Loaf of Bread Price According to Inflation Rate from 1900 in USD


Files Size Format Created Updated License Source
2 39kB csv zip 4 years ago 4 years ago
This is not the actual price of bread over time. These are the prices that should be in line with the inflation rate. The starting price for the loaf of bread was taken at $ 0.04 from 1900. According to calculations with a cumulative inflation rate, the price in today's USD should be $ 1.3. read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
bread_price 3kB csv (3kB) , json (14kB)
loaf-of-bread-price_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 6kB zip (6kB)


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

Field information

Field Name Order Type (Format) Description
year 1 integer (default)
inflation_rate_% 2 number (default)
inflation_rate_cumulative 3 number (default)
loaf_of_bread_price_in_actual_USD 4 number (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/gavram/loaf-of-bread-price
data info gavram/loaf-of-bread-price
tree gavram/loaf-of-bread-price
# Get a list of dataset's resources
curl -L -s https://datahub.io/gavram/loaf-of-bread-price/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/gavram/loaf-of-bread-price/r/0.csv

curl -L https://datahub.io/gavram/loaf-of-bread-price/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/")

json_file <- 'https://datahub.io/gavram/loaf-of-bread-price/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

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/gavram/loaf-of-bread-price/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/gavram/loaf-of-bread-price/datapackage.json')

# print list of all resources:

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':

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/gavram/loaf-of-bread-price/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) {
  // 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

Read me

This is not the actual price of bread over time. These are the prices that should be in line with the inflation rate. The starting price for the loaf of bread was taken at $ 0.04 from 1900. According to calculations with a cumulative inflation rate, the price in today’s USD should be $ 1.3. However, today the price of bread is between $ 2.2 and $ 4.4, which means that loaf of bread is more expensive today than it was in 1900.


Data obtained from:


Data is obtained using bread_dataflows.py script from the aforementioned web addresses. The script uses the Dataflows library and creates a dataset - data.csv file and a datapackage.json file with visualization. This dataset is published at DataHub.io https://datahub.io/gavram/loaf-of-bread-price. The dataset is very clean, with no missing values and except for the year and the price of bread, it contains an annual and cumulative inflation rate from 1900.


  • Clone or copy this repo on your local computer.
  • Change path to folder where requirements.txt is located.
  • To install requirements type or copy in your terminal: pip install -r requirements.txt
  • To run bread_dataflows.py type or copy in your terminal: python bread_dataflows.py


Creative Commons Attribution-NonCommercial 4.0 (CC-BY-NC-4.0) https://creativecommons.org/licenses/by-nc/4.0/ Data and Python scripts are free to use and distribute