IDCeMPy is a Python package that provides functions to fit and assess the performance of the following distinct sets of “inflated” discrete choice models: Fit the Zero-Inflated Ordered Probit (ZIOP) model without and with correlated errors (ZIOPC model) to evaluate zero-inflated ordered choice outcomes that result from a dual data generating process (d.g.p.). Fit the Middle-Inflated Ordered Probit (MIOP) model without and with correlated errors (MIOPC) to account for the inflated middle-category in ordered choice measures related to a dual d.g.p. Fit Generalized Inflated Multinomial Logit (GIMNL) models account for the predominant and heterogeneous share of observations in the baseline or any lower category in unordered polytomous choice outcomes. Compute AIC and Log-likelihood statistics and the Vuong Test statistic to assess the performance of each inflated discrete choice model in the package. IDCeMPy uses Newton numerical optimization methods to estimate the inflated discrete choice models listed above via Maximum Likelihood Estimation (MLE).