Gross Domestic Product of the United States (US GDP)

Mikanebu

Files Size Format Created Updated License Source
3 0B csv zip 5 months ago public_domain_dedication_and_license Bureau of Economics Analysis (US Government)
Gross Domestic Product (GDP) of the United States (US) both nominal and real on an annual and quarterly basis. Annual data is provided since 1930 and quarterly data since 1947. Both total GDP (levels) and annualized percentage change in GDP are provided. Both levels and changes are available both read more
Download

Data Files

File Description Size Last changed Download
year 3kB csv (3kB) , json (10kB)
quarter 9kB csv (9kB) , json (31kB)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 17kB zip (17kB)

year  

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

Field information

Field Name Order Type (Format) Description
date 1 date (%Y-%m-%d) The year
level-current 2 number GDP in billions of current dollars
level-chained 3 number GDP in billions of chained 2009 dollars
change-current 4 number GDP percent change based on current dollars
change-chained 5 number GDP percent change based on chained 2009 dollars

quarter  

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

Field information

Field Name Order Type (Format) Description
date 1 date (%Y-%m-%d) The quarter (first day of the quarter)
level-current 2 number GDP in billions of current dollars
level-chained 3 number GDP in billions of chained 2009 dollars
change-current 4 number GDP percent change based on current dollars
change-chained 5 number GDP percent change based on chained 2009 dollars

Read me

Gross Domestic Product (GDP) of the United States (US) both nominal and real on an annual and quarterly basis. Annual data is provided since 1930 and quarterly data since 1947. Both total GDP (levels) and annualized percentage change in GDP are provided. Both levels and changes are available both in current dollars (nominal GDP) and in chained 2009 dollars (real GDP). Data is sourced from US Government’s Bureau of Economic Analysis (BEA) and provided in standardized CSV.

Data

The calculation of GDP and, in particular, chained measures of GDP involves some complexities. You can read more about the benefits and issues of BEA’s Chain Indexes in the BEA’s 1997 Survey of Current Business.

Preparation

Requires Python. Install the requirements:

pip install -r scripts/requirements.txt

Then run the script to get the data:

python scripts/process.py

License

Public Domain Dedication and License.

Note we assume source data is public domain as US Federal Government.

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 datahub.io almost like you use git with the github. Here are installation instructions.

data get https://datahub.io/Mikanebu/gdp-us
tree Mikanebu/gdp-us
# Get a list of dataset's resources
curl -L -s https://datahub.io/Mikanebu/gdp-us/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/Mikanebu/gdp-us/r/0.csv

curl -L https://datahub.io/Mikanebu/gdp-us/r/1.csv

curl -L https://datahub.io/Mikanebu/gdp-us/r/2.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/Mikanebu/gdp-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/Mikanebu/gdp-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/Mikanebu/gdp-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/Mikanebu/gdp-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