# 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.
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"description": "\n# MGD_Outliers\nMGD_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.\n\n# Current version\n version 0.1.4\n\n# Installation\n\nYou can install MGD_Outliers using pip:\n\n pip install MGD_Outliers\n \n\n# Usage\n\nTo use the OutlierNinja class in MGD_Outliers, you first need to import the package:\n\n from MGD_Outliers import OutlierNinja\n \n\nThen, you have to create an instance of the OutlierNinja class:\n\n outliers = OutlierNinja(limit_factor=1.5) # 1.5 is a default value, you can change it as per your project requirement\n \n\nThen, you need to train this object using a dataframe object\n\n outliers.fit(data)\n\n\nOnce 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. \nFor example, you can use the detect_outliers method to locate outliers in the data:\n\n outliers.detect_outliers(column_name='age')\n \n\nYou can also use the plot_outlier_count method to plot barplot of the columns:\n\n outliers.plot_outlier_count()\n\n\nFor 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.\n\n\n# License\nThis package is licensed under the Apache License 2.0. See the LICENSE file for more information.\n\n\n# Contributing\nIf 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.\n\n\n\n\n\n",
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