Introduction To Danfo.js - Manipulating And Processing Structured Data

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Danfo.js

An open-source, JavaScript library providing high-performance, intuitive, and easy-to-use data structures for manipulating and processing structured data. It is heavily inspired by the Python's Pandas library and provides a similar interface and API. Moreover, Danfo.js is fast and It is built on Tensorflow.js and supports tensors out of the box.

Data science thrives in Python because of the ecosystem of open-source libraries - NumPy, Pandas, sklearn, and more. It's great to see similar tools being developed by the JavaScript community. This could be the start of something big. So let us see Danfo.js in action.

Installation

There are two ways to get danfo.js. To install it via npm, you can do the following:

npm install danfojs-node

We can also install and use it in the browsers by using the CDN below:

<script src="https://cdn.jsdelivr.net/npm/danfojs@0.1.1/dist/index.min.js"></script>

Creating a Series object by passing a list of values, letting danfo.js create a default integer index:

const dfd = require("danfojs-node")
s = new dfd.Series([1, 3, 5, undefined, 6, 8])
s.print()
0
0 1
1 3
2 5
3 NaN
4 6
5 8

Reading JSON data and vector operations

const json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
            { A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
            { A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
            { A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
df = new dfd.DataFrame(json_data) 
 // Adding to series object, can use sub, mul, div, and pow
df['A'].add(df['B']).print()
df['A'].pow(2).print()
// Maximum value of C
console.log(df['C'].max()) // 4.505899

Add A and B

A
0 4.744029998779297
1 0.2825700044631958
2 -0.13752996921539307
3 -0.8194299936294556

A Square

A
0 0.21270543336868286
1 0.2613254487514496
2 0.4776192009449005
3 0.22014862298965454

Reading CSV file from URL

dfd.read_csv("https://raw.githubusercontent.com/curran/data/gh-pages/jsLibraries/jsLibs.csv")
  .then(df => {

    //prints the first five columns
    df.head().print()

  }).catch(err => {
    console.log(err);
  })
LibraryMinified File Size (kb)Github Stars
0 Knockout.js175036
1 Angular.js10624580
2 Ember.js7110368
3 Can.js82928
4 React.js1237015

Calculate descriptive statistics for all numerical columns

df.describe().print()
Minified File Size (kb)Github Stars
count 77
mean 58.0714269464.286133
std 49.759789038.434833
min 1156
median 717015
max 12324580
variance 2476.03571481693304.23

The shape of the data, column names, and dtypes

console.log(df.shape);

console.log(df.column_names);

df.ctypes.print()
[ 7, 3 ]
[ 'Library', 'Minified File Size (kb)', 'Github Stars' ]
0
Library string
Minified File Size (kb) float32
Github Stars int32
dfd.read_csv("https://raw.githubusercontent.com/curran/data/gh-pages/jsLibraries/jsLibs.csv")
    .then(df => {
        df['Library'].print()
    }).catch(err => {
        console.log(err);
    })
Library
0 Knockout.js
1 Angular.js
2 Ember.js
3 Can.js
4 React.js
5 Backbone.js
6 Model.js

Selecting on a multi-axis by label, by slicing, and by query

dfd.read_csv("https://raw.githubusercontent.com/curran/data/gh-pages/jsLibraries/jsLibs.csv")
    .then(df => {
        // Selection by label
        const sub_df = df.loc({ rows: [0, 1], columns: ["Library", "Github Stars"] })
        sub_df.print()

       // Selection by slicing
        const slice_df = df.loc({ rows: ["0:4"], columns: ["Library", "Github Stars"] })
        slice_df.print()

       // Selection by query
        const query_df = df.query({ "column": "Github Stars", "is": ">", "to": 10000 })
        query_df.print()   
    }).catch(err => {
        console.log(err);
    })

Selection By Multi-Axis Label

LibraryGithub Stars
0 Knockout.js5036
1 Angular.js24580

Selection By Slicing

LibraryGithub Stars
0 Knockout.js5036
1 Angular.js24580
2 Ember.js10368
3 Can.js928

Selection By Query

LibraryMinified File Size (kb)Github Stars
1 Angular.js10624580
2 Ember.js7110368
5 Backbone.js6.518167

There are many mathematical operations we can perform over the dataframe object.


Danfo supports plotting

Danfo uses Plotly.js as backend for plotting. This gives us the ability to make interactive plots from DataFrame and Series. Plotting only works in the browser version of danfo.js, and requires an HTML div to show plots.

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
    <script src="https://cdn.jsdelivr.net/npm/danfojs@0.1.1/dist/index.min.js"></script>
    <title>Document</title>
</head>

<body>

    <div id="plot_div"></div>
    <script>

         dfd.read_csv("https://raw.githubusercontent.com/curran/data/gh-pages/jsLibraries/jsLibs.csv")
            .then(df => {

                var layout = {
                    title: 'JavaScript Libraries and Github Stars',
                    xaxis: {
                        title: 'Libraries',
                    },
                    yaxis: {
                        title: 'Stars',
                    }
                }

                new_df = df.set_index({key:"Library"})
                new_df.plot("plot_div").bar({columns:["Github Stars"],layout: layout })

            }).catch(err => {
                console.log(err);
            })

    </script>
</body>

</html>

newplot.png

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