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https://github.com/danielpuentee/outdpik
The fundamental toolkit for outliers search and visualization. It aims to be the fundamental high-level package for this purpose.
https://github.com/danielpuentee/outdpik
data-analysis matplotlib numpy python
Last synced: 10 days ago
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The fundamental toolkit for outliers search and visualization. It aims to be the fundamental high-level package for this purpose.
- Host: GitHub
- URL: https://github.com/danielpuentee/outdpik
- Owner: DanielPuentee
- License: gpl-3.0
- Created: 2022-10-19T13:26:55.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-27T21:05:04.000Z (about 2 years ago)
- Last Synced: 2024-12-15T12:45:16.463Z (11 days ago)
- Topics: data-analysis, matplotlib, numpy, python
- Language: Python
- Homepage: https://pypi.org/project/outdpik/
- Size: 649 KB
- Stars: 3
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: license.txt
Awesome Lists containing this project
README
# outdpik: Fundamental toolkit for outlier analysis and visualization
[](https://pypi.org/project/outdpik/)
[](license.txt)
[](https://pypi.org/project/outdpik/)
[](https://outdpik.readthedocs.io/en/latest/)
[](https://github.com/psf/black)
## 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)](https://pypi.org/project/outdpik)
```sh
# PyPI
pip install outdpik
```
## How to use outdpik
Examples of configuring and running outpdik:
```python
import outpdik as outdp
outdp = outdp()
```
We proceed to detect outliers returning a dictionary of numeric features and the outliers instances:
```python
outliers_dict = outdp.outliers(df = df, cols = "all")
```
Plotting advantages:
```python
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](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 [GNU](https://github.com/DanielPuentee/outdpik/blob/main/license.txt) - see the LICENSE file for details.
## Documentation
The official documentation is hosted on: https://outdpik.readthedocs.io/en/latest/
## Development
Want to contribute? Great!
Open a discussion in Github in this repo and we will answer as soon as possible.