Gold Prices (Monthly in USD)

core

Files Size Format Created Updated License Source
1 45kB csv public_domain_dedication_and_license Bundesbank statistics
Monthly gold prices since 1950 in USD (London market). Data is sourced from the Bundesbank. Data Bundesbank statistic page Notes from the Source General: 1 ounce of fine gold = 31.1034768g. Method of calculation: Since 1 April 1968, calculated from the daily morning fixing; From January 1950 to 21 read more
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Data Files

data  

Field information

Field Name Order Type (Format) Description
date 1 date (fmt:%Y-%m-%d)
price 2 number

Read me

Monthly gold prices since 1950 in USD (London market). Data is sourced from the Bundesbank.

Data

Notes from the Source

General: 1 ounce of fine gold = 31.1034768g. Method of calculation:

  • Since 1 April 1968, calculated from the daily morning fixing;
  • From January 1950 to 21 March 1954, calculated using the Bank of England's gold purchasing price (1 ounce of fine = pound 12.40) in connection with the average exchange rate for the pound in New York (up to the end of 1952; source: Federal Reserve Bulletin) and, from January 1953, midpoint exchange rates for the US dollar in London (source: Financial Times (FT)).
  • From 22 March 1954 to December 1959, calculated using the fixing price for gold bars of approx. 12 1/2 kg and 995/1000 fineness and over (so-called standard bars) according to data from Metallgesellschaft AG, Frankfurt am Main, in connection with the average midpoint exchange rates for the US dollar in London (source: FT).
  • From January 1960 to 14 March 1968, average fixing price for standard bars as specified in the Bank of England's Quarterly Bulletin.
  • On 15 March 1968, fixing price suspended. Gold market split into an official (reserved for central banks) and a free market as a result of the Washington Communique of 17 March 1968. Gold trading suspended from 18 to 29 March 1968.
  • Sources for daily prices: April 1968 - March 1974: FT; April 1974 - December 1980: Samuel Montagu & Co. Ltd.; January 1981 - December 2005: FT; January 2006 - present: Reuters.
  • Comment on 1968-03: Average from 1 to 14 March 1968.

License

The maintainers have licensed under the Public Domain Dedication and License. The source at the Bundesbank indicates no obvious restrictions on the data and the amount means that database rights are doubtful.

Import into your tool

In order to use Data Package in R follow instructions below:

install.packages("devtools")
library(devtools)
install_github("hadley/readr")
install_github("ropenscilabs/jsonvalidate")
install_github("ropenscilabs/datapkg")

#Load client
library(datapkg)

#Get Data Package
datapackage <- datapkg_read("https://pkgstore.datahub.io/core/gold-prices/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"data")

Tested with Python 3.5.2

To generate Pandas data frames based on JSON Table Schema descriptors we have to install jsontableschema-pandas plugin. To load resources from a data package as Pandas data frames use datapackage.push_datapackage function. Storage works as a container for Pandas data frames.

In order to work with Data Packages in Pandas you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-pandas

To get Data Package run following code:

import datapackage

data_url = "https://pkgstore.datahub.io/core/gold-prices/latest/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# to see datasets in this package
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

In order to work with Data Packages in Python you need to install our packages:

$ pip install datapackage

To get Data Package into your Python environment, run following code:

import datapackage

dp = datapackage.DataPackage('https://pkgstore.datahub.io/core/gold-prices/latest/datapackage.json')

# see metadata
print(dp.descriptor)

# get list of csv files
csvList = [dp.resources[x].descriptor['name'] for x in range(0,len(dp.resources))]
print(csvList) # ["resource name", ...]

# access csv file by the index starting 0
print(dp.resources[0].data)

To use this Data Package in JavaScript, please, follow instructions below:

Install datapackage using npm:

$ npm install [email protected]

Once the package is installed, use code snippet below


const Datapackage = require('datapackage').Datapackage

async function fetchDataPackageAndData(dataPackageIdentifier) {
  const dp = await new Datapackage(dataPackageIdentifier)
  await Promise.all(dp.resources.map(async (resource) => {
    if (resource.descriptor.format === 'geojson') {
      const baseUrl = resource._basePath.replace('/datapackage.json', '')
      const resourceUrl = `${baseUrl}/${resource._descriptor.path}`
      const response = await fetch(resourceUrl)
      resource.descriptor._values = await response.json()
    } else {
      // we assume resource is tabular for now ...
      const table = await resource.table
      // rows are simple arrays -- we can convert to objects elsewhere as needed
      const rowsAsObjects = false
      resource.descriptor._values = await table.read(rowsAsObjects)
    }
  }))

  // see the data package object
  console.dir(dp)

  // data itself is stored in Resource object, e.g. to access first resource:
  console.log(dp.resources[0]._values)

  return dp
}


fetchDataPackageAndData('https://pkgstore.datahub.io/core/gold-prices/latest/datapackage.json');

Our JavaScript is written using ES6 features. We are using node.js v7.4.0 and passing --harmony option to enable ES6:

$ node --harmony index.js

In order to work with Data Packages in SQL you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-sql
$ pip install sqlalchemy

To import Data Package to your SQLite Database, run following code:

import datapackage
from sqlalchemy import create_engine

data_url = 'https://pkgstore.datahub.io/core/gold-prices/latest/datapackage.json'
engine = create_engine('sqlite:///:memory:')

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'sql', engine=engine)

# to see datasets in this package
storage.buckets

# to execute sql command (assuming data is in "data" folder, name of resource is data and file name is data.csv)
storage._Storage__connection.execute('select * from data__data___data limit 1;').fetchall()

# description of the table columns
storage.describe('data__data___data')