huda 0.1.4

HuDa — Humanitarian Data Library utilities for opening, cleaning, transforming, validating, geospatial, analysis, visualization, automation, and interoperability.

HuDa — Humanitarian Data Library

HuDa is a practical Python library for humanitarian data workflows. It provides simple, consistent functions to open, clean, transform, validate, analyze, map, visualize, automate, and share humanitarian datasets.

• Focused on survey, 5W, monitoring, and geo-enabled data
• Consistent API patterns across modules
• Returns lightweight specs for rendering/exports where appropriate

Features

• Opening: CSV/Excel/JSON/SQL/API connectors
• Cleaning: normalize numbers/dates/text, translate categories, deduplicate, geocode
• Transformation: reshape, aggregate, indexes, ratios, growth, standardization
• Validation & Quality: ranges, missing/mandatory, country codes, dates, profiling
• Geospatial: folium maps, choropleths, overlays, heatmaps, clusters
• Analysis: correlation, time series, regression, PCA, coverage gaps (selected utilities)
• Visualization: chart specs for bar/line/pie/hist/box/heatmap, dashboards
• Automation: reports, snapshots, change detection (specs)
• Interoperability: export specs (CSV/Excel/JSON/Parquet/SQL/Stata/SPSS/GIS/HDX/HTML/API)

Installation

HuDa is published on PyPI as huda.

pip install huda

Minimum Python version: 3.8

Some modules rely on optional libraries (e.g., folium, geopandas, scikit-learn). See Requirements below if you plan to use those features.

Quickstart

import polars as pl
from huda.cleaning import translate_categories
from huda.transformation import percentage_calculation
from huda.Interoperability import export_csv

# Example data
df = pl.DataFrame({
    "province": ["Kabul", "Herat"],
    "cluster": ["wash", "wash"],
    "reached": [1200, 900],
    "target": [2000, 1100],
})

# Cleaning
df2 = translate_categories(df, columns={"cluster": {"wash": "WASH"}})

# Transformation
df3 = percentage_calculation(df2, numerator_col="reached", denominator_col="target", output_col="coverage_pct")

# Interoperability (returns intent spec; does not write files)
spec = export_csv(df3, path="/tmp/coverage.csv")
print(spec)

Module Highlights

Opening

from huda.opening import open_csv, open_excel, open_json
df = open_csv("/path/data.csv")

Cleaning

from huda.cleaning import numbers_standardization, dates_standardization, duplicate
df = numbers_standardization(df, columns=["reached"])  # normalize numeric fields
df = dates_standardization(df, column="report_date", style="iso")
df = duplicate(df, columns=["id"], keep="first")

Transformation

from huda.transformation import pivot_unpivot, severity_index_calculation
df_wide = pivot_unpivot(df, mode="pivot", index=["province"], columns="cluster", values="reached")
df_idx = severity_index_calculation(df, components=["fcs","rcsi"], weights={"fcs":0.6,"rcsi":0.4})

Validation & Quality

from huda.validation_and_quality import country_code_validation, automatic_data_profiling_report
report = automatic_data_profiling_report(df)
valid = country_code_validation(df, data_col="country")

Geospatial

from huda.geospatial import choropleth_maps_by_region
html_map = choropleth_maps_by_region(df, region_col="province", value_col="reached", geojson_path="/path/afg_provinces.geojson")
with open("map.html", "w", encoding="utf-8") as f:
    f.write(html_map)

Visualization (specs)

from huda.visualize import bar_chart, interactive_dashboard
chart = bar_chart(df, category_col="province", value_col="reached")
dashboard = interactive_dashboard(charts=[chart])

Interoperability (specs)

These functions return intent specs you can pass to renderers/uploaders.

from huda.Interoperability import (
    export_csv, export_excel, export_json, export_parquet,
    export_sql_database, export_stata, export_spss,
    export_shapefile, export_geojson, export_hdx_dataset,
    share_dashboard_html, api_integration_output,
)

spec_csv = export_csv(df, path="/tmp/data.csv")
spec_sql = export_sql_database(df, connection_uri="postgresql://user:pass@host:5432/db", table_name="huda_export")
spec_geo = export_geojson(df, path="/tmp/data.geojson", geometry_col="geom")
spec_dash = share_dashboard_html(dashboard, path="/tmp/dashboard.html", embed_assets=True)

Requirements

Core requirements and optional dependencies are specified in requirements.txt.

If you plan to use geospatial and mapping utilities, you’ll need packages like folium and geopandas (which may require system libraries on some platforms). For ML utilities (e.g., outlier isolation), you’ll need scikit-learn.

Development

python -m venv .venv
. .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Run a quick sanity check:

python -c "import huda, polars as pl; print('HuDa OK')"

Building & Publishing (maintainers)

HuDa uses PEP 517/518 builds via Hatchling (configured in pyproject.toml).

python -m pip install --upgrade build twine
python -m build
# TestPyPI upload
twine upload --repository testpypi dist/*
# PyPI upload
twine upload dist/*

Contributing

Contributions are welcome. Please open an issue to discuss improvements or new utilities aligned with humanitarian workflows.

License

MIT License. See pyproject.toml and add a LICENSE file for full text.

Links

• Repository: https://github.com/fiafghan/HuDa
• Issues: https://github.com/fiafghan/HuDa/issues
• Training website: in huda_website/ (React + Tailwind; run with Vite)