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js-neutron-frame
js-neutron-frame/dist/index.js

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Readme

Table of Contents

About The Project
Getting Started
Documentation and Examples
About The Project
- Built With
Getting Started
- Prerequisites
- Installation
Usage
Contributing
License
Contact
Acknowledgments

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About The Project

"js-neutron-frame" is a powerful library written in TypeScript designed to provide data manipulation and statistical analysis capabilities, akin to Python's Pandas library, but tailored specifically for JavaScript/TypeScript and Node.js applications. The library offers a flexible and efficient way to work with tabular data, enabling developers to perform data transformations, statistical calculations, and more, all within the JavaScript ecosystem.

Key Features:

Data Manipulation: With "js-neutron-frame", you can effortlessly work with data in tabular format, treating it like a spreadsheet. The library supports adding, updating, and deleting rows and columns, making data manipulation a breeze.

Statistical Analysis: "js-neutron-frame" comes equipped with a rich set of statistical functions. Perform operations such as calculating mean, median, mode, and standard deviation on columns or the entire DataFrame, gaining valuable insights from your data.

Data Import and Export: Easily import data from CSV and Excel files, ensuring seamless integration with your existing datasets. The library empowers you to export results and processed data back to various file formats with ease.

Dynamic Column Handling: When using the addRow function, "DataFrame.js" dynamically adjusts columns based on the provided data. New columns are automatically created if the keys in the provided row data do not exist in the current columns.

Flexible and Lightweight: Built with modern JavaScript, "DataFrame.js" offers a lightweight and versatile solution for data analysis. It is optimized for speed and memory efficiency, making it suitable for both small and large datasets.

Easy Integration: As an npm module, "DataFrame.js" is readily available for integration into any JavaScript or Node.js project. Simply install the package and start harnessing its data manipulation capabilities right away.

About The Project

There are many great README templates available on GitHub; however, I didn't find one that really suited my needs so I created this enhanced one. I want to create a README template so amazing that it'll be the last one you ever need -- I think this is it.

Here's why:

Your time should be focused on creating something amazing. A project that solves a problem and helps others
You shouldn't be doing the same tasks over and over like creating a README from scratch
You should implement DRY principles to the rest of your life 😄

Of course, no one template will serve all projects since your needs may be different. So I'll be adding more in the near future. You may also suggest changes by forking this repo and creating a pull request or opening an issue. Thanks to all the people have contributed to expanding this template!

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Getting Started

This is walkthrough of how the library can be installed/

Prerequisites and Installation

Install Node.js and npm
For TypeScript projects, install TypeScript [This step is not required for JavaScript projects]

npm install typescript --save-dev

Install the library from npm modules:

npm install js-neutron-frame

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Documentation and Examples

import the library

import { DataFrame, readCSV } from "js-neutron-frame";

Create DataFrame

Create an empty DataFrame:

const df = new DataFrame();

Create DataFrame by reading from .csv file:

const df = await readCSV('./oscar_age_male.csv');

DataFrame utilities

addRows(DataFrameRow): void The keys refer to the columns If a key is new, it is added to the columns

df.addRow({Index:1, Year: 1928, Age:44, Name:'Emil Jannings', Movie: 'The Last Command'});
df.addRow({Index:2, Year: 1929, Age:41, Name:'Warner Baxter', Movie: 'The Old Arizona'});
df.addRow({Index:3, Year: 1930, Age:62, Name:'George Arliss', Movie: 'Disraeli'});
df.addRow({Index:4, Year: 1931, Age:53, Name:'Lionel Barrymore', Movie: 'A Free Soul'});

addColumns(name: string, values: (number | string | boolean)[]): void

const BoxOffice = [50000, 60000, 75000];
df.addColumn('BoxOffice', salaryData);

getShape(): [row_size, column_size] returns the [rows, columns] of the DataFrame

df.getShape();

Output: [ 89, 5 ]

filterByColums(columnName: string, value: number | string | boolean): DataFrameRow[] Takes columnName and the value that has to be matched as input. This does simple plain matching and returns the filtered rows.

df.filterByColumn('Age', 44)

Output:

[
  {
    Index: 1,
    Year: 1928,
    Age: 44,
    Name: 'Emil Jannings',
    Movie: 'The Last Command'
  },
  {
    Index: 87,
    Year: 2014,
    Age: 44,
    Name: 'Matthew McConaughey',
    Movie: 'Dallas Buyers Club'
  }
]

showFirstRows(n: number = 5): DataFrameRow[] Shows n rows from beggining. Default 5 rows are shown if not specified.

const firstRows = df.showFirstRows(3);

Output:

[
  {
    Index: 1,
    Year: 1928,
    Age: 44,
    Name: 'Emil Jannings',
    Movie: 'The Last Command'
  },
  {
    Index: 2,
    Year: 1929,
    Age: 41,
    Name: 'Warner Baxter',
    Movie: 'In Old Arizona'
  },
  {
    Index: 3,
    Year: 1930,
    Age: 62,
    Name: 'George Arliss',
    Movie: 'Disraeli'
  }
]

showLastRows(n: number = 5): DataFrameRow[] Shows n rows from last. Default 5 rows are shown if not specified.

const lastRows = df.showLastRows(2)

Output:

[
  {
    Index: 3,
    Year: 1930,
    Age: 62,
    Name: 'George Arliss',
    Movie: 'Disraeli'
  },
  {
    Index: 4,
    Year: 1931,
    Age: 53,
    Name: 'Lionel Barrymore',
    Movie: 'A Free Soul'
  }
]

getColumnDataTypes(): { [key: string]: string } returns the data types of the columns based on their values

df.getColumnDataTypes();

Output:

{
  Index: 'number',
  Year: 'number',
  Age: 'number',
  Name: 'string',
  Movie: 'string'
}

Statistical Opeartions on the Data

For more examples, please refer to the google doc Documentation

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

npm
```
npm install npm@latest -g
```

Installation

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

Get a free API Key at https://example.com

Clone the repo

git clone https://github.com/your_username_/Project-Name.git

Install NPM packages
```
npm install
```
Enter your API in config.js
```
const API_KEY = 'ENTER YOUR API';
```

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Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

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<<<<<<< Updated upstream ### Contributing

Contributing

Stashed changes

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!