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EquityStack

Python scripts and Jupyter notebooks for development sector data workflows.

Part of OpenStacks License: MIT

Plug-and-play templates for health, gender, education, and climate equity data — built for reproducibility.

What This Is

EquityStack is a collection of Python scripts, Jupyter notebooks, and sample data for development sector analysis. It provides ready-to-use utilities for data cleaning, validation, modelling, and visualisation — with a focus on public health, gender equity, education, and climate resilience workflows.

This is the data pipeline layer of OpenStacks for Change — an open ecosystem of tools for public interest research and evaluation.

What's Inside

Core Modules

Module What It Does Status
cleaning/ Column name standardisation, dtype conversion, missing value handling, outlier flagging, cleaning log generation Ready
impact_evaluation/ Causal inference: Difference-in-Differences, Propensity Score Matching, Regression Discontinuity Design Ready
validation/ Input validation with Pydantic models Ready
io_helpers/ Chunked CSV reading, Stata/SPSS import, formatted Excel export Ready
modelling/ Multicollinearity checks (VIF) Ready
visualisation/ Annotated bar charts, district-level choropleth maps Ready
social_sector/ Public health access index Ready

Notebooks

Notebook What It Does
sector_gender_summary.ipynb Gender-disaggregated analysis with sample data
sector_wee_time_use.ipynb Women's economic empowerment time-use analysis

Data and Testing

Directory What It Contains
sample_data/ Gender sample and time-use sample datasets
tests/ 10 pytest test files covering all core modules
scripts/ Standalone export utilities

Getting Started

Prerequisites

  • Python 3.8+
  • Jupyter (notebook or lab)

Installation

git clone https://github.com/Varnasr/EquityStack.git
cd EquityStack
pip install -r requirements.txt
jupyter notebook

Quick Start

  1. Open a notebook from notebooks/ to see a working analysis
  2. Load practice data from sample_data/
  3. Use cleaning/ to prepare your own data
  4. Apply modelling/ and visualisation/ for analysis and outputs
  5. Export with io_helpers/ for Excel or dashboard-ready formats

Key Dependencies

  • pandas, numpy — data manipulation
  • matplotlib, seaborn — visualisation
  • scikit-learn, statsmodels — modelling
  • openpyxl, xlsxwriter — Excel I/O
  • pydantic — data validation
  • geopandas, folium — spatial mapping

How It Connects

EquityStack is one of several stacks in the OpenStacks ecosystem:

Stack Focus
InsightStack MEL tools, calculators, documentation
FieldStack R notebooks for fieldwork and evaluation
EquityStack (this repo) Python workflows for development data
SignalStack Research Rundown newsletter archive

Use EquityStack when you work in Python/Jupyter. Use FieldStack for R-based equivalents. Use InsightStack for Stata tools and MEL calculators.

Contributing

Contributions welcome — especially from data practitioners in the development sector. See CONTRIBUTING.md.

High-impact areas:

  • EDA tools — correlation matrices, group summaries, data profiling
  • Modelling — logistic regression evaluation, OLS summary tables
  • Social sector — climate risk flags, education outcomes, gender disaggregation, WEE analysis
  • Notebooks — more worked examples with real analysis workflows
  • Visualisation — categorical distributions, regression diagnostics, time series

Citation

@software{equitystack,
  author = {Sri Raman, Varna},
  title = {EquityStack: Python Workflows for Development Data},
  url = {https://github.com/Varnasr/EquityStack}
}

License

MIT — free to use, modify, and share. See LICENSE.

Part of OpenStacks for Change. Created by Varna Sri Raman.