svy 0.12.0

End-to-end surveys: design, sample, analyze, report.

svy

Modern Python tools for complex survey analysis, built for real-world statistical workflows.

svy is a rigorously design-based yet production-oriented library for survey design, weighting, and estimation — without sacrificing transparency or scalability.

🌐 Website: svylab.com 📘 Documentation: svylab.com/docs/svy 📦 Source: github.com/samplics-org/svy

---

What is svy?

svy is designed for people who actually work with complex survey data, including national statistical offices, public health and development programs, survey methodologists, and data scientists working with complex samples.

Correct inference first — without hiding assumptions or sacrificing usability.

Validation

svy has been validated against R's survey package, producing numerically identical results across Taylor linearization, replication methods, and complex survey designs. See the full comparison.

---

Installation

pip install svy

---

Quick Start

import svy

# Load example data
hld_data = svy.load_dataset(name="hld_sample_wb_2023")

# Define the survey design
hld_design = svy.Design(stratum=("geo1", "urbrur"), psu="ea", wgt="hhweight")

# Create a sample object
hld_sample = svy.Sample(data=hld_data, design=hld_design)

# Estimate the mean of total expenditure
tot_exp_mean = hld_sample.estimation.mean(y="tot_exp")

print(tot_exp_mean)

---

Capabilities

• Complex survey design — strata, clusters, weights
• Design-based estimation with valid standard errors
• Replication methods — BRR, bootstrap, jackknife, SDR
• Categorical data analysis — tabulation, crosstabulation, t-test, Rao-Scott test
• Generalized linear models — logistic, Poisson, Gamma with survey weights
• Explicit, inspectable, reproducible outputs
• Built on Polars, NumPy, SciPy, and Rust

---

Related Packages

Package	Purpose	Install
svy	Core survey design & estimation	pip install svy
svy-sae	Small Area Estimation	pip install svy-sae
svy-io	SPSS / Stata / SAS I/O	pip install svy-io

---

Documentation

Full documentation, tutorials, and methodological notes: 👉 svylab.com/docs/svy

---

Feedback

• Issues: github.com/samplics-org/svy/issues
• Discussions: github.com/samplics-org/svy/discussions

---

License

MIT License — Copyright © 2026 Samplics LLC