ml_dataframe

A way to store and manipulate data

The library exposes in-memory storage for dynamically typed data. The storage is represented by DataFrame class.

Usage example:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final data = [ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]; final dataframe = DataFrame(data); print(dataframe); // DataFrame (5 x 6) // Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species // 1 5.1 3.5 1.4 0.2 Iris-setosa // 2 4.9 3.0 1.4 0.2 Iris-setosa // 89 5.6 3.0 4.1 1.3 Iris-versicolor // 90 5.5 2.5 4.0 1.3 Iris-versicolor // 91 5.5 2.6 4.4 1.2 Iris-versicolor }

`DataFrame` API with examples:

Get the header of the data

By default, the very first row is considered a header, unless one specify their own header or autogenerated one. More on this is here

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final header = dataframe.header; print(header); // ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'] }

Get the rows of the data

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final rows = dataframe.rows; print(rows); // [ // [1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], // [2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], // [89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], // [90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], // [91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], // ], }

Get the series collection (columns) of the data

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final series = dataframe.series; print(series); // [ // 'Id': [1, 2, 89, 90, 91], // 'SepalLengthCm': [5.1, 4.9, 5.6, 5.5, 5.5], // 'SepalWidthCm': [3.5, 3.0, 3.0, 2.5, 2.6], // 'PetalLengthCm': [1.4, 1.4, 4.1, 4.0, 4.4], // 'PetalWidthCm': [0.2, 0.2, 1.3, 1.3, 1.2], // 'Species': ['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor'], // ], }

Get the shape of the data

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final shape = dataframe.shape; print(shape); // [5, 6] - 5 rows, 6 columns }

Add a series

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final firstSeries = Series('super_series', [1, 2, 3, 4, 5, 6]); final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final modifiedDataframe = dataframe.addSeries([firstSeries]); // The method doesn't mutate the original dataframe print(modifiedDataframe.series.first); // 'super_series': [1, 2, 3, 4, 5, 6] }

Drop a series by a series name

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); print(dataframe.shape); // [5, 6] - 6 rows, 6 columns  final modifiedDataframe = dataframe.dropSeries(names: ['Id']); // The method doesn't mutate the original dataframe print(modifiedDataframe.shape); // [5, 5] - after a series had been dropped, the number of columns became one lesser }

Drop a series by a series index

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); print(dataframe.shape); // [5, 6] - 5 rows, 6 columns  final modifiedDataframe = dataframe.dropSeries(indices: [0]); // The method doesn't mutate the original dataframe print(modifiedDataframe.shape); // [5, 5] - after a series had been dropped, the number of columns became one lesser }

Sample a new dataframe from rows of an existing dataframe

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final sampled = dataframe.sampleFromRows([0, 5]); print(sampled); // DataFrame (2 x 6) // Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species // 1 5.1 3.5 1.4 0.2 Iris-setosa // 91 5.5 2.6 4.4 1.2 Iris-versicolor }

Sample a new dataframe from series indices of an existing dataframe

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final sampled = dataframe.sampleFromSeries(indices: [0, 1]); print(sampled); // DataFrame (5 x 2) // Id SepalLengthCm // 1 5.1 // 2 4.9 // 89 5.6 // 90 5.5 // 91 5.5 }

Sample a new dataframe from series names of an existing dataframe

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final sampled = dataframe.sampleFromSeries(names: ['Id', 'SepalLengthCm']); print(sampled); // DataFrame (5 x 2) // Id SepalLengthCm // 1 5.1 // 2 4.9 // 89 5.6 // 90 5.5 // 91 5.5 }

Save a dataframe to a JSON file

import 'package:ml_dataframe/ml_dataframe.dart'; void main() async { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); await dataframe.saveAsJson('path/to/json/file.json'); }

Shuffle rows in a dataframe

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); print(dataframe); // DataFrame (5 x 6) // Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species // 1 5.1 3.5 1.4 0.2 Iris-setosa // 2 4.9 3.0 1.4 0.2 Iris-setosa // 89 5.6 3.0 4.1 1.3 Iris-versicolor // 90 5.5 2.5 4.0 1.3 Iris-versicolor // 91 5.5 2.6 4.4 1.2 Iris-versicolor final shuffled = dataframe.shuffle(); // keep in mind that `shuffle` like other methods returns a new dataframe, the method doesn't mutate the source dataframe  print(shuffled); // DataFrame (5 x 6) // Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species // 89 5.6 3.0 4.1 1.3 Iris-versicolor // 1 5.1 3.5 1.4 0.2 Iris-setosa // 91 5.5 2.6 4.4 1.2 Iris-versicolor // 2 4.9 3.0 1.4 0.2 Iris-setosa // 90 5.5 2.5 4.0 1.3 Iris-versicolor }

One can use seed parameter to keep the order of rows disregard the number of shuffle calls:

dataframe.shuffle(seed: 10);

Get a json-serializable representation

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final json = dataframe.toJson(); // json contains a serializable map }

Convert a dataframe to a matrix:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], [ 1, 5.1, 3.5, 1.4, 0.2], [ 2, 4.9, 3.0, 1.4, 0.2], [ 89, 5.6, 3.0, 4.1, 1.3], [ 90, 5.5, 2.5, 4.0, 1.3], [ 91, 5.5, 2.6, 4.4, 1.2], ]); final matrix = dataframe.toMatrix(); print(matrix); // because of internal representation of Float32 numbers there are some round-off errors in the output // Matrix 5 x 5: // (1.0, 5.099999904632568, 3.5, 1.399999976158142, 0.20000000298023224) // (2.0, 4.900000095367432, 3.0, 1.399999976158142, 0.20000000298023224) // (89.0, 5.599999904632568, 3.0, 4.099999904632568, 1.2999999523162842) // (90.0, 5.5, 2.5, 4.0, 1.2999999523162842) // (91.0, 5.5, 2.5999999046325684, 4.400000095367432, 1.2000000476837158) }

the method throws an error if there are inconvertible to a number values in the dataframe.

Get a series by its index

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final series = dataframe[0]; print(series); // Id: [1, 2, 89, 90, 91] }

Get a series by its name

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final dataframe = DataFrame([ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]); final series = dataframe['Id']; print(series); // Id: [1, 2, 89, 90, 91] }

Map values of a dataframe

import 'package:ml_dataframe/ml_dataframe'; void main() { final data = DataFrame([ ['col_1', 'col_2', 'col_3'], [ 2, 20, 200], [ 3, 30, 300], [ 4, 40, 400], ]); // the first generic type ia a type of the source value, the second generic type is a type of the mapped value final modifiedData = data.map<num, num>((value) => value * 2); print(modifiedData); // DataFrame (3 x 3) // col_1 col_2 col_3 // 4 40 400 // 6 60 600 // 8 80 800 }

Map values of a specific dataframe series

import 'package:ml_dataframe/ml_dataframe'; void main() { final data = DataFrame([ ['col_1', 'col_2', 'col_3'], [ 2, 20, 200], [ 3, 30, 300], [ 4, 40, 400], ]); // the first generic type ia a type of the source value, the second generic type is a type of the mapped value final modifiedData = data.mapSeries<num, num>((value) => value * 2, name: 'col_2'); print(modifiedData); // DataFrame (3 x 3) // col_1 col_2 col_3 // 2 40 200 // 3 60 300 // 4 80 400 }

Ways to create a dataframe

`DataFrame` constructor

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final data = [ ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'], [ 1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [ 2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [ 89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [ 90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [ 91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]; final dataframe = DataFrame(data); }

By default, the very first row is considered a header. If the data does not have a header, one can use autogenerated header by providing headerExists: false config to the constructor:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final data = [ [1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]; final dataframe = DataFrame(data, headerExists: false); print(dataframe.header); }

It outputs ['col_1', 'col_2', 'col_3', 'col_4', 'col_5', 'col_6']. col_ is a default prefix for the autogenerated columns.

Also, if there are no header row in the data, one can use a predefined header:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final data = [ [1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [2, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [89, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'], [90, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'], ]; final dataframe = DataFrame(data, header: ['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'feature_6']); }

`fromCsv` function

import 'package:ml_dataframe/ml_dataframe.dart'; void main() async { final data = await fromCsv('path/to/csv/file.csv'); }

If the csv file does not have a header row, it's needed to provide the corresponding flag:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() async { final data = await fromCsv('path/to/csv/file.csv', headerExists: false); }

Restore a dataframe previously persisted as a json file - `fromJson` function

import 'package:ml_dataframe/ml_dataframe.dart'; void main() async { final data = await fromJson('path/to/json/file.json'); }

This function works in conjunction with DataFrame saveAsJson method.

Dataframes with prefilled data

In order to test data processing algorithms, one can use "toy" datasets. The library exposes several of them:

Iris dataset - function `getIrisDataFrame`

One can create a dataframe filled with Iris data:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final data = getIrisDataFrame(); print(data); // DataFrame (150 x 6) // Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species // ... }

Pima Indians diabetes dataset - function `getPimaIndiansDiabetesDataFrame`

One can create a dataframe filled with Pima Indians diabetes data:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final data = getPimaIndiansDiabetesDataFrame(); print(data); // DataFrame (768 x 9) // Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome // ... }

Red wine quality dataset - function `getWineQualityDataframe`

One can create a dataframe filled with Red wine quality data:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final data = getWineQualityDataframe(); print(data); // DataFrame (1599 x 12) // fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality // ... }

Boston housing dataset - function `getHousingDataframe`

One can create a dataframe filled with Boston housing data:

import 'package:ml_dataframe/ml_dataframe.dart'; void main() { final data = getHousingDataframe(); print(data); // DataFrame (506 x 14) // CRIM ZN INDUS CHAS NOX RM ... MEDV // 0.00632 18.0 2.31 0 0.538 6.575 ... 24.0 // 0.02731 0.0 7.07 0 0.469 6.421 ... 21.6 // 0.02729 0.0 7.07 0 0.469 7.185 ... 34.7 // 0.03237 0.0 2.18 0 0.458 6.998 ... 33.4 // 0.06905 0.0 2.18 0 0.458 7.147 ... 36.2 // ... ... ... ... ... ... ... ... // 0.06263 0.0 11.93 0 0.573 6.593 ... 22.4 // 0.04527 0.0 11.93 0 0.573 6.12 ... 20.6 // 0.06076 0.0 11.93 0 0.573 6.976 ... 23.9 // 0.10959 0.0 11.93 0 0.573 6.794 ... 22.0 // 0.04741 0.0 11.93 0 0.573 6.03 ... 11.9 }

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LICENSE		LICENSE
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ml_dataframe

Table of contents

Usage example:

DataFrame API with examples:

Get the header of the data

Get the rows of the data

Get the series collection (columns) of the data

Get the shape of the data

Add a series

Drop a series by a series name

Drop a series by a series index

Sample a new dataframe from rows of an existing dataframe

Sample a new dataframe from series indices of an existing dataframe

Sample a new dataframe from series names of an existing dataframe

Save a dataframe to a JSON file

Shuffle rows in a dataframe

Get a json-serializable representation

Convert a dataframe to a matrix:

Get a series by its index

Get a series by its name

Map values of a dataframe

Map values of a specific dataframe series

Ways to create a dataframe

DataFrame constructor

fromCsv function

Restore a dataframe previously persisted as a json file - fromJson function

Dataframes with prefilled data

Iris dataset - function getIrisDataFrame

Pima Indians diabetes dataset - function getPimaIndiansDiabetesDataFrame

Red wine quality dataset - function getWineQualityDataframe

Boston housing dataset - function getHousingDataframe

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`DataFrame` API with examples:

`DataFrame` constructor

`fromCsv` function

Restore a dataframe previously persisted as a json file - `fromJson` function

Iris dataset - function `getIrisDataFrame`

Pima Indians diabetes dataset - function `getPimaIndiansDiabetesDataFrame`

Red wine quality dataset - function `getWineQualityDataframe`

Boston housing dataset - function `getHousingDataframe`

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