odpxprueba 1.22

The fundamental toolkit for outliers search and visualization

outdpik: Fundamental toolkit for outlier analysis and visualization

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.

Main Features

Here are just a few of the things that outdpik does well:

• 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

Where to get it

The source code is currently hosted on GitHub at: https://github.com/DanielPuentee/outdpik

Installer for the latest released version is available at the Python Package Index (PyPI)

# PyPI
pip install outdpik

How to use outdpik

Examples of configuring and running outpdik:

import outpdik as outdp
outdp = outdp()

We proceed to detect outliers returning a dictionary of numeric features and the outliers instances:

outliers_dict = outdp.outliers(df = df, cols = "all")

Plotting advantages:

outdp.plot_outliers(df = df, col = "x")

Dependencies

• pandas - Provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive
• NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays
• SciPy - Includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more
• matplotlib - Comprehensive library for creating static, animated, and interactive visualizations in Python
• seaborn - Provides a high-level interface for drawing attractive statistical graphics

License

This project is licensed under the terms of the GNU - see the LICENSE file for details.

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.