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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
3 | 123kB | csv zip | 6 years ago | 5 years ago | Open Data Commons Public Domain Dedication and License v1.0 | Bureau of Economics Analysis (US Government) |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
year | 2kB | csv (2kB) , json (9kB) | ||
quarter | 10kB | csv (10kB) , json (33kB) | ||
gdp-us_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 19kB | zip (19kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
date | 1 | year | 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 |
Signup to Premium Service for additional or customised data - Get Started
This is a preview version. There might be more data in the original version.
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 |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/gdp-us
data info core/gdp-us
tree core/gdp-us
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/gdp-us/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/gdp-us/r/0.csv
curl -L https://datahub.io/core/gdp-us/r/1.csv
curl -L https://datahub.io/core/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/core/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/core/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/core/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/core/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)
}
}
})()
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.
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.
Requires Python. Install the requirements:
pip install -r scripts/requirements.txt
Then run the script to get the data:
python scripts/process.py
Public Domain Dedication and License.
Note we assume source data is public domain as US Federal Government.
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