Metadata-Version: 2.1
Name: dumbledore
Version: 0.0.3
Summary: a python package to visvalize features of data and observe relation without writing lots of codes
Home-page: https://github.com/imZain448/dumbledore
Author: imzain448
Author-email: ahmadzain.448@gmail.com
License: UNKNOWN
Description: # Dumbledore
        this is a python module which is intended for data scientist and statistician to visualize features and understand relationships between different features without writing lots of codes. <br>
        This module is like an aggregator which combines multiple functionalities of different modules and brings them together to keep you out of trouble
        
        ------
        ----
        __installation__
        ```
        pip install dumbledore
        ```
        
        __requirements__ <br>
        this module depends on all most of scientific libraries like `pandas` , `matplotlib` , `seaborn`, `numpy` etc. all of which you must have already installed if you are a data scientist or statistician but don't worry if you haven't the pip command will automatically install all the dependencies.
        
        __examples__ <br>
        the  `vis_feature()` function helps you understand and visualize between different types of variables
        1. for a continuous feature and categorical target variable
        ```python
        import dumbledore as db
        import pandas as pd
        ...
        # load data
        ...
        db.basics.vis_feature((data , 'gender' , 'tenure', 'gist_earth_r' , target_continous=True , jitter=0.3)
        ```
        ![data](https://github.com/imZain448/dumbledore/blob/master/images/data1.png?raw=true)
        ![plot](https://github.com/imZain448/dumbledore/blob/master/images/plot1.png?raw=true)
        
        1. when your feature and target both are continous
        ```python
        db.basics.vis_feature((data , 'tenure' , 'MonthlyCharges', 'gist_earth_r' , continous=True , target_continous=True )
        ```
        ![data2](https://github.com/imZain448/dumbledore/blob/master/images/data2.png?raw=true)
        ![plot2](https://github.com/imZain448/dumbledore/blob/master/images/plot2.png?raw=true)
        
        similarly you can feed in categorical target or categorical feature you have to keep in mind the two arguements
        >__continous__ : True if feature is continous, False if feature categorical<br> 
        >__target_continous__ : True if target variable is continous , False if target variable is categorical
        
        
        ----
        __note__ : this module is still under development and has been released as alpha. so if you get any bug please open a issue without hesitation.
        
        > _THIS PRODUCT IS DISTRIBUTED UNDER GNU GPLv3 WHICH CAN BE FOUND IN THE LICENSE FILE_
        
        __developed by__ : [imzain448](https://github.com/imZain448) <br>
        meet me on
        - [Linkedin](https://www.linkedin.com/in/zain-ahmad-15aa25162/)
        - [Instagram](https://www.instagram.com/imzain448/?hl=en)
        - [Gmail : ahmadzain.448@gmail.com]
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
