Danfo.js
  • Danfo.js Documentation
  • Getting Started
  • API reference
    • General Functions
      • danfo.tensorflow
      • danfo. convertFunctionTotransformer
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      • danfo.Utils
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      • danfo.Dt
      • danfo.dateRange
      • danfo.OneHotEncoder
      • danfo.StandardScaler
      • danfo.MinMaxScaler
      • danfo.LabelEncoder
      • danfo.toDateTime
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    • Input/Output
      • danfo.readExcel
      • danfo.toExcel
      • danfo.readJSON
      • danfo.toJSON
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      • danfo.toCSV
    • Series
      • Creating a Series
      • Series.append
      • Series.cumSum
      • Series.cumMax
      • Series.cumProd
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      • Series.str.split
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      • Series.str.indexOf
      • Series.str.includes
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      • Series.str.concat
      • Series.str.charAt
      • Series.str.toUpperCase
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      • Series.str.capitalize
      • Series.dt.seconds
      • Series.dt.minutes
      • Series.dt.dayOfMonth
      • Series.dt.monthName
      • Series.dt.hours
      • Series.dt.dayOfWeek
      • Series.dt.dayOfWeek
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      • Series.dt.year
      • Series.argMax
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      • Series.argSort
      • Series.replace
      • Series.isNa
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      • Series.dropDuplicates
      • Series.valueCounts
      • Series.nUnique
      • Series.unique
      • Series.abs
      • Series.ne
      • Series.eq
      • Series.ge
      • Series.le
      • Series.gt
      • Series.lt
      • Series.iloc
      • Series.loc
      • Series.at
      • Series.iat
      • Series.ndim
      • Series.shape
      • Series.dtype
      • Series.values
      • Series.tensor
      • Series.index
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      • Series.describe
      • Series.copy
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      • Series.var
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      • Series.minimum
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      • Series.count
      • Series.sum
      • Series.max
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      • Series.mode
      • Series.median
      • Series.mean
      • Series.mod
      • Series.pow
      • Series.div
      • Series.mul
      • Series.sub
      • Series.add
      • Series.sample
      • Series.tail
      • Series.head
      • Series.and
      • Series.or
    • Dataframe
      • Creating a DataFrame
      • DataFrame.sortIndex
      • DataFrame.append
      • DataFrame.nUnique
      • DataFrame.tensor
      • DataFrame.print
      • DataFrame.toCSV
      • DataFrame.toJSON
      • DataFrame.toExcel
      • DataFrame.sortValues
      • DataFrame.setIndex
      • DataFrame.resetIndex
      • DataFrame.rename
      • DataFrame.drop
      • DataFrame.asType
      • DataFrame.shape
      • DataFrame.axis
      • DataFrame.ndim
      • DataFrame.values
      • DataFrame.selectDtypes
      • DataFrame.ctypes
      • DataFrame.index
      • DataFrame.loc
      • DataFrame.iloc
      • DataFrame.at
      • DataFrame.iat
      • DataFrame.head
      • DataFrame.tail
      • DataFrame.sample
      • DataFrame.add
      • DataFrame.sub
      • DataFrame.mul
      • DataFrame.div
      • DataFrame.pow
      • DataFrame.mod
      • DataFrame.mean
      • DataFrame.median
      • DataFrame.min
      • DataFrame.max
      • DataFrame.std
      • DataFrame.var
      • DataFrame.count
      • DataFrame.round
      • DataFrame.cumSum
      • DataFrame.cumMin
      • DataFrame.cumMax
      • DataFrame.cumProd
      • DataFrame.copy
      • DataFrame.describe
      • DataFrame.sum
      • DataFrame.abs
      • DataFrame.query
      • DataFrame.addColumn
      • DataFrame.groupby
      • DataFrame.column
      • DataFrame.fillNa
      • DataFrame.isNa
      • DataFrame.dropNa
      • DataFrame.apply
      • DataFrame.applyMap
      • DataFrame.It
      • DataFrame.gt
      • DataFrame.le
      • DataFrame.ge
      • DataFrame.ne
      • DataFrame.eq
      • DataFrame.replace
    • Configuration Options
    • Plotting
      • Timeseries Plots
      • Violin Plots
      • Box Plots
      • Tables
      • Pie Charts
      • Histograms
      • Scatter Plots
      • Bar Charts
      • Line Charts
      • Customizing your plots
    • Groupby
      • Groupby.getGroups
      • Groupby.col
      • Groupby.max
      • Groupby.min
      • Groupby.sum
      • Groupby.mean
      • Groupby.std
      • Groupby.var
      • Groupby.count
      • Groupby.cumSum
      • Groupby.cumMax
      • Groupby.cumMin
      • Groupby.cumProd
      • Groupby.agg
  • User Guides
    • Migrating to the stable version of Danfo.js
    • Using Danfojs in React
    • Titanic Survival Prediction using Danfo.js and Tensorflow.js
  • Building Data Driven Applications with Danfo.js - Book
  • Contributing Guide
  • Release Notes
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On this page
  • Examples
  • Standardize DataFrame Object using MinMaxScaler
  • Standardize Series Object Using MinMaxScaler

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  1. API reference
  2. General Functions

danfo.MinMaxScaler

Transform features by scaling each feature to a range of max and min values.

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Last updated 3 years ago

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class danfo.MinMaxScaler

danfo.js provides the MinMaxScaler class for standardization of DataFrame and Series. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

This transformation is often used as an alternative to zero mean, unit variance scaling like .

The API is similar to sklearn's , and provides a fit and transform method.

Examples

Standardize DataFrame Object using MinMaxScaler

const dfd = require("danfojs-node")

let scaler = new dfd.MinMaxScaler()

let data = [[100,1000,2000, 3000] ,
            [20, 30, 20, 10],
            [1, 1, 1, 0]]

let df = new dfd.DataFrame(data)
df.print()

scaler.fit(df)

let df_enc = scaler.transform(df)
df_enc.print()
╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ 0                 │ 1                 │ 2                 │ 3                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ 100               │ 1000              │ 2000              │ 3000              ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ 20                │ 30                │ 20                │ 10                ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2 │ 1                 │ 1                 │ 1                 │ 0                 ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝


 Shape: (3,4) 

╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ 0                 │ 1                 │ 2                 │ 3                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ 1                 │ 1                 │ 1                 │ 1                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ 0.19191919267...  │ 0.02902902849...  │ 0.00950475223...  │ 0.00333333341...  ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2 │ 0                 │ 0                 │ 0                 │ 0                 ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

Standardize Series Object Using MinMaxScaler

const dfd = require("danfojs-node")
let scaler = new dfd.MinMaxScaler()

let data = [[100,1000,2000, 3000] ,
            [20, 30, 20, 10],
            [1, 1, 1, 0]]

let df = new dfd.DataFrame(data)
let sf = df.iloc({columns: [0]})

scaler.fit(sf)

let df_enc = scaler.transform(sf)
df_enc.print()
 Shape: (3,1) 

╔═══╤═══════════════════╗
║   │ 0                 ║
╟───┼───────────────────╢
║ 0 │ 1                 ║
╟───┼───────────────────╢
║ 1 │ 0.19191919267...  ║
╟───┼───────────────────╢
║ 2 │ 0                 ║
╚═══╧═══════════════════╝

See also

Standardscaler
MinMaxScaler
MinMaxScaler