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‎.gitignore‎

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.editorconfig
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node_modules
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node_modules
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test/fixtures/*
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test/samples/*
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*.xlsx

‎README.md‎

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- 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
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## Installation
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There are three ways to install and use Danfo.js in your application
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* For Nodejs applications, you can install the [__danfojs-node__]() version via package managers like yarn and/or npm:
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To use Danfo.js via script tags, copy and paste the CDN below to the body of your HTML file
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```bash
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npm install danfojs-node
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or
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yarn add danfojs-node
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```
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For client-side applications built with frameworks like React, Vue, Next.js, etc, you can install the [__danfojs__]() version:
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```bash
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npm install danfojs
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or
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yarn add danfojs
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```
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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:
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script>
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```html
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<script src="https://cdn.jsdelivr.net/npm/danfojs@0.3.4/lib/bundle.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/danfojs@1.0.0/lib/bundle.min.js"></script>
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```
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See all available versions [here](https://www.jsdelivr.com/package/npm/danfojs)
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### Example Usage in the Browser
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> See the example below in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG)
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> Run in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG)
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```html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<script src="https://cdn.plot.ly/plotly-2.2.0.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script>
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script>
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<title>Document</title>
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</head>
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<body>
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</head>
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<body>
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<div id="div1"></div>
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<div id="div2"></div>
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<div id="div3"></div>
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<script>
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dfd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
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.then(df => {
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dfd.readCSV("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
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.then(df => {
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df['AAPL.Open'].plot("div1").box() //makes a box plot
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df['AAPL.Open'].plot("div1").box() //makes a box plot
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df.plot("div2").table() //display csv as table
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df.plot("div2").table() //display csv as table
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new_df = df.set_index({ column: "Date" }) //resets the index to Date column
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new_df.plot("div3").line({ columns: ["AAPL.Open", "AAPL.High"] }) //makes a timeseries plot
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}).catch(err => {
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console.log(err);
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})
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new_df = df.setIndex({ column: "Date", drop: true }); //resets the index to Date column
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new_df.head().print() //
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new_df.plot("div3").line({
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config: {
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columns: ["AAPL.Open", "AAPL.High"]
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}
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}) //makes a timeseries plot
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}).catch(err => {
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console.log(err);
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})
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</script>
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</body>
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</body>
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</html>
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```
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Output in Browser:
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![](assets/browser-out.gif)
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## How to install
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Danfo.js is hosted on NPM, and can installed via package managers like npm and yarn
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```sh
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npm install danfojs-node
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```
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### Example usage in Nodejs
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```javascript
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const dfd = require("danfojs-node")
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const file_url = "https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv"
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dfd.read_csv(file_url)
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dfd.readCSV(file_url)
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.then(df => {
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//prints the first five columns
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df.head().print()
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df_drop.print()
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//select columns by dtypes
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let str_cols = df_drop.selectDtypes(["string"])
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let num_cols = df_drop.selectDtypes(["int32", "float32"])
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str_cols.print()
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num_cols.print()
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//select columns by dtypes
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let str_cols = df_drop.select_dtypes(["string"])
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let num_cols = df_drop.select_dtypes(["int32", "float32"])
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str_cols.print()
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num_cols.print()
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//add new column to Dataframe
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let new_vals = df['Fare'].round(1)
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df_drop.addColumn({ column: "fare_round", values: new_vals, inplace: true })
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df_drop.addColumn("fare_round", new_vals, { inplace: true })
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df_drop.print()
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df_drop['fare_round'].round(2).print(5)
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//prints the number of occurence each value in the column
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df_drop['Survived'].value_counts().print()
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df_drop['Survived'].valueCounts().print()
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//print the last ten elementa of a DataFrame
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df_drop.tail(10).print()
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df_drop.isna().sum().print()
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df_drop.isNa().sum().print()
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}).catch(err => {
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console.log(err);
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})
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```
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Output in Node Console:
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![](assets/node-rec.gif)
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> If you want to use Danfo in frontend frameworks like React/Vue, read this [guide](https://danfo.jsdata.org/examples/using-danfojs-in-react)
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## Notebook support
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* You can use Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo)
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* VsCode nodejs notebook extension now supports Danfo.js. See guide [here](https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook)
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#### Created by [Rising Odegua](https://github.com/risenW) and [Stephen Oni](https://github.com/steveoni)
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<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>
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<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>

‎package.json‎

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"danfojs-browser/**"
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],
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"scripts": {
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"build": "cd danfojs-node && yarn build:clean && cd ../danfojs-browser && yarn build:clean",
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"test": "cd danfojs-node && yarn && yarn test:clean && cd ../danfojs-browser && yarn && yarn test:clean"
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"install": "cd src/danfojs-base && yarn && cd ../danfojs-browser && yarn && cd ../danfojs-node && yarn",
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"build": "cd src/danfojs-node && yarn build:clean && cd ../danfojs-browser && yarn build:clean",
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"test": "cd src/danfojs-base && yarn && cd ../danfojs-node && yarn && yarn test:clean && cd ../danfojs-browser && yarn && yarn test:clean"
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}
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}

‎src/danfojs-base/README.md‎

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

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