Now you can request additional data and/or customized columns!
Try It Now!Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 976kB | csv zip | 5 years ago | UCI - Cervical cancer (Risk Factors) Data Set |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
cervical-cancer | 95kB | csv (95kB) , json (724kB) | ||
cervical-cancer_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 33kB | zip (33kB) |
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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 | integer (default) | |
Number of sexual partners | 2 | number (default) | |
First sexual intercourse | 3 | number (default) | |
Num of pregnancies | 4 | number (default) | |
Smokes | 5 | number (default) | |
Smokes (years) | 6 | number (default) | |
Smokes (packs/year) | 7 | number (default) | |
Hormonal Contraceptives | 8 | number (default) | |
Hormonal Contraceptives (years) | 9 | number (default) | |
IUD | 10 | number (default) | |
IUD (years) | 11 | number (default) | |
STDs | 12 | number (default) | |
STDs (number) | 13 | number (default) | |
STDs:condylomatosis | 14 | number (default) | |
STDs:cervical condylomatosis | 15 | number (default) | |
STDs:vaginal condylomatosis | 16 | number (default) | |
STDs:vulvo-perineal condylomatosis | 17 | number (default) | |
STDs:syphilis | 18 | number (default) | |
STDs:pelvic inflammatory disease | 19 | number (default) | |
STDs:genital herpes | 20 | number (default) | |
STDs:molluscum contagiosum | 21 | number (default) | |
STDs:AIDS | 22 | number (default) | |
STDs:HIV | 23 | number (default) | |
STDs:Hepatitis B | 24 | number (default) | |
STDs:HPV | 25 | number (default) | |
STDs: Number of diagnosis | 26 | integer (default) | |
STDs: Time since first diagnosis | 27 | string (default) | |
STDs: Time since last diagnosis | 28 | string (default) | |
Dx:Cancer | 29 | integer (default) | |
Dx:CIN | 30 | integer (default) | |
Dx:HPV | 31 | integer (default) | |
Dx | 32 | integer (default) | |
Hinselmann | 33 | integer (default) | |
Schiller | 34 | integer (default) | |
Citology | 35 | integer (default) | |
Biopsy | 36 | integer (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/machine-learning/cervical-cancer
data info machine-learning/cervical-cancer
tree machine-learning/cervical-cancer
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/cervical-cancer/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/machine-learning/cervical-cancer/r/0.csv
curl -L https://datahub.io/machine-learning/cervical-cancer/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/cervical-cancer/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/cervical-cancer/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/cervical-cancer/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/cervical-cancer/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 dataset about cervical cancer occurrences. Cervical cancer is one the most frequent cancer diseases that occur to women. This dataset is showing some factors that might influence cervical cancer.
This dataset was found on UCI under the name Cervical cancer (Risk Factors) Data Set
The dataset was collected at ‘Hospital Universitario de Caracas’ in Caracas, Venezuela. The dataset comprises demographic information, habits, and historic medical records of 858 patients. Several patients decided not to answer some of the questions because of privacy concerns (missing values).
Output data is located in directory called data
data/cervical-cancer.csv
Attributes are the same as they were in input data.
To get our output data several things are done to input data:
Python scripts are located 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)
Or suggest your own feature from the link below