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Try It Now!Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 200kB | csv zip | 5 years ago | OpenML - Primary tumor |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
primary-tumor | 32kB | csv (32kB) , json (104kB) | ||
primary-tumor_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 10kB | zip (10kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
age | 1 | string (default) | <30, 30-59, >=60 |
sex | 2 | string (default) | female, male |
histologic-type | 3 | string (default) | epidermoid, adeno, anaplastic |
degree-of-diffe | 4 | string (default) | well, fairly, poorly |
bone | 5 | boolean (default) | |
bone-marrow | 6 | boolean (default) | |
lung | 7 | boolean (default) | |
pleura | 8 | boolean (default) | |
peritoneum | 9 | boolean (default) | |
liver | 10 | boolean (default) | |
brain | 11 | boolean (default) | |
skin | 12 | boolean (default) | |
neck | 13 | boolean (default) | |
supraclavicular | 14 | boolean (default) | |
axillar | 15 | boolean (default) | |
mediastinum | 16 | boolean (default) | |
abdominal | 17 | boolean (default) | |
class | 18 | string (default) | lung, head & neck, esophasus, thyroid, stomach, duoden & sm.int, colon, rectum, anus, salivary glands, pancreas, gallblader, liver, kidney, bladder, testis, prostate, ovary, corpus uteri, cervix uteri, vagina, breast |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/machine-learning/primary-tumor
data info machine-learning/primary-tumor
tree machine-learning/primary-tumor
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/primary-tumor/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/machine-learning/primary-tumor/r/0.csv
curl -L https://datahub.io/machine-learning/primary-tumor/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/machine-learning/primary-tumor/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/machine-learning/primary-tumor/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/machine-learning/primary-tumor/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/machine-learning/primary-tumor/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 a dataset about primary tumors in people. Locations of primary tumors are locations in body where the tumor first appeared and from there started to metastasize to other parts of the body.
This dataset was found on OpenML - primary-tumor
This primary tumor domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. _Please include this citation if you plan to use this database.
Data is located in directory data
data/primary-tumor.csv
To get our output data several things are done to input data:
Scripts are in directory scripts
scripts/main.py
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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