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IPv4 geolocation



Files Size Format Created Updated License Source
2 2MB csv zip 6 years ago 5 years ago Public Domain Dedication and License Maxmind GeoLite2
Database of IPv4 address networks with their respective geographical location. Data Based on GeoLite2 Country Free Downloadable Databases as of Apr 21, 2015 Two files were used to generate this read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
geoip2-ipv4 10MB csv (10MB) , json (37MB)
geoip2-ipv4_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 2MB zip (2MB)


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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
network 1 string This is the IPv4 network in CIDR format such as
geoname_id 2 integer A unique identifier for the network's location as specified by GeoNames.
continent_code 3 string The continent code for this IP. Possible codes are: AF - Africa, AS - Asia, EU - Europe, NA - North America, OC - Oceania, SA - South America
continent_name 4 string The continent name for this location
country_iso_code 5 string A two-character ISO 3166-1 country code for the country associated with the location.
country_name 6 string The country name for this location.
is_anonymous_proxy 7 boolean A 1 if the network is an anonymous proxy, otherwise 0.
is_satellite_provider 8 boolean A 1 if the network is for a satellite provider that provides service to multiple countries, otherwise 0.

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get
data info core/geoip2-ipv4
tree core/geoip2-ipv4
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="")

json_file <- ''
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = ''

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('')

# print list of all resources:

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = ''

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data

Read me

Database of IPv4 address networks with their respective geographical location.


Based on GeoLite2 Country Free Downloadable Databases as of Apr 21, 2015

Two files were used to generate this dataset:


with the following considerations:

  • Where geoname_id was not available, registered_country_geoname_id was used.
  • Where geoname_id and registered_country_genoname_id where empty, geoname_id, continent_code, continent_name, country_iso_code and country_name are empty.


Original CSVs were imported into a MySQL database, then with a script an additional CSV was created combining Country names, locations and IPs.


Datapackage: Creative Commons Zero

Original CSV: This dataset includes GeoLite2 data created by MaxMind, available from


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