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Copulas

Copulas is a Python library that models multivariate distributions using copula functions and samples synthetic data from numerical tables.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Copulas — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Copulas when a Python workflow needs copula-based modeling and sampling for numerical tabular data, with visual comparison of real and synthetic outputs.

Who it is for

  • Data scientists modeling dependencies across numerical columns.
  • Developers exploring synthetic tabular-data generation in Python.

Documented uses

  • Fit a multivariate model to numerical data.
  • Generate synthetic rows from the learned model.
  • Compare real and synthetic data through plots.
  • Inspect and tune learned model parameters.
Before you choose

Requirements & limits

  • The repository documents installation through pip or conda.
  • The quickstart uses numerical data and demonstrates a three-column sample dataset.
  • Copulas is a standalone library; the project separately points to SDV as an integrated synthetic-data solution.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Fit a copula model and sample synthetic data

Use the bundled three-column numerical demo dataset to fit a Gaussian multivariate model and generate synthetic rows.

Before you start

  • Python environment with Copulas installed using pip or conda.
  • The library's bundled sample_trivariate_xyz demo dataset.

Steps

  1. Install the library with the command below or conda install -c conda-forge copulas.

    Documented command
    pip install copulas

  2. Load sample_trivariate_xyz from copulas.datasets to obtain the demo table.

  3. Create a GaussianMultivariate model and fit it to the table.

  4. Call sample with the table's row count to generate a synthetic dataset.

Expected outcome

A synthetic dataset sampled from a Gaussian multivariate copula model fitted to the demo data.

Guide limitations

  • The demonstrated dataset has three numerical columns.
  • The recipe describes the Gaussian model; the repository also names Vine and Archimedean copulas.
Documentation 1 ↗
Source-based practical guide

Compare real and synthetic data visually

Create the documented 3D side-by-side comparison after generating synthetic data.

Before you start

  • Copulas installed in a Python environment.
  • A real dataset and a synthetic dataset, such as the outputs of the repository's demo workflow.

Steps

  1. Import compare_3d from copulas.visualization.

  2. Pass the real and synthetic datasets to compare_3d.

Expected outcome

A 3D visual comparison of the real and synthetic datasets.

Guide limitations

  • The repository also lists 1D histograms and 2D scatterplots as available visualization forms; this recipe documents only the 3D comparison call.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
sdv-dev/Copulas · Official homepage title · retained in its source language
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