MGD-Outliers 0.1.4

MGD_Outliers is a Pypyththon package for identifying and visualizing outliers in a DataFrame.

MGD_Outliers

MGD_Outliers is an open-source Python package for detecting and analyzing outliers in data. The package provides a class, OutlierNinja, which can be used to detect outliers in numerical data, and provides several methods to analyze and visualize the detected outliers in quick and efficient manner.

Current version

version 0.1.4

Installation

You can install MGD_Outliers using pip:

pip install MGD_Outliers

Usage

To use the OutlierNinja class in MGD_Outliers, you first need to import the package:

from MGD_Outliers import OutlierNinja

Then, you have to create an instance of the OutlierNinja class:

outliers = OutlierNinja(limit_factor=1.5)   # 1.5 is a default value, you can change it as per your project requirement

Then, you need to train this object using a dataframe object

outliers.fit(data)

Once the OutlierNinja object is trained, you can sit back, relax with your favorite drink and call its methods and attributes like a true data ninja. For example, you can use the detect_outliers method to locate outliers in the data:

outliers.detect_outliers(column_name='age')

You can also use the plot_outlier_count method to plot barplot of the columns:

outliers.plot_outlier_count()

For a full list of methods available in the OutlierNinja class, see the documentation. I hope you find this package helpful. So, let the OutlierNinja package do the hard work, while you sip on your drink and reap the benefits of a well-optimized dataset.

License

This package is licensed under the Apache License 2.0. See the LICENSE file for more information.

Contributing

If you find any bugs or issues with MGD_Outliers, or if you have any suggestions for new features, please open an issue on GitHub. If you would like to contribute to the development of MGD_Outliers, please fork the repository and submit a pull request.