Depositors with commercial banks (per 1,000 adults)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 1,252 +9.76% 28
United Arab Emirates United Arab Emirates 1,516 +17.5% 17
Argentina Argentina 981 +2.18% 37
Azerbaijan Azerbaijan 1,791 +4.11% 8
Bangladesh Bangladesh 1,061 +7.93% 33
Bulgaria Bulgaria 1,408 +0.257% 22
Bahrain Bahrain 2,191 +8.21% 4
Belize Belize 867 +1.07% 41
Brazil Brazil 912 +9.41% 39
Botswana Botswana 839 -2.18% 43
China China 48.5 +9.25% 62
Colombia Colombia 1,606 -3.01% 13
Cape Verde Cape Verde 1,526 -12.5% 15
Cyprus Cyprus 1,268 -0.384% 26
Djibouti Djibouti 142 +2.92% 59
Dominican Republic Dominican Republic 770 +3.29% 44
Ecuador Ecuador 715 +4.62% 47
Egypt Egypt 392 +3.72% 58
Estonia Estonia 2,123 +2.81% 5
Georgia Georgia 1,050 -2.75% 34
Guinea Guinea 131 +18.5% 60
Croatia Croatia 1,525 -0.919% 16
Hungary Hungary 1,253 +0.706% 27
Indonesia Indonesia 1,888 +1.74% 7
Israel Israel 1,065 +0.716% 32
Italy Italy 760 +2.05% 46
Kyrgyzstan Kyrgyzstan 1,584 +31.5% 14
Kuwait Kuwait 1,440 +6.73% 20
Laos Laos 874 +9.58% 40
Lebanon Lebanon 489 -6.29% 53
Lesotho Lesotho 433 +7.43% 55
Latvia Latvia 1,274 +1.09% 25
Moldova Moldova 1,674 +5.56% 11
Maldives Maldives 1,769 +14.4% 9
North Macedonia North Macedonia 1,247 +2.16% 30
Malta Malta 1,478 -2.53% 19
Mauritius Mauritius 1,956 +1.66% 6
Malaysia Malaysia 703 +0.923% 48
Nicaragua Nicaragua 422 -5.12% 57
Pakistan Pakistan 585 +25.8% 51
Peru Peru 1,435 +8.66% 21
Poland Poland 1,359 -1.3% 23
Paraguay Paraguay 1,025 +31.7% 35
Palestinian Territories Palestinian Territories 766 +0.426% 45
Rwanda Rwanda 509 +39.5% 52
Saudi Arabia Saudi Arabia 1,747 +18.4% 10
Singapore Singapore 2,902 +1.91% 1
Solomon Islands Solomon Islands 468 -1.57% 54
Sierra Leone Sierra Leone 426 +15.1% 56
El Salvador El Salvador 913 +7.57% 38
San Marino San Marino 1,617 -0.203% 12
South Sudan South Sudan 114 +7.31% 61
Suriname Suriname 1,244 +1.74% 31
Thailand Thailand 1,509 +4.05% 18
Timor-Leste Timor-Leste 660 +2.46% 49
Turkey Turkey 2,404 +10.1% 3
Uganda Uganda 601 -1.61% 50
Ukraine Ukraine 2,608 +5.66% 2
Uruguay Uruguay 1,251 +4.79% 29
Uzbekistan Uzbekistan 850 +9.61% 42
St. Vincent & Grenadines St. Vincent & Grenadines 990 +1.53% 36
Samoa Samoa 1,357 +32.6% 24 

                    
# 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.DPTR.P3'

# 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.DPTR.P3'

# 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))