danfojs-node

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

Danfojs: powerful javascript data analysis toolkit

!Node.js CI
!Twitter

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:

``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 to your HTML file:

`html

`
See all available versions here

$3

> Run in Code Sandbox

`html














`

Output in Browser:

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

$3

`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
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

!image

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.

#### Licence MIT

#### Created by Rising Odegua and Stephen Oni