Mobile cellular subscriptions (per 100 people)

Source: worldbank.org, 01.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 55.5 -1.28% 155
Angola Angola 70.1 +5.18% 146
Albania Albania 93 -5.49% 128
Andorra Andorra 156 +9.6% 23
United Arab Emirates United Arab Emirates 199 +1.94% 3
Argentina Argentina 138 +4.73% 40
Armenia Armenia 135 +3.05% 46
Australia Australia 113 +2.78% 98
Austria Austria 122 +0.479% 79
Azerbaijan Azerbaijan 108 +0.168% 106
Burundi Burundi 63.2 +12.3% 153
Belgium Belgium 103 +1.49% 114
Benin Benin 116 +9.73% 92
Bangladesh Bangladesh 114 +4.08% 94
Bulgaria Bulgaria 118 +1.07% 86
Bahrain Bahrain 154 +10.2% 26
Bahamas Bahamas 100 -1.55% 120
Bosnia & Herzegovina Bosnia & Herzegovina 118 -1.02% 87
Belarus Belarus 129 +0.517% 53
Brazil Brazil 101 -0.222% 119
Brunei Brunei 127 +9.39% 63
Bhutan Bhutan 95.6 +0.522% 124
Botswana Botswana 179 +0.461% 8
Canada Canada 94.1 +3.19% 126
Switzerland Switzerland 129 +3.77% 54
Chile Chile 136 +0.56% 42
China China 128 +3.26% 57
Côte d’Ivoire Côte d’Ivoire 172 +6.67% 11
Cameroon Cameroon 96.5 +7.41% 123
Congo - Kinshasa Congo - Kinshasa 53.2 +9.27% 157
Congo - Brazzaville Congo - Brazzaville 94.9 +1.33% 125
Colombia Colombia 167 +6.9% 15
Comoros Comoros 110 +9.26% 104
Cape Verde Cape Verde 113 -0.198% 96
Costa Rica Costa Rica 146 -5.94% 30
Cuba Cuba 69.6 +1.25% 147
Curaçao Curaçao 93.8 +1.15% 127
Cyprus Cyprus 156 +2.95% 24
Czechia Czechia 126 -1.64% 67
Germany Germany 125 +0.407% 69
Djibouti Djibouti 48.5 +3.58% 160
Denmark Denmark 127 +0.344% 64
Dominican Republic Dominican Republic 91.9 +1.65% 130
Algeria Algeria 112 +3.54% 100
Ecuador Ecuador 101 +2.96% 118
Egypt Egypt 92.8 +2.37% 129
Spain Spain 128 +3.5% 59
Estonia Estonia 150 -1.36% 28
Finland Finland 127 -0.43% 60
France France 117 +0.636% 91
Gabon Gabon 123 -0.508% 77
United Kingdom United Kingdom 123 +2.47% 75
Georgia Georgia 155 +0.803% 25
Ghana Ghana 98.8 -18.2% 121
Guinea-Bissau Guinea-Bissau 128 +1.73% 58
Greece Greece 111 +1.62% 103
Guatemala Guatemala 114 -1.26% 95
Hong Kong SAR China Hong Kong SAR China 319 +9.11% 1
Honduras Honduras 74.4 -1.9% 142
Croatia Croatia 117 +2.12% 90
Hungary Hungary 105 -0.699% 111
Indonesia Indonesia 125 +1.92% 68
India India 80.6 +0.476% 136
Ireland Ireland 111 +0.171% 102
Iran Iran 166 +2.19% 16
Iraq Iraq 101 +2.28% 117
Iceland Iceland 123 +2.72% 72
Israel Israel 153 +1.45% 27
Italy Italy 132 +0.15% 49
Jamaica Jamaica 115 +8.82% 93
Jordan Jordan 67.5 -0.295% 149
Japan Japan 178 +5.83% 9
Kazakhstan Kazakhstan 127 +1.17% 61
Kenya Kenya 121 -0.46% 82
Kyrgyzstan Kyrgyzstan 109 +1.56% 105
Cambodia Cambodia 121 +6.54% 81
Kiribati Kiribati 51.3 +4.36% 158
St. Kitts & Nevis St. Kitts & Nevis 119 +1.68% 84
South Korea South Korea 162 +9.03% 18
Kuwait Kuwait 168 -0.396% 14
Laos Laos 64.8 +2.22% 151
Liechtenstein Liechtenstein 127 +0.595% 62
Sri Lanka Sri Lanka 142 -6.23% 33
Lesotho Lesotho 69.4 +1.84% 148
Lithuania Lithuania 137 +1.01% 41
Luxembourg Luxembourg 144 +3.45% 31
Latvia Latvia 120 +4.09% 83
Macao SAR China Macao SAR China 192 +11.7% 4
Morocco Morocco 148 +4.43% 29
Monaco Monaco 104 +4% 113
Moldova Moldova 131 -4.67% 51
Madagascar Madagascar 75.5 +16.5% 141
Maldives Maldives 142 +3.77% 35
Mexico Mexico 112 +5.53% 101
North Macedonia North Macedonia 105 -5.81% 112
Malta Malta 141 +5.82% 37
Myanmar (Burma) Myanmar (Burma) 121 +12.5% 80
Montenegro Montenegro 207 -0.0193% 2
Mongolia Mongolia 141 -1.21% 36
Mauritania Mauritania 90.8 -17.4% 133
Mauritius Mauritius 165 +0.578% 17
Malawi Malawi 61.1 +2.41% 154
Malaysia Malaysia 143 +3.27% 32
Namibia Namibia 87.7 -12.8% 135
Niger Niger 65.7 +3.72% 150
Nigeria Nigeria 98.5 -1.11% 122
Nicaragua Nicaragua 106 +1.5% 108
Netherlands Netherlands 117 +1.32% 88
New Zealand New Zealand 127 +9.37% 65
Oman Oman 135 -1.16% 44
Pakistan Pakistan 76.5 -3.24% 138
Panama Panama 157 +2.98% 22
Peru Peru 122 -1.73% 78
Philippines Philippines 117 -20.5% 89
Palau Palau 135 +0.181% 43
Papua New Guinea Papua New Guinea 34.1 -30.8% 162
Poland Poland 135 -1.35% 45
Puerto Rico Puerto Rico 125 +3.57% 70
Portugal Portugal 123 +0.164% 74
Paraguay Paraguay 127 -1.16% 66
Palestinian Territories Palestinian Territories 76.7 +0.164% 137
French Polynesia French Polynesia 119 +1.64% 85
Qatar Qatar 158 -2.73% 19
Romania Romania 123 +1.74% 73
Russia Russia 181 +7.34% 6
Rwanda Rwanda 91.5 +13.5% 132
Saudi Arabia Saudi Arabia 158 +5.33% 20
Senegal Senegal 124 +4.88% 71
Singapore Singapore 173 +0.113% 10
El Salvador El Salvador 180 -1.93% 7
Somalia Somalia 54 +8.61% 156
Serbia Serbia 128 -1.03% 55
South Sudan South Sudan 46.6 -3.86% 161
São Tomé & Príncipe São Tomé & Príncipe 63.5 -26.9% 152
Suriname Suriname 157 +5.5% 21
Slovakia Slovakia 138 +1.62% 39
Slovenia Slovenia 129 +2.19% 52
Sweden Sweden 140 -0.367% 38
Eswatini Eswatini 128 +5.69% 56
Seychelles Seychelles 185 +13.1% 5
Syria Syria 72.4 -7.9% 143
Chad Chad 70.2 +7.26% 145
Togo Togo 75.8 +4.95% 140
Thailand Thailand 169 -4.3% 13
Timor-Leste Timor-Leste 113 +4.15% 97
Trinidad & Tobago Trinidad & Tobago 134 +0.448% 47
Tunisia Tunisia 134 +1.71% 48
Turkey Turkey 106 +1.89% 109
Tanzania Tanzania 105 +13.3% 110
Uganda Uganda 76.3 +8.78% 139
Ukraine Ukraine 123 +2.08% 76
Uruguay Uruguay 142 +1.35% 34
United States United States 112 +3.33% 99
Uzbekistan Uzbekistan 107 +1.34% 107
St. Vincent & Grenadines St. Vincent & Grenadines 102 -0.687% 116
Venezuela Venezuela 70.2 +5.65% 144
Vietnam Vietnam 131 -4.98% 50
Vanuatu Vanuatu 89.3 +9.4% 134
Yemen Yemen 50.9 +9.56% 159
South Africa South Africa 172 +6.71% 12
Zambia Zambia 102 +3.68% 115
Zimbabwe Zimbabwe 91.6 +2.96% 131 

                    
# 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.CEL.SETS.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.CEL.SETS.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))