danfojs-node-nightly

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. It is built on Tensorflow.js, and supports 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, Data package).
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@0.2.7/lib/bundle.min.js"></script>

See all available versions here

Example Usage in the Browser

Run in Code Sandbox

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <script src="/Users/mac/Documents/Opensource/danfojs/src/danfojs-browser/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
        cols_2_remove = ['Age', 'Pclass']
        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

You can use Danfo.js on Dnotebooks playground here
VsCode nodejs notebook extension now supports Danfo.js. See guide here

See the Official Getting Started Guide

Documentation

The official documentation can be found here

Danfo.js Official Book

We recently 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.