US Employment and Unemployment rates since 1940

core

Files Size Format Created Updated License Source
2 39kB csv zip 1 week ago odc-pddl country-us gov employment economics statistics
US Employment and Unemployment rates since 1940. Official title: *Employment status of the civilian noninstitutional population, 1940 to date* from USA Bureau of Labor Statistics. Data Numbers are in thousands. Licenses As US Federal Government data can assume Public Domain. Maintainer licenses read more
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Data Files

File Description Size Last changed Download Other formats
aat1 [csv] CSV file (derived) 5kB aat1 [csv] aat1 [json] (20kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 9kB datapackage_zip [zip]

aat1  

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

Field information

Field Name Order Type (Format) Description
year 1 year
population 2 integer
labor_force 3 string
population_percent 4 number
employed_total 5 number
employed_percent 6 number
agrictulture_ratio 7 number
nonagriculture_ratio 8 number
unemployed 9 integer
unemployed_percent 10 number
not_in_labor 11 integer
footnotes 12 string

datapackage_zip  

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

Read me

US Employment and Unemployment rates since 1940. Official title: Employment status of the civilian noninstitutional population, 1940 to date from USA Bureau of Labor Statistics.

Data

Numbers are in thousands.

Licenses

As US Federal Government data can assume Public Domain. Maintainer licenses any additional rights from processing and structuring under Public Domain Dedication and License.

Sources

US Employment and Unemployment rates since 1940 From the USA Bureau of Labor Statistics Employment Related Data

Import into your tool

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

install.packages("jsonlite")
library("jsonlite")

json_file <- "http://datahub.io/core/employment-us/datapackage.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources[[1]]$path
data <- read.csv(url(path_to_file))
print(data)

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

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = "http://datahub.io/core/employment-us/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

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('http://datahub.io/core/employment-us/datapackage.json')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

data = package.resources[0].read()
print(data)

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 = 'http://datahub.io/core/employment-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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer
})()

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = 'http://datahub.io/core/employment-us/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data
Datapackage.json