Danfo.js
  • Danfo.js Documentation
  • Getting Started
  • API reference
    • General Functions
      • danfo.tensorflow
      • danfo. convertFunctionTotransformer
      • danfo.streamCsvTransformer
      • danfo.streamJSON
      • danfo.streamCSV
      • danfo.Utils
      • danfo.Str
      • danfo.Dt
      • danfo.dateRange
      • danfo.OneHotEncoder
      • danfo.StandardScaler
      • danfo.MinMaxScaler
      • danfo.LabelEncoder
      • danfo.toDateTime
      • danfo.getDummies
      • danfo.concat
      • danfo.merge
    • Input/Output
      • danfo.readExcel
      • danfo.toExcel
      • danfo.readJSON
      • danfo.toJSON
      • danfo.readCSV
      • danfo.toCSV
    • Series
      • Creating a Series
      • Series.append
      • Series.cumSum
      • Series.cumMax
      • Series.cumProd
      • Series.cumMin
      • Series.str.split
      • Series.str.len
      • Series.str.join
      • Series.str.trim
      • Series.str.substring
      • Series.str.substr
      • Series.str.slice
      • Series.str.search
      • Series.str.repeat
      • Series.str.replace
      • Series.str.lastIndexOf
      • Series.str.indexOf
      • Series.str.includes
      • Series.str.endsWith
      • Series.str.startsWith
      • Series.str.concat
      • Series.str.charAt
      • Series.str.toUpperCase
      • Series.str.toLowerCase
      • Series.str.capitalize
      • Series.dt.seconds
      • Series.dt.minutes
      • Series.dt.dayOfMonth
      • Series.dt.monthName
      • Series.dt.hours
      • Series.dt.dayOfWeek
      • Series.dt.dayOfWeek
      • Series.dt.month
      • Series.dt.year
      • Series.argMax
      • Series.argMin
      • Series.argSort
      • Series.replace
      • Series.isNa
      • Series.fillNa
      • Series.dropNa
      • 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
      • Series.apply
      • Series.map
      • Series.setIndex
      • Series.resetIndex
      • Series.describe
      • Series.copy
      • Series.sortValues
      • Series.var
      • Series.std
      • Series.round
      • Series.minimum
      • Series.maximum
      • Series.count
      • Series.sum
      • Series.max
      • Series.min
      • 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
  • Drop columns by specifying the names
  • Drop rows by specifying int labels/index
  • Drop rows by specifying string labels/index

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  1. API reference
  2. Dataframe

DataFrame.drop

Drop specified labels from rows or columns.Remove rows or columns by specifying label names and corresponding axis.

danfo.DataFrame.drop(options)

Parameters
Type
Description
Default

options

Object

{

columns: Array of column names to drop.

index: Array of index labels to drop.

inplace: Boolean indicating whether to perform the operation inplace or not. Defaults to false

}

{**inplace:**false}

Returns:

**** return DataFrame

Examples

Drop columns by specifying the names

By setting inplace to true, the original DataFrame is modified and nothing is returned. To not modify the original DataFrame and return a new one, set inplace to false or leave it as default.

const dfd = require("danfojs-node")

let data = { "A": [-20, 30, 47.3, -20],
             "B": [34, -4, 5, 6] ,
             "C": [20, 20, 30, 30],
             "D": ["a", "b", "c", "c"] }

let df = new dfd.DataFrame(data)
df.drop({ columns: ["C", "B"], inplace: true });
df.print()
╔═══╤═══════════════════╤═══════════════════╗
║   │ A                 │ D                 ║
╟───┼───────────────────┼───────────────────╢
║ 0 │ -20               │ a                 ║
╟───┼───────────────────┼───────────────────╢
║ 1 │ 30                │ b                 ║
╟───┼───────────────────┼───────────────────╢
║ 2 │ 47.3              │ c                 ║
╟───┼───────────────────┼───────────────────╢
║ 3 │ -20               │ c                 ║
╚═══╧═══════════════════╧═══════════════════╝

Drop rows by specifying int labels/index

const dfd = require("danfojs-node")

let data = {
    "A": [-20, 30, 47.3, -20],
    "B": [34, -4, 5, 6],
    "C": [20, 20, 30, 30],
    "D": ["a", "b", "c", "c"]
}

let df = new dfd.DataFrame(data)
df.drop({ index: [0, 2], inplace: true });
df.print()
╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ A                 │ B                 │ C                 │ D                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ 30                │ -4                │ 20                │ b                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3 │ -20               │ 6                 │ 30                │ c                 ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

Drop rows by specifying string labels/index

const dfd = require("danfojs-node")

let data = { "A": [-20, 30, 47.3, -20],
             "B": [34, -4, 5, 6] ,
             "C": [20, 20, 30, 30],
             "D": ["a", "b", "c", "c"] }

let df = new dfd.DataFrame(data, {index: ["a", "b", "c", "d"]})
df.drop({ index: ["a", "c"], inplace: true });
df.print()
╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ A                 │ B                 │ C                 │ D                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ b │ 30                │ -4                │ 20                │ b                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ d │ -20               │ 6                 │ 30                │ c                 ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
PreviousDataFrame.renameNextDataFrame.asType

Last updated 3 years ago

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