Mankey Stats

Mankey_stats is a Python library that allows the user to quickly and efficiently perform data preparation techniques to transform the datasets for ML modeling, this is done through the utilization of several transformation and statistical analysis methods.

Documentation

• Documentation Generated through sphinx and available in the html_documentation zip file

Primary functionality include:

• Detailed analysis of features, including numerical distibution tests
• Analysis and handling of outliers and missing data
• Interactive plotting and data visualization functionality
• Transformation options including One hot encoding, ordinal transformations, and weight of evidence
• Functionality to prepare date fields for ML models
• Ability to examine and recommend changes without modifying the underlying data
• Optimized logic to ensure fast execution times, using numpy, scipy, and vectorization techniques

Analysis of features:

• Feature Normality test
• Grubb's test and Tucky's fences for handling outliers (based on stat. distribution)
• Missing value analysis (% and best method to handle - mode/median/or mean)
• Best scaling methods are selected for each numeric feature (min-max scaler or standard scaler)

Multiple methods to handle categorical features:

• One Hot encoder
• Ordinal encoder
• Weight of Evidence transformations

Date manipulation

• Ability to expand date fields to YEAR, MONTH, and/or DAY fields
• Subtract date features to create a "due in days" field

Installation

The library is published in the PyPi repository, it can be installed with pip:

pip install mankey_stats

Feel free to help us improve, simply clone it from this github and submit your features :)

git clone https://github.com/mankey_stats/mankey_stats.git

Dependencies:

We rely on the proven ML libraries: pandas, Seabor, plotly, numpy, scipy, and Scikit Learn

Example Usage

>>> import pandas as pd
>>> from mankey_stats.ordinal_encoder as ordinal_encoder

>>> data = {'type':  ['bad', 'average', 'good', 'very good', 'excellent'],
            'level': [1, 2, 3, 4, 5]
            
>>> levels_dict = {'type':  ['bad', 'average', 'good', 'very good', 'excellent'],
                   }
                   
>>> data = pd.DataFrame(data)
>>> print(data)

Out[1]:
type    level
bad       1
average   2
good      3
very good 4
excellent 5  
Name: var_A, dtype: int64

    t_ord = transformers.Ordinal_Transformer()
    t_ord.fit( levels_dict, df,None)

    df = t_ord.transform(df, None)

Out[2]:
0       1
1       2
2       3
3       4
4       5
Name: var_A, dtype: int64

• Documentation Generated through sphinx and available in the html_documentation zip file

Documentation

mankey-stats documentation is built using Sphinx and is hosted on Read the Docs - although we recommend html_documentation zip file while we fix some integrations issues :D.

You can re-build the docs using build html

License

MIT