snapedautility

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snapedautility is an open-source library that generate useful function to kickstart EDA (Exploratory Data Analysis) with just a few lines of code. The system is built around quickly analyzing the whole dataset and providing a detailed report with visualization. Its goal is to help quick analysis of feature characteristics, detecting outliers from the observations and other such data characterization tasks.

Features

1. plot_histograms: Plots the distribution for numerical, categorical and text features
2. detect_outliers: Generate a violin plot that indicates the outliers that deviate from other observations on data.
3. plot_corr: Generates Correlation Plots for numerical (Pearson's correlation), categorical (uncertainty coefficient) and categorical-numerical (correlation ratio) datatypes seamlessly for all data types.

Installation

$ pip install snapedautility

Usage

plot_histograms

>>> from snapedautility.plot_histograms import plot_histograms
>>> df = penguins_data
>>> plot_histograms(df, ["Culmen Length (mm)", "Culmen Depth (mm)", 'Species'], 100, 100)

plot_corr

>>> from snapedautility.plot_corr import plot_corr
>>> df = penguins_data
>>> plot_corr(df, ["Culmen Length (mm)", "Culmen Depth (mm)", 'Species'])

detect_outliers

>>> from snapedautility.detect_outliers import detect_outliers 
>>> s = pd.Series([1,1,2,3,4,5,6,9,10,13,40])
>>> detect_outliers(s)

Documentation

The official documentation is hosted on Read the Docs: https://snapedautility.readthedocs.io/

Contributors

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

Similar Work

We recognize EDA (exploratory data analysis) and preprocessing packages are common in the Python open source ecosystem. Our package aims to do a few things very well, and be light weight. A non exhaustive list of EDA helper packages in Python include:

• pandasprofiling
  • This was the original inspiration for this project. We would like to expand the functionalities on this project.
• sweetviz
  • This package produces very clean visuals detailing breakdowns in descriptive statistics and can do so with train/test sets for model building workflows.
• ExploriPy
  • This packages does the most common EDA tasks but also adds in the ability to do statistical testing using analysis of variance (ANOVA), Chi Square test of independence etc.

License

snapedautility was created by Kyle Ahn, Harry Chan and Dongxiao Li. It is licensed under the terms of the MIT license.

Credits

snapedautility was created with cookiecutter and the py-pkgs-cookiecutter template.