EBasic EDA functions implemented
Project description
eda_mds: Simplified Exploratory Data Analysis
Basic EDA functions implemented to improve on core Pandas DataFrame functions.
Installation
This project has not yet been uploaded to PyPI. Please see contributing for instructions to install locally.
Summary
This package is created for kick-starting the EDA stage of a machine learning and analytics project.
It's primary objective is to improve upon the popular EDA functions present in pandas package.
There are four functions that deliver insights and identify potential problems in the dataset.
Function Descriptions
cor_eda: This function accepts a dataset and isolates its numerical continuous variables. It calculates the correlation between each numerically continuous variable from scratch and displays the results in a table.info_na: This function replicates and extends behaviour of pandas.DataFrame.info. Additional information about null values in rows and columns is included.cat_var_stats: This function creates summary statistics about categorical variables in the dataframe. Number of unique values, frequency of values, and suggested category binning is included.describe_outliers: This function extends the functionality of pandas.Dataframe.describe for numeric data by providing a count of lower-tail and upper-tail outliers for a given threshold.
Python Ecosystem Integration
Our functions are heavily inspired from pandas package for python.
EDA functions such as pandas.Dataframe.info, pandas.Dataframe.describe and pandas.Dataframe.corr are recreated and improved upon in this package.
Our functions also depend on the pandas.Dataframe object.
Usage
Installation
This project has not yet been uploaded to PyPI. Please see contributing for instructions to install locally.
Function Usage
Each function takes a pandas.DataFrame object.
Please see the included vingette for intended use.
Contributing
Package created by Koray Tecimer, Paolo De Lagrave-Codina, Nicole Bidwell, Simon Frew.
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.
License
eda_mds was created by Koray Tecimer, Paolo De Lagrave-Codina, Nicole Bidwell, Simon Frew. It is licensed under the
terms of the MIT license.
Credits
eda_mds was created with cookiecutter and
the py-pkgs-cookiecutter template.
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