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svy

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

svy is a rigorously design-based, production-oriented library for survey design, weighting, and estimation.

🌐 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 (to at least six significant digits) across Taylor linearization, replication methods, and complex survey designs — except where svy adopts a different, justifiable adjustment by design, most of which align with the conventions used in established software such as Stata. See the full comparison.


Installation

pip install svy # svy[report] for rich outputs

or

uv add svy

Quick Start

import svy

# Load example data
hld_data = svy.datasets.load("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 msgspec

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


License

MIT License — Copyright © 2026 Samplics LLC

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