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10 year US Government Bond Yields (long-term interest rate)

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core

Files Size Format Created Updated License Source
2 96kB csv zip 1 year ago 6 days ago Open Data Commons Public Domain Dedication and License v1.0 Federal Reserve (Release H.15)
10 year nominal yields on US government bonds from the Federal Reserve. The 10 year government bond yield is considered a standard indicator of long-term interest rates. Data Data comes from the [Release H.15 from the Federal Reserve - Selected Interest Rates Daily][fed] specifically the [10 year read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
monthly 26kB csv (26kB) , json (42kB)
bond-yields-us-10y_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 13kB zip (13kB)

monthly  

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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)
Rate 2 number (default) Percent per year

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/core/bond-yields-us-10y
data info core/bond-yields-us-10y
tree core/bond-yields-us-10y
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/bond-yields-us-10y/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/bond-yields-us-10y/r/0.csv

curl -L https://datahub.io/core/bond-yields-us-10y/r/1.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/bond-yields-us-10y/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/bond-yields-us-10y/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/bond-yields-us-10y/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/bond-yields-us-10y/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)
    }
  }
})()

Read me

10 year nominal yields on US government bonds from the Federal Reserve. The 10 year government bond yield is considered a standard indicator of long-term interest rates.

Data

Data comes from the Release H.15 from the Federal Reserve - Selected Interest Rates Daily specifically the 10 year US Treasury (monthly, csv).

Preparation

You will need Python 3.6 or greater and dataflows library to run the script

To update the data run the process script locally:

# Install dataflows
pip install dataflows

# Run the script
python flows/run.py

Note we keep a copy of the raw data from the Federal Reserve (pre-tidying) in archive.

License

Licensed under the Public Domain Dedication and License (assuming either no rights or public domain license in source data).


Keywords and keyphrases: us 10 year bond yield historical data, 10 year us government bond yield, long term government bond rate, us bond rates.
Datapackage.json

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