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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 98kB | csv zip | 6 years ago | 3 years ago | Open Data Commons Public Domain Dedication and License v1.0 | Federal Reserve (Release H.15) |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
monthly | 27kB | csv (27kB) , json (43kB) | ||
bond-yields-us-10y_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 14kB | zip (14kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Date | 1 | date (%Y-%m-%d) | |
Rate | 2 | number (default) | Percent per year |
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)
}
}
})()
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 comes from the Release H.15 from the Federal Reserve - Selected Interest Rates Daily specifically the 10 year US Treasury (monthly, csv).
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
.
Licensed under the Public Domain Dedication and License (assuming either no rights or public domain license in source data).
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
Customized data (e.g. you need different or additional data)
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