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.

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Quick Overview

Danfo.js is a powerful open-source JavaScript library for data manipulation and analysis. It provides a pandas-like API for working with structured data in JavaScript and TypeScript, making it easier for developers to perform complex data operations in both browser and Node.js environments.

Pros

Familiar pandas-like API for JavaScript developers
Supports both browser and Node.js environments
Offers a wide range of data manipulation and analysis functions
Integrates well with visualization libraries like Plotly.js

Cons

Performance may be slower compared to native JavaScript operations for large datasets
Limited ecosystem compared to more established data analysis libraries
Documentation could be more comprehensive for advanced use cases
Steeper learning curve for developers not familiar with pandas

Code Examples

Creating a DataFrame:

import { DataFrame } from "danfojs-node"

const data = { 
  "Name": ["John", "Jane", "Mike"],
  "Age": [28, 34, 22],
  "City": ["New York", "London", "Paris"]
}

const df = new DataFrame(data)
console.log(df.head())

Filtering and sorting data:

// Filter rows where Age > 25 and sort by Age in descending order
const filtered = df.query("Age > 25").sortValues("Age", { ascending: false })
console.log(filtered)

Performing group operations:

// Group by City and calculate mean Age
const grouped = df.groupby(["City"]).mean()
console.log(grouped)

Getting Started

To get started with Danfo.js, follow these steps:

Install the library using npm:
```
npm install danfojs-node
```

Import the necessary modules in your JavaScript file:

import { DataFrame, Series } from "danfojs-node"

Create a DataFrame and start manipulating your data:

const df = new DataFrame({ 
  "A": [1, 2, 3],
  "B": [4, 5, 6]
})
console.log(df.describe().print())

For more detailed information and advanced usage, refer to the official Danfo.js documentation.

Competitor Comparisons

stdlib

4,700

✨ Standard library for JavaScript and Node.js. ✨

Pros of stdlib

Comprehensive library with a wide range of mathematical and statistical functions
Well-documented and actively maintained
Supports both Node.js and browser environments

Cons of stdlib

Larger package size due to its extensive functionality
Steeper learning curve for beginners
May be overkill for projects that only need basic data manipulation

Code Comparison

danfojs:

const dfd = require("danfojs-node")

let df = new dfd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
let mean = df.mean()
console.log(mean)

stdlib:

const stdlib = require('@stdlib/stdlib');

const arr = [1, 2, 3, 4, 5, 6];
const mean = stdlib.stats.mean(arr);
console.log(mean);

Summary

While stdlib offers a more comprehensive set of mathematical and statistical functions, danfojs focuses specifically on data manipulation and analysis, similar to pandas in Python. stdlib is better suited for projects requiring advanced mathematical operations, while danfojs may be more appropriate for data-centric applications. The choice between the two depends on the specific needs of your project and your familiarity with each library's syntax and structure.

data-forge-ts

1,345

The JavaScript data transformation and analysis toolkit inspired by Pandas and LINQ.

Pros of Data-Forge-ts

Written in TypeScript, providing better type safety and developer experience
More comprehensive data manipulation capabilities, including advanced filtering and grouping
Supports both Node.js and browser environments

Cons of Data-Forge-ts

Smaller community and fewer contributors compared to Danfojs
Less focus on machine learning and statistical operations
Documentation may be less extensive and user-friendly

Code Comparison

Data-Forge-ts:

import { DataFrame } from 'data-forge';

const df = new DataFrame({
    columnNames: ["A", "B", "C"],
    rows: [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
});
const filtered = df.where(row => row.A > 3);

Danfojs:

import dfd from "danfojs-node"

const df = new dfd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], 
    { columns: ["A", "B", "C"] })
const filtered = df.query({ column: "A", is: ">", to: 3 })

Both libraries offer similar functionality for creating and manipulating dataframes, but with slightly different syntax and approaches. Data-Forge-ts leverages TypeScript's type system, while Danfojs focuses on providing a pandas-like API for JavaScript developers.

arrow

14,426

Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

Pros of Arrow

Broader language support (C++, Python, R, etc.) beyond JavaScript
More mature project with larger community and ecosystem
Optimized for high-performance data processing and analytics

Cons of Arrow

Steeper learning curve due to its lower-level nature
Less focused on data manipulation and analysis in JavaScript specifically
Requires more setup and configuration for JavaScript projects

Code Comparison

Arrow (JavaScript):

const arrow = require('apache-arrow');
const table = arrow.Table.from([
  { id: 1, name: 'John' },
  { id: 2, name: 'Jane' }
]);

Danfojs:

const dfd = require('danfojs-node');
const df = new dfd.DataFrame([
  { id: 1, name: 'John' },
  { id: 2, name: 'Jane' }
]);

Summary

Arrow is a cross-language development platform for in-memory data, offering high performance and interoperability. Danfojs is specifically designed for data manipulation and analysis in JavaScript, providing a more accessible API for web developers. While Arrow excels in performance and multi-language support, Danfojs offers a more straightforward approach for JavaScript-centric data projects.

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README

Danfojs: powerful javascript data analysis toolkit

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 library, and provides a similar API. This means that users familiar with Pandas, 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 to Tensors.
Easy handling of missing-data (represented as NaN) in floating point as well as non-floating point data
Size mutability: columns can be inserted/deleted from DataFrame
Automatic and explicit alignment: 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 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, fancy indexing, and querying of large data sets
Intuitive merging and joining data sets
Robust IO tools for loading data from flat-files (CSV, Json, Excel).
Powerful, flexible and intutive API for plotting DataFrames and Series interactively.
Timeseries-specific functionality: date range generation and date and time properties.
Robust data preprocessing functions like OneHotEncoders, LabelEncoders, and scalers like StandardScaler and 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:

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:

npm install danfojs

or

yarn add danfojs

For use directly in HTML files, you can add the latest script tag from JsDelivr to your HTML file:

    <script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.js"></script>

See all available versions here

Quick Examples

Example Usage in the Browser


<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.js"></script>

    <title>Document</title>
  </head>

  <body>
    <div id="div1"></div>
    <div id="div2"></div>
    <div id="div3"></div>

    <script>

      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);
          })
    </script>
  </body>
</html>

Output in Browser:

Example usage in Nodejs

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:

Notebook support

VsCode nodejs notebook extension now supports Danfo.js. See guide here
ObservableHQ Notebooks. See example notebook here

See the Official Getting Started Guide

Documentation

The official documentation can be found here

Danfo.js Official Book

We published a book titled "Building Data Driven Applications with Danfo.js". Read more about it here

Discussion and Development

Development discussions take place here.

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.

Licence MIT

Created by Rising Odegua and Stephen Oni

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of stdlib

Cons of stdlib

Code Comparison

Summary

Pros of Data-Forge-ts

Cons of Data-Forge-ts

Code Comparison

Pros of Arrow

Cons of Arrow

Code Comparison

Summary

Convert designs to code with AI

README

Danfojs: powerful javascript data analysis toolkit

What is it?

Main Features

Installation

Quick Examples

Example Usage in the Browser

Example usage in Nodejs

Notebook support

Documentation

Danfo.js Official Book

Discussion and Development

Contributing to Danfo

Licence MIT

Created by Rising Odegua and Stephen Oni

Top Related Projects

Convert designs to code with AI

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