10y UK Government Bond Yields (long-term interest rate)

core

Files Size Format Created Updated License Source
3 81kB csv zip 6 months ago 1 week ago Open Data Commons Public Domain Dedication and License v1.0 Bank of England
10 year nominal yields on UK government bonds from the bank of England. The 10 year government bond yield is considered a standard indicator of long-term interest rates. This is a direct extract from the Bank of [England IUAAMNPY series: "Annual average yield from British Government Securities, 10 read more
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Data Files

File Description Size Last changed Download
quarterly 3kB csv (3kB) , json (5kB)
annual 642B csv (642B) , json (1kB)
bond-yields-uk-10y_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 7kB zip (7kB)

quarterly  

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 Quarterly average yield from British Government Securities, 10 year Nominal Par Yield

annual  

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

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d)
Rate 2 number Annual average yield from British Government Securities, 10 year Nominal Par Yield

Read me

10 year nominal yields on UK government bonds from the bank of England. The 10 year government bond yield is considered a standard indicator of long-term interest rates. This is a direct extract from the Bank of England IUAAMNPY series: “Annual average yield from British Government Securities, 10 year Nominal Par Yield”.

Data

Data from Bank of England (series IUAAMNPY “Annual average yield from British Government Securities, 10 year Nominal Par Yield”) with some minor processing (see scripts).

Full information about the BoE Yields data may be found on the BoE website at: http://www.bankofengland.co.uk/statistics/Pages/iadb/notesiadb/Yields.aspx

There are several relevant series:

License

The Bank of England Terms of Use appear only to allow non-commercial use:

Statistical Interactive Database (IADB) Terms and Conditions

The content of the database is for general information only, and is provided to users free of charge. Commercial use for financial gain is not permitted without the express permission of the Bank of England. The Bank of England reserves the right to terminate or restrict user access if it determines that a user is acting in a manner contrary to the interests of other users of the database e.g. excessive usage. [retrieved 2013-04-07]

However, the amounts of data provided in this dataset is so minimal as likely to fall below any threshold for Database Rights.

As such the maintainers feel warranted in putting the dataset out under the Public Domain Dedication and License but that they can, obviously, only license (or dedicate) material they control (or in which there are no rights).

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Download CLI tool and use it with the datahub almost like you use git with the github:

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

# Get resources

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

curl -L https://datahub.io/core/bond-yields-uk-10y/r/1.csv

curl -L https://datahub.io/core/bond-yields-uk-10y/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/bond-yields-uk-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-uk-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-uk-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-uk-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)
    }
  }
})()
Datapackage.json