# Technical Documentation: javascriptdata/danfojs

> ℹ️ **Provenance:** Multi-Tier Ingestion · [CodeWiki Reference](https://codewiki.google/github.com/javascriptdata/danfojs)

### Overview (`README.md`)

<div align="center">
  <img src="assets/logo.png"><br>
</div>

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

## Danfojs: powerful javascript data analysis toolkit 

[](https://www.jsdelivr.com/package/npm/danfojs)
[](https://coveralls.io/github/opensource9ja/danfojs)
 
<span class="badge-patreon"><a href="https://www.patreon.com/bePatron?u=40496758" title="Donate to this project using Patreon"><img src="https://img.shields.io/badge/patreon-donate-yellow.svg" alt="Patreon donate button" /></a></span>

## 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](https://pandas.pydata.org/pandas-docs/stable/) library, and provides a similar API. This means that users familiar with [Pandas](https://pandas.pydata.org/pandas-docs/stable/), can easily pick up danfo.js. 

## Main Features

  - Danfo.js is fast and supports Tensorflow.js tensors out of the box. This means you can [convert Danfo data structure](https://danfo.jsdata.org/api-reference/dataframe/dataframe.tensor) to Tensors.
  - Easy handling of [missing-data](https://danfo.jsdata.org/getting-started#missing-data) (represented as
    `NaN`) in floating point as well as non-floating point data
  - Size mutability: columns can be [inserted/deleted](https://danfo.jsdata.org/api-reference/dataframe#combining-comparing-joining-merging) from DataFrame
  - Automatic and explicit [alignment](https://danfo.jsdata.org/api-reference/dataframe#reindexing-selection-label-manipulation): 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](https://danfo.jsdata.org/api-reference/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](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.loc), [fancy indexing](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.iloc), and [querying](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.query) of
    large data sets
  - Intuitive [merging](https://danfo.jsdata.org/api-reference/general-functions/danfo.merge) and [joining](https://danfo.jsdata.org/api-reference/general-functions/danfo.concat) data
    sets
  - Robust IO tools for loading data from [flat-files](https://danfo.jsdata.org/api-reference/input-output)
    (CSV, Json, Excel).
  - Powerful, flexible and intutive API for [plotting](https://danfo.jsdata.org/api-reference/plotting) DataFrames and Series interactively.
  - [Timeseries](https://danfo.jsdata.org/api-reference/series#accessors)-specific functionality: date range
    generation and date and time properties. 
  - Robust data preprocessing functions like [OneHotEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.onehotencoder), [LabelEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.labelencoder), and scalers like [StandardScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.standardscaler) and [MinMaxScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.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](https://www.jsdelivr.com/package/npm/danfojs) to your HTML file:

```html
    <script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.js"></script>
```
See all available versions [here](https://www.jsdelivr.com/package/npm/danfojs)

### Quick Examples
* [Danfojs with HTML and vanilla JavaScript on CodePen](https://codepen.io/risingodegua/pen/bGpwyYW)
* [Danfojs with React on Code Sandbox](https://codesandbox.io/s/using-danfojs-in-react-dwpv54?file=/src/App.js)
* [Danfojs on ObservableHq](https://observablehq.com/@risingodegua/using-danfojs-on-observablehq)
* [Danfojs in Nodejs on Replit](https://replit.com/@RisingOdegua/Danfojs-in-Nodejs)

### Example Usage in the Browser
```html

<!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@1.1.2/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

```
/* Detailed source-code truncated for AI context efficiency. */
```
Output in Node Console:


## Notebook support
* VsCode nodejs notebook extension now supports Danfo.js. See guide [here](https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook)
* ObservableHQ Notebooks. See example notebook [here](https://observablehq.com/@risingodegua/using-danfojs-on-observablehq)

#### [See the Official Getting Started Guide](https://danfo.jsdata.org/getting-started)

## Documentation
The official documentation can be found [here](https://danfo.jsdata.org)

## Discussion and Development
Development discussions take place [here](https://github.com/opensource9ja/danfojs/discussions). 

## 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](https://danfo.jsdata.org/contributing-guide).

#### Licence [MIT](https://github.com/opensource9ja/danfojs/blob/master/LICENCE)

---

### Overview (`src/danfojs-base/README.md`)

## danfojs-base 

**danfojs-base** is the core module of Danfo.js. Danfojs-node and Danfojs-browser folders simply extends/export this functions and classes from this module.

## Folders and Files

  - __aggregators__: All files that contain functions that aggregate data.
  - __core__: Holds the core classes of Danfo.js.
    - `daterange`: Class that represents a date range.
    - `datetime`: Class that represents a date and time.
    - `frame`: Class that represents a frame.
    - `series`: Class that represents a series.
    - `generic`: Class that represents a generic object.
    - `indexing`: Class that represents an indexing.
    - `math.ops`: Class that represents a math operation.
    - `strings`: Class that represents a string.  
  - __io__: Holds the IO classes of Danfo.js.
    - __browser__: Holds the browser IO classes.
      - `io.csv`: Holds the CSV IO classes.
      - `io.json`: Holds the JSON IO classes.
      - `io.excel`: Holds the excel IO classes.
    - __node__: Holds the node IO classes for Node.js
      - `io.csv`: Holds the CSV IO classes for Node.js
      - `io.json`: Holds the JSON IO classes for Node.js
      - `io.excel`: Holds the excel IO classes for Node.js
  - __plotting__: Holds the plotting classes
    - __plotly__: Holds the plotting class for Plotly charts.
    - __vega__: (Stub) Holds the plotting class for Vega charts.
  - __shared__
    - `config`: Holds the configuration class.
    - `defaults`: Holds the default values for the configuration class.
    - `errors`: Holds the error classes.
    - `tensorflowlib`: Autogenerated tensorflow library export.
    - `utils`: Holds the utility classes.
    - `types`: Holds the type classes.
  - __transformers__: Holds the transformers files
    - __encoders__: Holds the encoder classes.
       - `dummy.encoder`: Holds the dummy encoder class
        - `one.hot.encoder`: Holds the onehot encoder class
        - `label.encoder`: Holds the label encoder class
    - __scalers__
      - `min.max.scalers`: Holds the min max scaler class
      - `standard.scalers`: Holds the standard scaler class
    - `concat`: Holds the concatenation class
    - `merge`: Holds the merge class
  - `index.ts`: Entry point for the module.

---

### Overview (`src/danfojs-browser/README.md`)

<div align="center">
  <img src="assets/logo.png"><br>
</div>

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

## Danfojs: powerful javascript data analysis toolkit 

[](https://www.jsdelivr.com/package/npm/danfojs)
[](https://coveralls.io/github/opensource9ja/danfojs)
 
<span class="badge-patreon"><a href="https://www.patreon.com/bePatron?u=40496758" title="Donate to this project using Patreon"><img src="https://img.shields.io/badge/patreon-donate-yellow.svg" alt="Patreon donate button" /></a></span>


## 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](https://pandas.pydata.org/pandas-docs/stable/) library, and provides a similar API. This means that users familiar with [Pandas](https://pandas.pydata.org/pandas-docs/stable/), 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](https://danfo.jsdata.org/api-reference/dataframe/dataframe.tensor) to Tensors.
  - Easy handling of [missing-data](https://danfo.jsdata.org/getting-started#missing-data) (represented as
    `NaN`) in floating point as well as non-floating point data
  - Size mutability: columns can be [inserted/deleted](https://danfo.jsdata.org/api-reference/dataframe#combining-comparing-joining-merging) from DataFrame
  - Automatic and explicit [alignment](https://danfo.jsdata.org/api-reference/dataframe#reindexing-selection-label-manipulation): 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](https://danfo.jsdata.org/api-reference/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](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.loc), [fancy indexing](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.iloc), and [querying](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.query) of
    large data sets
  - Intuitive [merging](https://danfo.jsdata.org/api-reference/general-functions/danfo.merge) and [joining](https://danfo.jsdata.org/api-reference/general-functions/danfo.concat) data
    sets
  - Robust IO tools for loading data from [flat-files](https://danfo.jsdata.org/api-reference/input-output)
    (CSV, Json, Excel, Data package).
  - Powerful, flexible and intutive API for [plotting](https://danfo.jsdata.org/api-reference/plotting) DataFrames and Series interactively.
  - [Timeseries](https://danfo.jsdata.org/api-reference/series#accessors)-specific functionality: date range
    generation and date and time properties. 
  - Robust data preprocessing functions like [OneHotEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.onehotencoder), [LabelEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.labelencoder), and scalers like [StandardScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.standardscaler) and [MinMaxScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.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](https://www.jsdelivr.com/package/npm/danfojs) to your HTML file:

```html
    <script src="https://cdn.jsdelivr.net/npm/danfojs@1.2.0/lib/bundle.js"></script>
```
See all available versions [here](https://www.jsdelivr.com/package/npm/danfojs)

### Example Usage in the Browser

> Run in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG)

```html

<!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@1.2.0/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

```
/* Detailed source-code truncated for AI context efficiency. */
```
Output in Node Console:


## Notebook support
* You can use Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo)
* VsCode nodejs notebook extension now supports Danfo.js. See guide [here](https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook)

#### [See the Official Getting Started Guide](https://danfo.jsdata.org/getting-started)

## Documentation
The official documentation can be found [here](https://danfo.jsdata.org)

## Danfo.js Official Book




We recently published a book titled "Building Data Driven Applications with Danfo.js". Read more about it [here](https://danfo.jsdata.org/building-data-driven-applications-with-danfo.js-book)

## Discussion and Development
Development discussions take place [here](https://github.com/opensource9ja/danfojs/discussions). 

## 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](https://danfo.jsdata.org/contributing-guide).

#### Licence [MIT](https://github.com/opensource9ja/danfojs/blob/master/LICENCE)

#### Created by [Rising Odegua](https://github.com/risenW) and [Stephen Oni](https://github.com/steveoni)

<a href="https://www.producthunt.com/posts/danfo-js?utm_source=badge-featured&utm_medium=badge&utm_souce=badge-danfo-js" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=233871&theme=light" alt="Danfo.js - Open Source JavaScript library for manipulating data. | Product Hunt Embed" style="width: 250px; height: 54px;" width="250px" height="54px" /></a>

---

### Overview (`src/danfojs-node/README.md`)

<div align="center">
  <img src="assets/logo.png"><br>
</div>

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

## Danfojs: powerful javascript data analysis toolkit 

[](https://www.jsdelivr.com/package/npm/danfojs)
[](https://coveralls.io/github/opensource9ja/danfojs)
 
<span class="badge-patreon"><a href="https://www.patreon.com/bePatron?u=40496758" title="Donate to this project using Patreon"><img src="https://img.shields.io/badge/patreon-donate-yellow.svg" alt="Patreon donate button" /></a></span>


## 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](https://pandas.pydata.org/pandas-docs/stable/) library, and provides a similar API. This means that users familiar with [Pandas](https://pandas.pydata.org/pandas-docs/stable/), 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](https://danfo.jsdata.org/api-reference/dataframe/dataframe.tensor) to Tensors.
  - Easy handling of [missing-data](https://danfo.jsdata.org/getting-started#missing-data) (represented as
    `NaN`) in floating point as well as non-floating point data
  - Size mutability: columns can be [inserted/deleted](https://danfo.jsdata.org/api-reference/dataframe#combining-comparing-joining-merging) from DataFrame
  - Automatic and explicit [alignment](https://danfo.jsdata.org/api-reference/dataframe#reindexing-selection-label-manipulation): 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](https://danfo.jsdata.org/api-reference/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](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.loc), [fancy indexing](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.iloc), and [querying](https://danfo.jsdata.org/api-reference/dataframe/danfo.dataframe.query) of
    large data sets
  - Intuitive [merging](https://danfo.jsdata.org/api-reference/general-functions/danfo.merge) and [joining](https://danfo.jsdata.org/api-reference/general-functions/danfo.concat) data
    sets
  - Robust IO tools for loading data from [flat-files](https://danfo.jsdata.org/api-reference/input-output)
    (CSV, Json, Excel, Data package).
  - Powerful, flexible and intutive API for [plotting](https://danfo.jsdata.org/api-reference/plotting) DataFrames and Series interactively.
  - [Timeseries](https://danfo.jsdata.org/api-reference/series#accessors)-specific functionality: date range
    generation and date and time properties. 
  - Robust data preprocessing functions like [OneHotEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.onehotencoder), [LabelEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.labelencoder), and scalers like [StandardScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.standardscaler) and [MinMaxScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.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](https://www.jsdelivr.com/package/npm/danfojs) to your HTML file:

```html
    <script src="https://cdn.jsdelivr.net/npm/danfojs@1.2.0/lib/bundle.js"></script>
```
See all available versions [here](https://www.jsdelivr.com/package/npm/danfojs)

### Example Usage in the Browser

> Run in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG)

```html

<!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@1.2.0/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

```
/* Detailed source-code truncated for AI context efficiency. */
```
Output in Node Console:


## Notebook support
* You can use Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo)
* VsCode nodejs notebook extension now supports Danfo.js. See guide [here](https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook)

#### [See the Official Getting Started Guide](https://danfo.jsdata.org/getting-started)

## Documentation
The official documentation can be found [here](https://danfo.jsdata.org)

## Danfo.js Official Book




We recently published a book titled "Building Data Driven Applications with Danfo.js". Read more about it [here](https://danfo.jsdata.org/building-data-driven-applications-with-danfo.js-book)

## Discussion and Development
Development discussions take place [here](https://github.com/opensource9ja/danfojs/discussions). 

## 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](https://danfo.jsdata.org/contributing-guide).

#### Licence [MIT](https://github.com/opensource9ja/danfojs/blob/master/LICENCE)

#### Created by [Rising Odegua](https://github.com/risenW) and [Stephen Oni](https://github.com/steveoni)

<a href="https://www.producthunt.com/posts/danfo-js?utm_source=badge-featured&utm_medium=badge&utm_souce=badge-danfo-js" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=233871&theme=light" alt="Danfo.js - Open Source JavaScript library for manipulating data. | Product Hunt Embed" style="width: 250px; height: 54px;" width="250px" height="54px" /></a>

---

--- METRICS ---
- Files Extracted: 4
- Estimated Token Budget: ~6027 tokens
- Canonical Reference: https://codewiki.google/github.com/javascriptdata/danfojs