Now you can request additional data and/or customized columns!
Try It Now!Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 94kB | csv zip | 6 years ago | 5 years ago | Open Data Commons Public Domain Dedication and License v1.0 |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
core-list | 12kB | csv (12kB) , json (27kB) | ||
registry_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 8kB | zip (8kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
name | 1 | string | Name of the dataset |
github_url | 2 | string | The location in GitHub |
run_date | 3 | string | Last run date |
modified | 4 | string | Frequency information (year-A, quarter-Q, month-M, day-D, no-N) |
validated_metadata | 5 | string | Metadata validation status |
validated_data | 6 | string | Data validation status |
published | 7 | string | Published location on DataHub |
ok_on_datahub | 8 | string | Status on DataHub |
validated_metadata_message | 9 | string | Error messages if validation fails |
validated_data_message | 10 | string | Error messages if validation fails |
auto_publish | 11 | string | Published by DataHub automatically |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/registry
data info core/registry
tree core/registry
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/registry/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/registry/r/0.csv
curl -L https://datahub.io/core/registry/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/registry/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/registry/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/registry/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/registry/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)
}
}
})()
Core data registry and tooling.
Registry is maintained as Tabular Data Package with list of datasets in core-list.csv.
To add a dataset add it to the core-list.csv
- we recommend fork and pull.
Discussion of proposals for new datasets and for incorporation of prepared datasets takes place in the issues.
To propose a new dataset for inclusion, please create a new issue.
$ npm install
DOMAIN
- testing or production environment. For example: https://datahub.io
TYPE
- type of dataset. For example: examples or core
node index.js [COMMAND] [PATH]
# PATH - path to csv file
To clone all core datasets run the following command:
node index.js clone [PATH]
It will clone all core datasets into following directory: data/${pkg_name}
To check all core datasets run the following command:
node index.js check [PATH]
It will validate metadata and data according to the latest spec.
To normalize all core datasets run the following command:
node index.js norm [PATH]
It will normalize all core datasets into following directory: data/${pkg_name}
To publish all core data packages run the following command:
node index.js push [PATH]
We use Ava for our tests. For running tests use:
$ [sudo] npm test
To run tests in watch mode:
$ [sudo] npm run watch:test
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
Customized data (e.g. you need different or additional data)
Or suggest your own feature from the link below