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danfo.OneHotEncoder
Encode categorical features as a one-hot numeric array.
class danfo.OneHotEncoder
danfo.js provides the OneHotEncoder class for encoding values in Series and Arrays to one-hot numeric arrays. This is mostly used as a preprocessing step before most machine learning tasks.
Node
const dfd = require("danfojs-node")
let data = {
fruits: ['pear', 'mango', "pawpaw", "mango", "bean"],
Count: [20, 30, 89, 12, 30],
Country: ["NG", "NG", "GH", "RU", "RU"]
}
let df = new dfd.DataFrame(data)
let encode = new dfd.OneHotEncoder()
encode.fit(df['fruits'])
console.log(encode);
let sf_enc = encode.transform(df['fruits'].values)
console.log(sf_enc)
let new_sf = encode.transform(["mango", "bean"])
console.log(new_sf)
Output
OneHotEncoder { '$labels': [ 'pear', 'mango', 'pawpaw', 'bean' ] }
[
[ 1, 0, 0, 0 ],
[ 0, 1, 0, 0 ],
[ 0, 0, 1, 0 ],
[ 0, 1, 0, 0 ],
[ 0, 0, 0, 1 ]
]
[ [ 0, 1, 0, 0 ], [ 0, 0, 0, 1 ] ]
Labels not found in the original data used for fitting are represented with 0s all through
Last modified 1yr ago