Files Size Format Created Updated License Source
4 0B xlsx csv zip 4 months ago

Data Files

File Description Size Last changed Download
sample-2sheets 5kB xlsx (5kB)
sample-2sheets-sheet-1 64B csv (64B) , json (166B)
sample-2sheets-sheet-2 64B csv (64B) , json (166B)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 14kB zip (14kB)


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


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

Field information

Field Name Order Type (Format) Description
header1 1 string (default)
header2 2 string (default)
header3 3 string (default)


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

Field information

Field Name Order Type (Format) Description
header4 1 string (default)
header5 2 string (default)
header6 3 string (default)

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the almost like you use git with the github. Here is installation instructions.

data get
tree anuveyatsu/sample-2sheets-blue-eagle-55

Also you can get the data using cURL:

# Get a list of dataset's resources
curl -L -s | grep path

# Get resources





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

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

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