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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 48kB | geojson zip | 5 years ago | 5 years ago | Natural Earth |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
admin1-us | 108kB | geojson (108kB) | ||
geo-admin1-us_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 34kB | zip (34kB) |
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This is a preview version. There might be more data in the original version.
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/geo-admin1-us
data info core/geo-admin1-us
tree core/geo-admin1-us
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/geo-admin1-us/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/geo-admin1-us/r/0.geojson
curl -L https://datahub.io/core/geo-admin1-us/r/1.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/core/geo-admin1-us/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
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 = 'https://datahub.io/core/geo-admin1-us/datapackage.json'
# 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('https://datahub.io/core/geo-admin1-us/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
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 = 'https://datahub.io/core/geo-admin1-us/datapackage.json'
// 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) {
console.log(dataset.resources[id]._descriptor.name)
}
// 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 file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()
Geodata data package providing geojson polygons for the states in the USA.
The data comes from Natural Earth, a community effort to make visually pleasing, well-crafted maps with cartography or GIS software at small scale.
This dataset covers the United States of America. admin1 are the biggest administrative area below the country : ie the states. See documentation for more information.
The shape of the admin1 have four fields :
adm1_code
for the subdivision inside the country. Documentation is not clear what this code is, but it could be FIPSAll data is licensed under the Open Data Commons Public Domain Dedication and License.
Note that the original data from Natural Earth is public domain. While no credit is formally required a link back or credit to Natural Earth, Lexman and the Open Knowledge Foundation is much appreciated.
All source code is licenced under the MIT licence.
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
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