mds_2025_helper_functions

A package to streamline common code chunks executed by students in the UBC MDS program circa 2025.

Functions

• compare_model_scores() - a function that takes multiple models and returns a table of mean CV scores for each for easy comparison.
• perform_eda() - a function to perform exploratory data analysis on a dataset
• dataset_summary() - a function that generates a comprehensive summary of a dataset, including missing value statistics, feature counts, duplicate rows, and descriptive statistics.
• htv() - (Hypothesis Test Visualization) provide good plots for user's hypothesis test result, easier to understand what happend in test rather than just number.

Similar packages

• While this package extends cross-validation from scikit-learn, there are no known packages that provide CV score comparison similar to compare_model_scores(). The most similar is the summary_cv() function in the CrossPy package, which summarizes CV scores for a single model.

• While the ProfileReport class from the ydata-profiling package provides automated exploratory data analysis and reporting, there are no known packages that offer the same level of flexible, on-demand visualizations and insights as the perform_eda() function. The most similar functionality is available in pandas-profiling, which generates detailed HTML reports but lacks the modular, interactive approach that perform_eda() provides for tailoring EDA to specific datasets and workflows.

• The dataset_summary() function combines essential dataset insights—missing values, feature types, duplicates, and basic statistics—into one comprehensive and easy-to-use tool. While similar functionality exists in libraries like pandas-profiling and missingno, these tools focus on specific aspects or full-scale exploratory analysis. No single function consolidates all these features in one place, making dataset_summary() a uniquely efficient solution for preprocessing workflows.

• There is no similar function could provide plot for hypothesis test output. Data Scientist do it manually, but it is not friendly for learner.

Installation

$ pip install mds_2025_helper_functions

Usage

• TODO

Contributing

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.

Contributors

Karlygash Zhakupbayeva, Samuel Adetsi, Xi Cu, Michael Hewlett

License

mds_2025_helper_functions was created by Karlygash Zhakupbayeva, Samuel Adetsi, Xi Cu, Michael Hewlett. It is licensed under the terms of the MIT license.

Credits

mds_2025_helper_functions was created with cookiecutter and the py-pkgs-cookiecutter template.