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-## Main Features
-
-- It supports numpy arrays and pandas dataframes
-- Multiple outlier detection techniques that can be combined
-- Power full visualizations
-
-
-# Dillinger
-## _The Last Markdown Editor, Ever_
-
-[](https://nodesource.com/products/nsolid)
-
-[](https://travis-ci.org/joemccann/dillinger)
-
-Dillinger is a cloud-enabled, mobile-ready, offline-storage compatible,
-AngularJS-powered HTML5 Markdown editor.
-
-- Type some Markdown on the left
-- See HTML in the right
-- ✨Magic ✨
-
-## Features
-
-- Import a HTML file and watch it magically convert to Markdown
-- Drag and drop images (requires your Dropbox account be linked)
-- Import and save files from GitHub, Dropbox, Google Drive and One Drive
-- Drag and drop markdown and HTML files into Dillinger
-- Export documents as Markdown, HTML and PDF
-
-Markdown is a lightweight markup language based on the formatting conventions
-that people naturally use in email.
-As [John Gruber] writes on the [Markdown site][df1]
-
-> The overriding design goal for Markdown's
-> formatting syntax is to make it as readable
-> as possible. The idea is that a
-> Markdown-formatted document should be
-> publishable as-is, as plain text, without
-> looking like it's been marked up with tags
-> or formatting instructions.
-
-This text you see here is *actually- written in Markdown! To get a feel
-for Markdown's syntax, type some text into the left window and
-watch the results in the right.
-
-## Tech
+# outdpik: Fundamental toolkit for outlier analysis and visualization
-Dillinger uses a number of open source projects to work properly:
+[](https://pypi.org/project/outdpik/)
+[](license.txt)
+[](https://github.com/psf/black)
-- [AngularJS] - HTML enhanced for web apps!
-- [Ace Editor] - awesome web-based text editor
-- [markdown-it] - Markdown parser done right. Fast and easy to extend.
-- [Twitter Bootstrap] - great UI boilerplate for modern web apps
-- [node.js] - evented I/O for the backend
-- [Express] - fast node.js network app framework [@tjholowaychuk]
-- [Gulp] - the streaming build system
-- [Breakdance](https://breakdance.github.io/breakdance/) - HTML
-to Markdown converter
-- [jQuery] - duh
-
-And of course Dillinger itself is open source with a [public repository][dill]
- on GitHub.
-
-## Installation
-
-Dillinger requires [Node.js](https://nodejs.org/) v10+ to run.
-
-Install the dependencies and devDependencies and start the server.
-
-```sh
-cd dillinger
-npm i
-node app
-```
-
-For production environments...
-
-```sh
-npm install --production
-NODE_ENV=production node app
-```
-
-## Plugins
-
-Dillinger is currently extended with the following plugins.
-Instructions on how to use them in your own application are linked below.
-
-| Plugin | README |
-| ------ | ------ |
-| Dropbox | [plugins/dropbox/README.md][PlDb] |
-| GitHub | [plugins/github/README.md][PlGh] |
-| Google Drive | [plugins/googledrive/README.md][PlGd] |
-| OneDrive | [plugins/onedrive/README.md][PlOd] |
-| Medium | [plugins/medium/README.md][PlMe] |
-| Google Analytics | [plugins/googleanalytics/README.md][PlGa] |
-
-## Development
+## What is it?
+Outdpik is an open source Python package that provides different methods for outlier detection.
+It aims to be the fundamental high-level package for this purpose.
+Additionally, it offers visualization methods for the outlier analysis.
-Want to contribute? Great!
-
-Dillinger uses Gulp + Webpack for fast developing.
-Make a change in your file and instantaneously see your updates!
-
-Open your favorite Terminal and run these commands.
-
-First Tab:
-
-```sh
-node app
-```
-
-Second Tab:
-
-```sh
-gulp watch
-```
-
-(optional) Third:
+## Main Features
+Here are just a few of the things that outdpik does well:
-```sh
-karma test
-```
+- It supports numpy arrays and pandas dataframes
+- Multiple outlier detection techniques that can be combined
+- Powerful visualizations
+- Flexible at including one or more columns for the analysis
-#### Building for source
+## Where to get it
+The source code is currently hosted on GitHub at:
+https://github.com/DanielPuentee/outdpik
-For production release:
+Installer for the latest released version is available at the [Python
+Package Index (PyPI)](https://pypi.org/project/outdpik)
```sh
-gulp build --prod
+# PyPI
+pip install outdpik
```
-Generating pre-built zip archives for distribution:
+## How to use outdpik 🤙
+Examples of configuring and running outpdik:
-```sh
-gulp build dist --prod
+```python
+import outpdik as outdp
+outdp = outdp()
```
-## Docker
-
-Dillinger is very easy to install and deploy in a Docker container.
-
-By default, the Docker will expose port 8080, so change this within the
-Dockerfile if necessary. When ready, simply use the Dockerfile to
-build the image.
+We proceed to detect outliers returning a dictionary of numeric features and the outliers instances:
-```sh
-cd dillinger
-docker build -t /dillinger:${package.json.version} .
+```python
+outliers_dict = outdp.outliers(df = df, cols = "all")
```
+Plotting advantages:
-This will create the dillinger image and pull in the necessary dependencies.
-Be sure to swap out `${package.json.version}` with the actual
-version of Dillinger.
-
-Once done, run the Docker image and map the port to whatever you wish on
-your host. In this example, we simply map port 8000 of the host to
-port 8080 of the Docker (or whatever port was exposed in the Dockerfile):
-
-```sh
-docker run -d -p 8000:8080 --restart=always --cap-add=SYS_ADMIN --name=dillinger /dillinger:${package.json.version}
+```python
+outdp.plot_outliers(df = df, col = "x")
```
+
-> Note: `--capt-add=SYS-ADMIN` is required for PDF rendering.
-
-Verify the deployment by navigating to your server address in
-your preferred browser.
-
-```sh
-127.0.0.1:8000
-```
+## Dependencies
+- [pandas - Provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive](https://pandas.pydata.org/)
+- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
+- [SciPy - Includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more](https://scipy.org/)
+- [matplotlib - Comprehensive library for creating static, animated, and interactive visualizations in Python](https://matplotlib.org/)
+- [seaborn - Provides a high-level interface for drawing attractive statistical graphics](https://seaborn.pydata.org/)
## License
+This project is licensed under the terms of the [MIT](license.txt) - see the LICENSE file for details.
-MIT
-
-**Free Software, Hell Yeah!**
-
-[//]: # (These are reference links used in the body of this note and get stripped out when the markdown processor does its job. There is no need to format nicely because it shouldn't be seen. Thanks SO - http://stackoverflow.com/questions/4823468/store-comments-in-markdown-syntax)
-
- [dill]:
- [git-repo-url]:
- [john gruber]:
- [df1]:
- [markdown-it]:
- [Ace Editor]:
- [node.js]:
- [Twitter Bootstrap]:
- [jQuery]:
- [@tjholowaychuk]:
- [express]:
- [AngularJS]:
- [Gulp]:
-
- [PlDb]:
- [PlGh]:
- [PlGd]:
- [PlOd]:
- [PlMe]:
- [PlGa]:
+## Documentation
+The official documentation is hosted on FALTA: https://pandas.pydata.org/pandas-docs/stable
+
+## Development
+Want to contribute? Great!
+Open a discussion in Github in this repo and we will answer as soon as possible.
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