	-3.17%	100
Singapore	6.09	-5.2%	106
Solomon Islands	3.76	-2.53%	117
Sierra Leone	2.52	-2.08%	124
El Salvador	9.28	+2.03%	87
San Marino	122	-0.454%	1
Serbia	24	-1.42%	26
South Sudan	1.58	-0.144%	126
Suriname	10.4	-1.15%	79
Slovakia	20	-3.32%	37
Sweden	10.5	-0.58%	78
Thailand	8.35	-4.13%	93
Timor-Leste	5.36	-2.16%	110
Trinidad & Tobago	9.3	-1.45%	86
Turkey	14.4	-1.72%	61
Uganda	2.26	-3.36%	125
Ukraine	0.39	-0.616%	127
Uruguay	8.95	-6.45%	88
United States	26.6	-2.07%	22
Uzbekistan	54.3	+4.36%	5
St. Vincent & Grenadines	17.2	-12.5%	42
Vietnam	3.06	+1.66%	121
Samoa	30.5	+32.2%	16
Kosovo	16.3	+11.6%	49
South Africa	8.72	-4.23%	92
Zimbabwe	5.4	-2.65%	109

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.CBK.BRCH.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.CBK.BRCH.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

Commercial bank branches (per 100,000 adults)

Year: 2023

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