Country

Indicator

Value type

Values present

Fixed telephone subscriptions (per 100 people)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	0.439	+9.75%	116
Angola	0.236	-10.6%	120
Albania	6.16	-1.75%	86
Andorra	63.9	+0.157%	2
United Arab Emirates	21.2	-4.93%	42
Argentina	15.4	-0.645%	55
Armenia	11.2	-11.8%	70
Australia	24.4	-6.51%	34
Austria	39.5	-2.71%	13
Azerbaijan	15.8	-0.629%	53
Burundi	0.105	-6.25%	125
Belgium	22.8	-10.6%	39
Benin	0.0101	-9.01%	131
Bangladesh	0.176	+3.53%	121
Bulgaria	9.11	-9.8%	77
Bahrain	15.7	-8.72%	54
Bahamas	24.2	+11%	35
Bosnia & Herzegovina	19.3	-4.93%	47
Belarus	45.8	-0.651%	10
Brazil	12.1	-6.92%	65
Brunei	26.5	-1.12%	31
Bhutan	2.34	-6.77%	104
Botswana	3.62	-3.72%	93
Canada	27.7	-5.78%	29
Switzerland	33.9	-0.877%	19
Chile	10.1	-10.6%	75
China	12.2	-3.17%	64
Côte d’Ivoire	0.784	-9.47%	114
Colombia	13.9	-4.79%	58
Comoros	1.04	+17.8%	111
Cape Verde	11.6	+5.45%	68
Costa Rica	13.3	+37.5%	60
Cuba	14.4	+1.41%	57
Cyprus	29.3	-9.29%	27
Czechia	11.1	-4.31%	71
Germany	45.9	0%	9
Djibouti	2.49	+1.22%	102
Denmark	12.7	+5.83%	63
Dominican Republic	10.1	-0.98%	75
Algeria	13.7	+11.4%	59
Ecuador	7.98	-13.4%	79
Egypt	10.9	+5.83%	72
Spain	38.5	-1.53%	15
Estonia	17.4	-11.7%	50
Finland	2.82	-13.2%	99
France	56	-1.93%	3
United Kingdom	38.8	-12%	14
Georgia	7.3	-12.4%	84
Ghana	0.946	-5.02%	112
Greece	48.4	+3.42%	6
Guatemala	10.7	0%	73
Hong Kong SAR China	46.8	-4.88%	8
Honduras	4.17	+5.3%	90
Croatia	30.9	-2.22%	24
Hungary	27.8	-5.44%	28
Indonesia	3.26	+7.95%	97
India	2.15	+11.4%	107
Ireland	22.6	-5.44%	40
Iran	32	-2.44%	22
Iraq	4.39	-19.2%	89
Iceland	21.3	-13.1%	41
Israel	31.4	-20.1%	23
Italy	33.8	+1.5%	20
Jamaica	16.2	+3.18%	52
Jordan	3.94	-1.25%	92
Japan	48	-0.621%	7
Kazakhstan	12.7	-9.93%	63
Kenya	0.123	+6.03%	123
Kyrgyzstan	2.62	-15.5%	101
Kiribati	0.0196		129
South Korea	42.8	-2.73%	12
Kuwait	11.8	-5.6%	67
Liechtenstein	25.9	-5.82%	32
Sri Lanka	7.43	-34.2%	83
Lesotho	0.318	+7.8%	118
Lithuania	7.83	-11.6%	80
Latvia	7.52	-18.9%	82
Macao SAR China	12.2	-6.87%	64
Morocco	7.62	+7.48%	81
Monaco	111	+0.909%	1
Moldova	27.6	-11.8%	30
Madagascar	0.0109	-91.6%	130
Maldives	2.44	-4.31%	103
Mexico	19.8	-3.88%	46
Malta	48.6	-1.02%	5
Myanmar (Burma)	1.09	+9.44%	110
Montenegro	30	-3.23%	26
Mongolia	15.3	+9.29%	56
Mauritius	36.4	+0.552%	17
Malawi	0.0228	-50.4%	128
Malaysia	23.9	-2.05%	36
Namibia	2.74	-7.74%	100
Nigeria	0.0491	+12.9%	127
Nicaragua	3.43	+6.85%	95
Netherlands	23.6	-7.45%	37
New Zealand	12.8	-13.5%	62
Oman	11.2	-1.75%	70
Pakistan	1.04	-9.57%	111
Panama	18.2	-5.21%	48
Peru	4.44	-17.3%	88
Philippines	4.03	-6.06%	91
Palau	45.1	+0.222%	11
Poland	12.9	-5.15%	61
Puerto Rico	23.4	+2.63%	38
Portugal	52.8	+1.15%	4
Paraguay	3	+20.5%	98
Palestinian Territories	7.09	-17.8%	85
Qatar	17.6	-2.76%	49
Romania	10.3	-11.2%	74
Rwanda	0.0599	-20.9%	126
Saudi Arabia	20.4	-3.32%	45
Senegal	2.21	+31.5%	106
Singapore	33	-5.98%	21
Serbia	37.4	-2.09%	16
South Sudan	0		132
São Tomé & Príncipe	0.848	-23.6%	113
Suriname	20.5	+17.8%	44
Slovakia	9.16	-7.29%	76
Slovenia	30.5	-4.39%	25
Sweden	8.51	-19%	78
Eswatini	3.41	+7.23%	96
Seychelles	14.4	-5.26%	57
Syria	11.9	-4.03%	66
Togo	0.715	-0.97%	115
Thailand	5.7	-6.4%	87
Timor-Leste	0.129	-3.73%	122
Trinidad & Tobago	20.7	-5.05%	43
Tunisia	15.3	+3.38%	56
Turkey	11.4	-11.6%	69
Tanzania	0.114	-13%	124
Uganda	0.237	+6.28%	119
Ukraine	3.5	-17.5%	94
Uruguay	35.6	-4.04%	18
United States	25.6	-6.91%	33
Uzbekistan	17.2	+5.52%	51
St. Vincent & Grenadines	10.1	-6.48%	75
Vietnam	2.31	-3.75%	105
South Africa	2.14	+1.9%	108
Zambia	0.392	-18%	117
Zimbabwe	1.89	+4.42%	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 = 'IT.MLT.MAIN.P2'

# 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 <- 'IT.MLT.MAIN.P2'

# 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

Fixed telephone subscriptions (per 100 people)

Year: 2023

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