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  • License BSD-3-Clause

A lightweight JavaScript package for working with tabular data, inspired by pandas in Python.

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    Readme

    📟 A lightweight JavaScript package for working with tabular data, inspired by pandas in Python..

    npm version DOWNLOADS

    jpandas

    • 📦 Easy creation of tabular data structures in JavaScript.
    • 📦 Provides a DataFrame class inspired by pandas in Python.
    • 👨‍🏫 Developed by Rajnish.

    Table of Contents

    Installation

    npm install jpandas

or

yarn add jpandas

    Usage

    Here’s a quick example of how to use the DataFrame Library in your project:

    import DataFrame from 'jpandas';

const data = [
    { Name: 'Ankit', Age: 23, University: 'BHU' },
    { Name: 'Aishwarya', Age: 21, University: 'JNU' }
];

const df = new DataFrame(data);
console.log(df.getRowCount()); // Outputs: 2

    Creating DataFrames

    From an Array

    You can create a DataFrame from a 2D array:

    const data = [
    [1, 4, 7],
    [2, 5, 8],
    [3, 6, 9]
];
const df = new DataFrame(data);
console.log(df.getRowCount()); // Outputs: 3

    From an Object

    You can also create a DataFrame from an object where keys are column names:

    const data = {
    Name: ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'],
    Age: [23, 21, 22, 21],
    University: ['BHU', 'JNU', 'DU', 'BHU']
};
const df = new DataFrame(data);
console.log(df.getValue(0, 'Name')); // Outputs: 'Ankit'

    From CSV String

    To create a DataFrame from a CSV string:

    const csvData = `Name,Age,University\nAnkit,23,BHU\nAishwarya,21,JNU`;
const df = new DataFrame(csvData);
console.log(df.getValue(1, 'Age')); // Outputs: 21

    From JSON String

    You can also create a DataFrame from a JSON string:

    const jsonString = `[{"Name": "Ankit", "Age": 23, "University": "BHU"}, {"Name": "Aishwarya", "Age": 21, "University": "JNU"}]`;
const df = new DataFrame(JSON.parse(jsonString));
console.log(df.getValue(1, 'University')); // Outputs: 'JNU'

    DataFrame Operations

    Group By

    Group your DataFrame by a specific column:

    const grouped = df.groupBy('University');
console.log(Object.keys(grouped).length); // Outputs: number of unique universities

    Rename Columns

    You can rename columns easily:

    const renamedDf = df.rename({ a: 'x', b: 'y' });
console.log(renamedDf.getColumns()); // Outputs: ['x', 'y', 'c']

    Transform DataFrame

    Transform your DataFrame using a custom function:

    const transformedDf = df.transform(row => ({
    FullName: row.Name,
    Age: row.Age + 1
}));
console.log(transformedDf.getValue(0, 'FullName')); // Outputs: 'Ankit'

    Calculate Mean

    Calculate the mean of a numeric column:

    const meanAge = df.mean('Age');
console.log(meanAge); // Outputs: average age

    Contributing

    License

    Contact

    GitHub @rajnish93 (follow) To stay up to date on free & open-source software

    LinkedIn @krajnishsingh (connect) On the LinkedIn profile