Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 212kB | csv zip | 5 years ago | CBOE VIX Page |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
vix-daily | 11kB | csv (11kB) , json (30kB) | ||
simple-graph-spec_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 16kB | zip (16kB) |
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) | |
VIXOpen | 2 | number | |
VIXHigh | 3 | number | |
VIXLow | 4 | number | |
VIXClose | 5 | number |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/examples/simple-graph-spec
data info examples/simple-graph-spec
tree examples/simple-graph-spec
# Get a list of dataset's resources
curl -L -s https://datahub.io/examples/simple-graph-spec/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/examples/simple-graph-spec/r/0.csv
curl -L https://datahub.io/examples/simple-graph-spec/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/examples/simple-graph-spec/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/examples/simple-graph-spec/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/examples/simple-graph-spec/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/examples/simple-graph-spec/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)
}
}
})()
This is an example dataset, that demonstrates how to build the simple and nice graphs using the “Simple Graph Spec”. We are using CBOE Volatility Index (VIX) time-series dataset for 2015-2016 as an example to create line and bar charts.
We assume that you are familiar with what datapackage.json is and its specifications.
To create graphs for your tabular data, the datapackage.json
should include the views
attribute that is responsible for visualizations.
“Simple Graph Spec” is the quickest and easiest way to build a graph. To use it, inside views
you should set specType
to simple
and define some graph specifications in spec
property. See example datapackage.json
:
{
"name": "simple-graph-spec",
"title": "Simple Graph Spec Tutorial",
"sources": [{
"name": "CBOE VIX Page",
"web": "http://www.cboe.com/micro/vix/historical.aspx"
}],
"resources": [
{
"name": "vix-daily",
"path": "data/vix-daily.csv",
"format": "csv",
"mediatype": "text/csv",
"schema": {
"fields": [
{
"name": "Date",
"type": "date",
"description": ""
},
{
"name": "VIXOpen",
"type": "number",
"description": ""
},
{
"name": "VIXHigh",
"type": "number",
"description": ""
},
{
"name": "VIXLow",
"type": "number",
"description": ""
},
{
"name": "VIXClose",
"type": "number",
"description": ""
}
],
"primaryKey": "Date"
}
}
],
"views": [
{
"name": "simple-view-line",
"title": "tutorial-on-simple-views-line",
"resources": ["vix-daily"],
"specType": "simple",
"spec": {
"type": "line",
"group": "Date",
"series": ["VIXHigh", "VIXLow"]
}
},
{
"name": "simple-view-bar",
"title": "tutorial-on-simple-views-bar",
"resources": ["vix-daily"],
"specType": "simple",
"spec": {
"type": "bar",
"group": "Date",
"series": ["VIXOpen", "VIXOpen", "VIXHigh", "VIXLow"]
}
}
]
}
Only 3 properties enough to define graph specifications inside spec
property:
Attribute | Type | Description |
---|---|---|
type | String | line, bar, pie (defaults to line) |
group | String | Field name, that will be used as abscissa (usually date field) |
series | Array | Field name(s) that will be used as ordinate |
You can define multiple views for your dataset. For example, to display line graph as presented above, we defined graph type
to be a line
:
...
"spec": {
"type": "line",
...
}
Similarly to display bar chart we’ve used bar
type:
...
"spec": {
"type": "bar",
...
}
We use Date
field to display data over time, by setting group
attribute to the field name:
...
"spec": {
...
"group": "Date",
...
}
You can set any number of fields to display in series
attribute as an array:
...
"spec": {
...
"series": [
"VIXHigh",
"VIXLow"
]
}
In our case we’ve displayed line graph for VIXHigh
and VIXLow
and similarly, in the bar chart, we use all four series and all of them are presented in chart.
Outside of spec
attribute there are some other important parameters to note:
Attribute | Type | Description |
---|---|---|
name | String | Unique identifier for view within list of views (lines 51 and 62) |
title | String | Title for the graph (lines 52 and 63) |
resources | Array | Data sources for this spec. It can be either resource name or index. By default it is the first resource (lines 53 and 64) |
specType | String | Available options: simple, vega, plotly (Required) |