Bank of England Interest Rate unlisted

core

Files Size Format Created Updated License Source
2 0B csv zip 5 months ago Open Data Commons Public Domain Dedication and Licence (PDDL) Bank of England Website (originally an xls)
Interest Rate since 1694 from Bank of England. Data Data comes from the Bank of England Website www.bankofengland.co.uk. Rate is either minimum lending rate, minimum band 1 dealing rate, or repo rate depending on era. Converted by assuming where month or day not given that it was the first of the read more
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Data Files

File Description Size Last changed Download
data 13kB csv (13kB) , json (30kB)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 13kB zip (13kB)

data  

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) date
rate 2 number (default) in percent %

Read me

Interest Rate since 1694 from Bank of England.

Data

Data comes from the Bank of England Website www.bankofengland.co.uk. Rate is either minimum lending rate, minimum band 1 dealing rate, or repo rate depending on era. Converted by assuming where month or day not given that it was the first of the month (see data.original.csv)

Source

Bank of England Website (originally an xls) www.bankofengland.co.uk/statistics/Documents/rates/baserate.xls

License

Public Domain Dedication and License.

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/core/interest_rates-gb
tree core/interest_rates-gb
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/interest_rates-gb/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/interest_rates-gb/r/0.csv

curl -L https://datahub.io/core/interest_rates-gb/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/interest_rates-gb/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/interest_rates-gb/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/interest_rates-gb/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/interest_rates-gb/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