jcds is a modular Python library (hosted in the jcds-lib repository) designed to support reproducible workflows in data science and exploratory data analysis (EDA).
It provides a curated collection of functions for inspecting, transforming, and accessing tabular data from local and cloud sources, with particular emphasis on usability within Jupyter notebooks.

📚 This project originated during my time as a graduate student in the MSADS (Master of Science in Applied Data Science) program at the University of San Diego.
I often ran into repetitive tasks — inspecting nulls, handling encodings, wrangling column names, or working with messy CSVs and S3-hosted files — across multiple class and capstone projects.
To address these real-world pain points, I began building jcds as a personal toolkit grounded in DRY (Don't Repeat Yourself) principles — and have been pair programming alongside Generative AI 🤖 to refine and expand it throughout my learning journey.

✅ Compatible with Python 3.7 and above. Developed and tested on Python 3.10.

pip install git+https://github.com/junclemente/jcds.git

pip install git+https://github.com/junclemente/jcds.git@v0.2.1

pip install git+https://github.com/junclemente/jcds.git@develop

pip install git+https://github.com/junclemente/jcds.git@v0.2.1[aws]

Installs:

• boto3
• botocore

Use httpimport directly in Jupyter:

import httpimport

with httpimport.github_repo('junclemente', 'jcds', ref='<branch>'):
    import jcds as jcds

You can also import specific submodules:

import jcds.eda as eda

import pandas as pd
import jcds.eda as jeda

df = pd.read_csv('data.csv')

See the EDA workflow notebook:

• examples/eda_workflow.ipynb

When specifying the ref in httpimport.github_repo():

• develop – Actively evolving 🚧
• 0.x.x – Stable and reproducible ✅

🔒 Recommended: Use a versioned tag for reproducibility.

Each subpackage has a built-in help() function.

import jcds
jcds.help()

import jcds.eda
jcds.eda.help()

import jcds.aws as jaws
jaws.help()

jcds.eda.help('dqr_cat')

This project uses pytest.

Run all tests:

pytest

Run a specific test file:

pytest tests/unit/test_eda_helpers.py

Measure test coverage:

pytest --cov=jcds --cov-report=term

📄 jcds documentation

Built with:

• MkDocs
• Material theme
• mkdocstrings

1. Add/update docstrings (Google or NumPy style).
2. Update mkdocs.yml and related .md files.
3. Preview locally:

mkdocs serve

URL:

http://127.0.0.1:8000/

4. Deploy to GitHub Pages:

mkdocs gh-deploy

See CHANGELOG.md for version history and updates.

This project follows Conventional Commits specifications:

Used for consistent history and release tracking.