US Employment and Unemployment rates since 1940

core

Files Size Format Created Updated License Source
2 38kB csv zip 5 days 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. US Employment and Unemployment rates since 1940 From the USA Bureau of Labor read more
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Data Files

File Description Size Last changed Download
aat1 CSV file (derived) 5kB csv (5kB) , json (20kB)
employment-us_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 11kB zip (11kB)

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

employment-us_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.

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

License

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

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 <- 'https://datahub.io/core/employment-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)
  }
}

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 = 'https://datahub.io/core/employment-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/employment-us/datapackage.json')

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

# print all tabular data(if exists any)
resources = package.resources
for resource in resources:
    if resource.tabular:
        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/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 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)
    }
  }
})()
Datapackage.json