Now you can request additional data and/or customized columns!
Try It Now!Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
3 | 117kB | arff csv zip | 4 years ago | 4 years ago | Open Data Commons Public Domain Dedication and License |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
lymph_arff | 22kB | arff (22kB) | ||
lymph | 15kB | csv (15kB) , json (69kB) | ||
lymph_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 13kB | zip (13kB) |
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This is a preview version. There might be more data in the original version.
Signup to Premium Service for additional or customised data - Get Started
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
lymphatics | 1 | string (default) | |
block_of_affere | 2 | string (default) | |
bl_of_lymph_c | 3 | string (default) | |
bl_of_lymph_s | 4 | string (default) | |
by_pass | 5 | string (default) | |
extravasates | 6 | string (default) | |
regeneration_of | 7 | string (default) | |
early_uptake_in | 8 | string (default) | |
lym_nodes_dimin | 9 | number (default) | |
lym_nodes_enlar | 10 | number (default) | |
changes_in_lym | 11 | string (default) | |
defect_in_node | 12 | string (default) | |
changes_in_node | 13 | string (default) | |
changes_in_stru | 14 | string (default) | |
special_forms | 15 | string (default) | |
dislocation_of | 16 | string (default) | |
exclusion_of_no | 17 | string (default) | |
no_of_nodes_in | 18 | number (default) | |
class | 19 | string (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/machine-learning/lymph
data info machine-learning/lymph
tree machine-learning/lymph
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/lymph/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/machine-learning/lymph/r/0.arff
curl -L https://datahub.io/machine-learning/lymph/r/1.csv
curl -L https://datahub.io/machine-learning/lymph/r/2.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/lymph/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/lymph/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/lymph/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/lymph/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)
}
}
})()
The resources for this dataset can be found at https://www.openml.org/d/10
Author:
Source: Unknown -
Please cite:
Citation Request: This lymphography 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.
Title: Lymphography Domain
Sources: (a) See Above. (b) Donors: Igor Kononenko, University E.Kardelj Faculty for electrical engineering Trzaska 25 61000 Ljubljana (tel.: (38)(+61) 265-161
Bojan Cestnik
Jozef Stefan Institute
Jamova 39
61000 Ljubljana
Yugoslavia (tel.: (38)(+61) 214-399 ext.287)
© Date: November 1988
Past Usage: (sveral)
Relevant Information: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also breast-cancer and primary-tumor.)
Number of Instances: 148
Number of Attributes: 19 including the class attribute
Attribute information: — NOTE: All attribute values in the database have been entered as numeric values corresponding to their index in the list of attribute values for that attribute domain as given below.
Missing Attribute Values: None
Class Distribution: Class: Number of Instances: normal find: 2 metastases: 81 malign lymph: 61 fibrosis: 4
Relabeled values in attribute ‘lymphatics’
From: ‘1’ To: normal
From: ‘2’ To: arched
From: ‘3’ To: deformed
From: ‘4’ To: displaced
Relabeled values in attribute ‘block_of_affere’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘bl_of_lymph_c’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘bl_of_lymph_s’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘by_pass’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘extravasates’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘regeneration_of’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘early_uptake_in’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘changes_in_lym’
From: ‘1’ To: bean
From: ‘2’ To: oval
From: ‘3’ To: round
Relabeled values in attribute ‘defect_in_node’
From: ‘1’ To: no
From: ‘2’ To: lacunar
From: ‘3’ To: lac_margin
From: ‘4’ To: lac_central
Relabeled values in attribute ‘changes_in_node’
From: ‘1’ To: no
From: ‘2’ To: lacunar
From: ‘3’ To: lac_margin
From: ‘4’ To: lac_central
Relabeled values in attribute ‘changes_in_stru’
From: ‘1’ To: no
From: ‘2’ To: grainy
From: ‘3’ To: drop_like
From: ‘4’ To: coarse
From: ‘5’ To: diluted
From: ‘6’ To: reticular
From: ‘7’ To: stripped
From: ‘8’ To: faint
Relabeled values in attribute ‘special_forms’
From: ‘1’ To: no
From: ‘2’ To: chalices
From: ‘3’ To: vesicles
Relabeled values in attribute ‘dislocation_of’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘exclusion_of_no’
From: ‘1’ To: no
From: ‘2’ To: yes
Relabeled values in attribute ‘class’
From: ‘1’ To: normal
From: ‘2’ To: metastases
From: ‘3’ To: malign_lymph
From: ‘4’ To: fibrosis
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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