Metadata-Version: 2.1
Name: py-AutoClean
Version: 0.0.8a0
Summary: AutoClean - Python Package for Automated Preprocessing & Cleaning of Datasets
Home-page: https://github.com/elisemercury/AutoClean
Author: Elise Landman
Author-email: elisejlandman@hotmail.com
License: MIT
Download-URL: https://github.com/elisemercury/AutoClean/archive/refs/tags/0.0.1-beta.tar.gz
Description: # AutoClean - Automated Data Preprocessing & Cleaning
        
        **AutoClean automates data preprocessing & cleaning for your next Data Science project in Python.**
        
        Read more on the AutoClean algorithm in my **Medium** article [Automated Data Cleaning withÂ Python](link).
        
        ---
        
        ## Description
        It is commonly known among Data Scientists that data cleaning and preprocessing make up a major part of a data science project. And, you will probably agree with me that it is not the most exciting part of the project. *Wouldn't it be great if this part could be automated?*
        
        AutoClean helps you exactly with that: it performs **preprocessing** and **cleaning** of data in Python in an **automated manner**, so that you can **save time** when working on your next project.
        
        AutoClean supports:
        
        * Various imputation methods for **missing values**  
        * Handling of **outliers**  
        * **Encoding** of categorical data (OneHot, Label)  
        * **Extraction** of datatime values  
        * and more!
        
        ## Basic Usage
        
        AutoClean takes a **Pandas dataframe as input** and has a built-in logic of how to **automatically** clean and process your data. You can let your dataset run through the default AutoClean pipeline by using:
        
        ````python
        from AutoClean import AutoClean
        pipeline = AutoClean(dataset)
        ````
        
        The resulting output dataframe can be accessed by using:
        
        ````python
        pipeline.output
        
        > Output:
            col_1  col_2  ...  col_n
        1   data   data   ...  data
        2   data   data   ...  data
        ... ...    ...    ...  ...
        ````
        
        ## Adjustable Parameters
        
        In some cases, the default settings of AutoClean might not optimally fit your data. Therefore it also supports **manual settings** so that you can adjust it to whatever processing steps you might need. 
        
        It has the following adjustable parameters, for which the options and descriptions can be found below:
        
        ````python
        AutoClean(dataset, missing_num='auto', missing_categ='auto', encode_categ=['auto'],     
                  extract_datetime='s', outliers='winz', outlier_param=1.5, logfile=True, verbose=False)
        ````
        
        | Parameter | Type | Default Value | Other Values |
        | ------ | :---: | :---: | ------ | 
        | missing_num | `str` | `'auto'` | `linreg`, `knn`, `mean`, `median`, `most_frequent`, `delete`, `False` |
        | missing_categ | `str` | `'auto'` | `logreg`, `knn`, `most_frequent`, `delete`, `False` |
        | missing_categ | `list` | `['auto']` | `['onehot']`, `['label']`, `False` ; to encode only specific columns add a list of column names or indexes: `['auto', ['col1', 2]]` |
        | extract_datetime | `str` | `'s'` | `D`, `M`, `Y`, `h`, `m`, `False` |
        | outliers | `str` | `'winz'` | `delete`|
        | outlier_param | `int`, `float` | `1.5` | any int or float, `False` |
        | logfile | `bool` | `True` | `False` |
        | verbose | `bool` | `False` | `True` |
        
        ### missing_num
        
        Defines how **numerical** missing values in the data are handled. Missing values can be predicted, imputed or deleted. When set to `auto`, AutoClean first attempts to predict the missing values with **Linear Regression**, and the values that could not be predicted are **imputed with K-NN**.
        
        You can specify the handling method by setting `missing_num` to: `'linreg'`, `'knn'`, `'mean'`, `'median'`, `'most_frequent'`, `'delete'` or to `False` if you want to skip this step.
        
        ### missing_categ
        
        Defines how **categorical** missing values in the data are handled. Missing values can be predicted, imputed or deleted. When set to `auto`, AutoClean first attempts to predict the missing values with **Logistic Regression**, and the values that could not be predicted are **imputed with K-NN**.
        
        You can specify the handling method by setting `missing_categ` to: `'logreg'`, `'knn'`, `'most_frequent'`, `'delete'` or to `False` if you want to skip this step.
        
        ### extract_datetime
        
        AutoClean can search the data for datetime features, and **extract** the values to separate columns. When set to `s`, it extracts the datetime values up to the seconds i. e. day, month, year, hour, minutes, seconds.
        
        You can set the granularity of the extraction manually by setting `extract_datetime`to `D` for day, `M` for month, `Y` for year, `h` for hour, `m` for minutes or to `False` if you want to skip this step. 
        
        ### outliers
        
        Defines how **outliers** in the data are handled. Outliers can be manipulated with two different methods: winsorization or deletion. You can specfiy the method by setting `outliers` to `winz` for winzorization, `delete`for deletion or to `False` if you want to skip this step.
        
        *When are outliers considered to be outliers?*  
        Oberservations are considered outliers if they are outside the following bounds:
        
        ````python
        [Q1 - 1.5*IQR , Q3 + 1.5*IQR]
        ````
        where  
        ... Q1 and Q3 are the first and third quartile of the feature values
        ... IQR is the interquartile range of the feature values
        
        As soon as a value is below the lower or upper bound, the chosen outlier handling method is applied i. e. either winsorization, meaning it will be replaced by the respective lower or upper bound, or the observation will be deleted.
        
        You can **customize** the outlier bounds by changing the default `outlier_param` value of `1.5` to any integer or float of your choice.
        
        ### outlier_param
        
        ! Recommended not to change default value
        
        You can **customize** the outlier bounds by changing the default `outlier_param` value of `1.5` to any integer or float of your choice.
        
        ### logfile
        
        Defines whether a logfile should be generated while the AutoClean process runs. If set to `True`, it will create a `autoclean.log` file in your current working directory.
        
        You can view a [sample logfile here](https://github.com/elisemercury/AutoClean/blob/main/AutoClean/autoclean.log).
        
        ### verbose
        
        Defined whether the logfile output should be shown on the console while the AutoClean process runs. Set to `True` if you want to follow the process logs in real-time.
Keywords: automated,cleaning,preprocessing,autoclean
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
