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https://github.com/javascriptdata/danfojs

Danfo.js is an open source, JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.
https://github.com/javascriptdata/danfojs

danfojs data-analysis data-analytics data-manipulation data-science dataframe javascript pandas plotting-charts stream-data stream-processing table tensorflow tensors

Last synced: 27 days ago
JSON representation

Danfo.js is an open source, JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.

Host: GitHub
URL: https://github.com/javascriptdata/danfojs
Owner: javascriptdata
License: mit
Created: 2020-05-13T21:53:26.000Z (over 4 years ago)
Default Branch: dev
Last Pushed: 2024-07-08T17:05:40.000Z (4 months ago)
Last Synced: 2024-10-13T03:05:45.117Z (27 days ago)
Topics: danfojs, data-analysis, data-analytics, data-manipulation, data-science, dataframe, javascript, pandas, plotting-charts, stream-data, stream-processing, table, tensorflow, tensors
Language: TypeScript
Homepage: https://danfo.jsdata.org/
Size: 80.1 MB
Stars: 4,775
Watchers: 31
Forks: 209
Open Issues: 119
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

awesome-projects - Danfo

README

        


  




-----------------

## Danfojs: powerful javascript data analysis toolkit 

![Node.js CI](https://github.com/opensource9ja/danfojs/workflows/Node.js%20CI/badge.svg?branch=master)

[![](https://data.jsdelivr.com/v1/package/npm/danfojs/badge?style=rounded)](https://www.jsdelivr.com/package/npm/danfojs)

[![Coverage Status](https://coveralls.io/repos/github/opensource9ja/danfojs/badge.svg)](https://coveralls.io/github/opensource9ja/danfojs)

![Twitter](https://img.shields.io/twitter/url?style=social&url=https%3A%2F%2Ftwitter.com%2FDanfoJs) 



## What is it?

**Danfo.js** is a javascript package that provides fast, flexible, and expressive data

structures designed to make working with "relational" or "labeled" data both

easy and intuitive. It is heavily inspired by [Pandas](https://pandas.pydata.org/pandas-docs/stable/) library, and provides a similar API. This means that users familiar with [Pandas](https://pandas.pydata.org/pandas-docs/stable/), can easily pick up danfo.js. 

## Main Features

  - Danfo.js is fast and supports Tensorflow.js tensors out of the box. This means you can [convert Danfo data structure](https://danfo.jsdata.org/api-reference/dataframe/dataframe.tensor) to Tensors.

  - Easy handling of [missing-data](https://danfo.jsdata.org/getting-started#missing-data) (represented as

    `NaN`) in floating point as well as non-floating point data

  - Size mutability: columns can be [inserted/deleted](https://danfo.jsdata.org/api-reference/dataframe#combining-comparing-joining-merging) from DataFrame

  - Automatic and explicit [alignment](https://danfo.jsdata.org/api-reference/dataframe#reindexing-selection-label-manipulation): objects can

    be explicitly aligned to a set of labels, or the user can simply

    ignore the labels and let `Series`, `DataFrame`, etc. automatically

    align the data for you in computations

  - Powerful, flexible [groupby](https://danfo.jsdata.org/api-reference/groupby) functionality to perform

    split-apply-combine operations on data sets, for both aggregating

    and transforming data

  - Make it easy to convert Arrays, JSONs, List or Objects, Tensors and 

    differently-indexed data structures

    into DataFrame objects

  - Intelligent label-based [slicing](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.loc), [fancy indexing](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.iloc), and [querying](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.query) of

    large data sets

  - Intuitive [merging](https://danfo.jsdata.org/api-reference/general-functions/danfo.merge) and [joining](https://danfo.jsdata.org/api-reference/general-functions/danfo.concat) data

    sets

  - Robust IO tools for loading data from [flat-files](https://danfo.jsdata.org/api-reference/input-output)

    (CSV, Json, Excel).

  - Powerful, flexible and intutive API for [plotting](https://danfo.jsdata.org/api-reference/plotting) DataFrames and Series interactively.

  - [Timeseries](https://danfo.jsdata.org/api-reference/series#accessors)-specific functionality: date range

    generation and date and time properties. 

  - Robust data preprocessing functions like [OneHotEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.onehotencoder), [LabelEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.labelencoder), and scalers like [StandardScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.standardscaler) and [MinMaxScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.minmaxscaler) are supported on DataFrame and Series

## Installation

There are three ways to install and use Danfo.js in your application

* For Nodejs applications, you can install the [__danfojs-node__]() version via package managers like yarn and/or npm:

```bash

npm install danfojs-node

or

yarn add danfojs-node

```

For client-side applications built with frameworks like React, Vue, Next.js, etc, you can install the [__danfojs__]() version:

```bash

npm install danfojs

or

yarn add danfojs

```

For use directly in HTML files, you can add the latest script tag from [JsDelivr](https://www.jsdelivr.com/package/npm/danfojs) to your HTML file:

```html

    

```

See all available versions [here](https://www.jsdelivr.com/package/npm/danfojs)

### Quick Examples

* [Danfojs with HTML and vanilla JavaScript on CodePen](https://codepen.io/risingodegua/pen/bGpwyYW)

* [Danfojs with React on Code Sandbox](https://codesandbox.io/s/using-danfojs-in-react-dwpv54?file=/src/App.js)

* [Danfojs on ObservableHq](https://observablehq.com/@risingodegua/using-danfojs-on-observablehq)

* [Danfojs in Nodejs on Replit](https://replit.com/@RisingOdegua/Danfojs-in-Nodejs)

### Example Usage in the Browser

```html

  

    

    

    

    Document

  

  

    


    

    


    

      dfd.readCSV("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")

          .then(df => {

              df['AAPL.Open'].plot("div1").box() //makes a box plot

              df.plot("div2").table() //display csv as table

              new_df = df.setIndex({ column: "Date", drop: true }); //resets the index to Date column

              new_df.head().print() //

              new_df.plot("div3").line({

                  config: {

                      columns: ["AAPL.Open", "AAPL.High"]

                  }

              })  //makes a timeseries plot

          }).catch(err => {

              console.log(err);

          })

    

  

```

Output in Browser:

![](assets/browser-out.gif)

### Example usage in Nodejs

```javascript

const dfd = require("danfojs-node");

const file_url =

  "https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv";

dfd

  .readCSV(file_url)

  .then((df) => {

    //prints the first five columns

    df.head().print();

    // Calculate descriptive statistics for all numerical columns

    df.describe().print();

    //prints the shape of the data

    console.log(df.shape);

    //prints all column names

    console.log(df.columns);

    // //prints the inferred dtypes of each column

    df.ctypes.print();

    //selecting a column by subsetting

    df["Name"].print();

    //drop columns by names

    let cols_2_remove = ["Age", "Pclass"];

    let df_drop = df.drop({ columns: cols_2_remove, axis: 1 });

    df_drop.print();

    //select columns by dtypes

    let str_cols = df_drop.selectDtypes(["string"]);

    let num_cols = df_drop.selectDtypes(["int32", "float32"]);

    str_cols.print();

    num_cols.print();

    //add new column to Dataframe

    let new_vals = df["Fare"].round(1);

    df_drop.addColumn("fare_round", new_vals, { inplace: true });

    df_drop.print();

    df_drop["fare_round"].round(2).print(5);

    //prints the number of occurence each value in the column

    df_drop["Survived"].valueCounts().print();

    //print the last ten elementa of a DataFrame

    df_drop.tail(10).print();

    //prints the number of missing values in a DataFrame

    df_drop.isNa().sum().print();

  })

  .catch((err) => {

    console.log(err);

  });

```

Output in Node Console:

![](assets/node-rec.gif)

## Notebook support

* VsCode nodejs notebook extension now supports Danfo.js. See guide [here](https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook)

* ObservableHQ Notebooks. See example notebook [here](https://observablehq.com/@risingodegua/using-danfojs-on-observablehq)

#### [See the Official Getting Started Guide](https://danfo.jsdata.org/getting-started)

## Documentation

The official documentation can be found [here](https://danfo.jsdata.org)

## Danfo.js Official Book

We published a book titled "Building Data Driven Applications with Danfo.js". Read more about it [here](https://danfo.jsdata.org/building-data-driven-applications-with-danfo.js-book)

## Discussion and Development

Development discussions take place [here](https://github.com/opensource9ja/danfojs/discussions). 

## Contributing to Danfo

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. A detailed overview on how to contribute can be found in the [contributing guide](https://danfo.jsdata.org/contributing-guide).

#### Licence [MIT](https://github.com/opensource9ja/danfojs/blob/master/LICENCE)

#### Created by [Rising Odegua](https://github.com/risenW) and [Stephen Oni](https://github.com/steveoni)