  This article introduces the Python package gcimpute for missing data
imputation. gcimpute can impute missing data with many different variable
types, including continuous, binary, ordinal, count, and truncated values, by
modeling data as samples from a Gaussian copula model. This semiparametric
model learns the marginal distribution of each variable to match the empirical
distribution, yet describes the interactions between variables with a joint
Gaussian that enables fast inference, imputation with confidence intervals, and
multiple imputation. The package also provides specialized extensions to handle
large datasets (with complexity linear in the number of observations) and
streaming datasets (with online imputation). This article describes the
underlying methodology and demonstrates how to use the software package.
