Package: eda-mds
eda_mds: Simplified Exploratory Data AnalysisBasic EDA functions implemented to improve on core Pandas DataFrame functions.SummaryThis 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 inpandaspackage.
There are four functions that deliver insights and identify potential problems in the dataset.Function Descriptionscor_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 ofpandas.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 ofpandas.Dataframe.describefor numeric data by providing a count of lower-tail and upper-tail outliers for a given threshold.Python Ecosystem IntegrationOur functions are heavily inspired frompandaspackage for python.
EDA functions such aspandas.Dataframe.info,pandas.Dataframe.describeandpandas.Dataframe.corrare recreated and improved upon in this package.
Our functions also depend on thepandas.Dataframeobject.InstallationUser SetupThis package can be installed via PyPi by running the following command in your terminal.$pipinstalleda_mdsDeveloper SetupHere's how to installeda_mdsfor local development.Clone a copy ofeda_mdslocally, by running the following command in your terminal.$gitclonehttps://github.com/UBC-MDS/eda_mds.gitCreate/activate newcondaenvironment and install poetry.$condacreate-neda_mds_devpython=3.9poetry$condaactivateeda_mds_devNavigate to the root directory.$cdpath/to/eda_mdsInstalleda_mdsusingpoetry.$poetryinstallRunning the Tests and CoverageTo run the tests navigate to the root directory.$cdpath/to/eda_mdsTo run the tests navigate to the root directory.$pytestTo run the coverage report.$coveragereportUsageFunction UsageBelow provides a short depiction on how to start using the functions in this package, after you have completed the installation. Please see thevingettefor detailed usage. Note: Each function takes in apandas.DataFrameobject.Import the functions andpandas.fromeda_mdsimportinfo_na,describe_outliers,cat_var_stats,cor_edaimportpandasaspdLoad your dataset of choice.df=pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv')Begin using the functions!info_na(df)describe_outliers(df)cat_var_stats(df)cor_eda(df)ContributingPackage created by Koray Tecimer, Paolo De Lagrave-Codina, Nicole Bidwell, Simon Frew.Interested in contributing? Check out thecontributing 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.Licenseeda_mdswas created by Koray Tecimer, Paolo De Lagrave-Codina, Nicole Bidwell, Simon Frew.
Code is licensed under the terms of the MIT license.
Non-code portions, specifically vignettes and related documentation, is licensed under the terms of the Creative Commons Zero v1.0 Universal license.Creditseda_mdswas created withcookiecutterand
thepy-pkgs-cookiecuttertemplate.