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danfojs

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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

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 and delimited) and JSON data format.
Timeseries-specific functionality: date range generation and date and time properties.

To use danfo.js via script tags, copy and paste the CDN below to your HTML file

  <script src="https://cdn.jsdelivr.net/npm/danfojs@0.0.13/dist/index.min.js"></script>

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@0.0.13/dist/index.min.js"></script>
    <title>Document</title>
</head>

<body>

    <div id="some_div"></div>
    <div id="alldiv"></div>
    <script>

        dfd.read_csv("https://raw.githubusercontent.com/risenW/medium_tutorial_notebooks/master/train.csv")
            .then(df => {
                df.describe().print()

                //prints in console
                //  Shape: (5,5) 

                // ╔════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
                // ║        │ Product_Weight    │ Product_Shelf...  │ Product_Price     │ Product_Super...  │ Supermarket_O...  ║
                // ╟────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
                // ║ count  │ 4188              │ 4990              │ 4990              │ 4990              │ 4990              ║
                // ╟────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
                // ║ mean   │ 12.908838         │ 0.066916          │ 391.803772        │ 6103.52002        │ 2004.783447       ║
                // ╟────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
                // ║ std    │ NaN               │ 0.053058          │ 119.378259        │ 4447.333835       │ 8.283151          ║
                // ╟────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
                // ║ min    │ 4.555             │ 0                 │ 78.730003         │ 83.230003         │ 1992              ║
                // ╟────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
                // ║ median │ NaN               │ 0.053564          │ 393.86            │ 5374.675          │ 2006              ║
                // ╚════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

                var layout = {
                    title: 'A sample plot',
                    xaxis: {
                        title: 'X',
                    },
                    yaxis: {
                        title: 'Y',
                    }
                };

                //Displays plot in the specified div
                df['Product_Weight'].plot("some_div", { kind: "histogram" })
                df.plot("alldiv", { x: "Product_Price", y: "Product_Shelf_Visibility", kind: "scatter", mode: 'markers' })


            }).catch(err => {
                console.log(err);
            })
    </script>
</body>

</html>

To install Node.js version, go here

Installation from sources

To install danfo in [development mode], clone the repo:

git clone https://github.com/opensource9ja/danfojs

cd into danfojs folder and run:

npm install

Documentation

The official documentation can be found here

Discussion and Development

Most development discussions take place on github in this repo. Feel free to use the issues tab.

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. As contributors and maintainers to this project, you are expected to abide by danfo' code of conduct. More information can be found at: Contributor Code of Conduct Javascript version of Pandas

danfojs