🕷️ Crawler Inspector

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Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 140 (from laksa127)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa140.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html","crawl_time":1776369112,"first_indexed_time":1526806359,"http_code":200,"src_unparsed":"org,statsmodels!www,\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html s443","src_root_hash":"260885690736431140","history_drop_reason":null,"meta_title":"statsmodels.discrete.count_model.ZeroInflatedPoisson - statsmodels 0.15.0 (+989)","meta_descriptions":[],"attrs_boilerpipe_text":"cdf\n(X)\nThe cumulative distribution function of the model.\ncov_params_func_l1\n(likelihood_model, xopt, ...)\nComputes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.\nfit\n([start_params, method, maxiter, ...])\nFit the model using maximum likelihood.\nfit_regularized\n([start_params, method, ...])\nFit the model using a regularized maximum likelihood.\nfrom_formula\n(formula, data[, subset, drop_cols])\nCreate a Model from a formula and dataframe.\nget_distribution\n(params[, exog, exog_infl, ...])\nGet frozen instance of distribution based on predicted parameters.\nhessian\n(params)\nGeneric Zero Inflated model Hessian matrix of the loglikelihood\ninformation\n(params)\nFisher information matrix of model.\ninitialize\n()\nInitialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.\nloglike\n(params)\nLoglikelihood of Generic Zero Inflated model.\nloglikeobs\n(params)\nLoglikelihood for observations of Generic Zero Inflated model.\npdf\n(X)\nThe probability density (mass) function of the model.\npredict\n(params[, exog, exog_infl, exposure, ...])\nPredict expected response or other statistic given exogenous variables.\nscore\n(params)\nScore vector of model.\nscore_obs\n(params)\nGeneric Zero Inflated model score (gradient) vector of the log-likelihood","attrs_markdown":"[Skip to content](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson)\n\n[![logo](https:\/\/www.statsmodels.org\/dev\/_static\/statsmodels-logo-v2-bw.svg)](https:\/\/www.statsmodels.org\/dev\/index.html \"statsmodels 0.15.0 (+989)\")\n\nstatsmodels 0.15.0 (+989)\n\nstatsmodels.discrete.count\\_model.ZeroInflatedPoisson\n\nInitializing search\n\n[statsmodels](https:\/\/github.com\/statsmodels\/statsmodels\/ \"Go to repository\")\n\n[![logo](https:\/\/www.statsmodels.org\/dev\/_static\/statsmodels-logo-v2-bw.svg)](https:\/\/www.statsmodels.org\/dev\/index.html \"statsmodels 0.15.0 (+989)\")  statsmodels 0.15.0 (+989)\n\n[statsmodels](https:\/\/github.com\/statsmodels\/statsmodels\/ \"Go to repository\")\n\n- [Installing statsmodels](https:\/\/www.statsmodels.org\/dev\/install.html)\n- [Getting started](https:\/\/www.statsmodels.org\/dev\/gettingstarted.html)\n- [User Guide](https:\/\/www.statsmodels.org\/dev\/user-guide.html)\n  User Guide\n  \n  - [Background](https:\/\/www.statsmodels.org\/dev\/user-guide.html#background)\n  - [Regression and Linear Models](https:\/\/www.statsmodels.org\/dev\/user-guide.html#regression-and-linear-models)\n    Regression and Linear Models\n    \n    - [Linear Regression](https:\/\/www.statsmodels.org\/dev\/regression.html)\n    - [Generalized Linear Models](https:\/\/www.statsmodels.org\/dev\/glm.html)\n    - [Generalized Estimating Equations](https:\/\/www.statsmodels.org\/dev\/gee.html)\n    - [Generalized Additive Models (GAM)](https:\/\/www.statsmodels.org\/dev\/gam.html)\n    - [Robust Linear Models](https:\/\/www.statsmodels.org\/dev\/rlm.html)\n    - [Linear Mixed Effects Models](https:\/\/www.statsmodels.org\/dev\/mixed_linear.html)\n    - [Regression with Discrete Dependent Variable](https:\/\/www.statsmodels.org\/dev\/discretemod.html)\n      Regression with Discrete Dependent Variable\n      \n      - [Module Reference](https:\/\/www.statsmodels.org\/dev\/discretemod.html#module-statsmodels.discrete.discrete_model)\n        Module Reference\n        \n        - [statsmodels. discrete. discrete\\_ model. Logit](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.Logit.html)\n        - [statsmodels. discrete. discrete\\_ model. Probit](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.Probit.html)\n        - [statsmodels. discrete. discrete\\_ model. MNLogit](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.MNLogit.html)\n        - [statsmodels. discrete. discrete\\_ model. Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.Poisson.html)\n        - [statsmodels. discrete. discrete\\_ model. Negative Binomial](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.NegativeBinomial.html)\n        - [statsmodels. discrete. discrete\\_ model. Negative Binomial P](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.NegativeBinomialP.html)\n        - [statsmodels. discrete. discrete\\_ model. Generalized Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.GeneralizedPoisson.html)\n        - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html)\n          statsmodels.\n          \n          discrete.\n          \n          count\\_\n          \n          model.\n          \n          Zero\n          \n          Inflated\n          \n          Poisson\n          \n          - [C statsmodels. discrete. count\\_ model. Zero Inflated Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson)\n            statsmodels.\n            \n            discrete.\n            \n            count\\_\n            \n            model.\n            \n            Zero\n            \n            Inflated\n            \n            Poisson\n            \n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. cdf](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. cov\\_ params\\_ func\\_ l1](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. fit](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. fit\\_ regularized](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. from\\_ formula](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. get\\_ distribution](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. hessian](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. information](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.information.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. initialize](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. loglike](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. loglikeobs](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. pdf](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. predict](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.predict.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. score](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.score.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. score\\_ obs](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. endog\\_ names](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. exog\\_ names](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names.html)\n        - statsmodels.\n          \n          discrete.\n          \n          count\\_\n          \n          model.\n          \n          Zero\n          \n          Inflated\n          \n          Poisson\n          [statsmodels. discrete. count\\_ model. Zero Inflated Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html)\n          Contents\n          \n          - [C statsmodels. discrete. count\\_ model. Zero Inflated Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson)\n            - [Parameters](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-parameters)\n            - [Attributes](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-attributes)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. cdf](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. cov\\_ params\\_ func\\_ l1](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. fit](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. fit\\_ regularized](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. from\\_ formula](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. get\\_ distribution](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. hessian](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. information](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.information.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. initialize](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. loglike](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. loglikeobs](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. pdf](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. predict](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.predict.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. score](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.score.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. score\\_ obs](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. endog\\_ names](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names.html)\n            - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson. exog\\_ names](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names.html)\n        - [statsmodels. discrete. count\\_ model. Zero Inflated Negative Binomial P](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialP.html)\n        - [statsmodels. discrete. count\\_ model. Zero Inflated Generalized Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoisson.html)\n        - [statsmodels. discrete. truncated\\_ model. Hurdle Count Model](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.truncated_model.HurdleCountModel.html)\n        - [statsmodels. discrete. truncated\\_ model. Truncated LFNegative Binomial P](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.truncated_model.TruncatedLFNegativeBinomialP.html)\n        - [statsmodels. discrete. truncated\\_ model. Truncated LFPoisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.truncated_model.TruncatedLFPoisson.html)\n        - [statsmodels. discrete. conditional\\_ models. Conditional Logit](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.conditional_models.ConditionalLogit.html)\n        - [statsmodels. discrete. conditional\\_ models. Conditional MNLogit](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.conditional_models.ConditionalMNLogit.html)\n        - [statsmodels. discrete. conditional\\_ models. Conditional Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.conditional_models.ConditionalPoisson.html)\n        - [statsmodels. miscmodels. ordinal\\_ model. Ordered Model](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.miscmodels.ordinal_model.OrderedModel.html)\n        - [statsmodels. discrete. discrete\\_ model. Logit Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.LogitResults.html)\n        - [statsmodels. discrete. discrete\\_ model. Probit Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.ProbitResults.html)\n        - [statsmodels. discrete. discrete\\_ model. Count Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.CountResults.html)\n        - [statsmodels. discrete. discrete\\_ model. Multinomial Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.MultinomialResults.html)\n        - [statsmodels. discrete. discrete\\_ model. Negative Binomial Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.NegativeBinomialResults.html)\n        - [statsmodels. discrete. discrete\\_ model. Generalized Poisson Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.GeneralizedPoissonResults.html)\n        - [statsmodels. discrete. count\\_ model. Zero Inflated Poisson Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoissonResults.html)\n        - [statsmodels. discrete. count\\_ model. Zero Inflated Negative Binomial Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults.html)\n        - [statsmodels. discrete. count\\_ model. Zero Inflated Generalized Poisson Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults.html)\n        - [statsmodels. discrete. truncated\\_ model. Hurdle Count Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.truncated_model.HurdleCountResults.html)\n        - [statsmodels. discrete. truncated\\_ model. Truncated LFPoisson Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.truncated_model.TruncatedLFPoissonResults.html)\n        - [statsmodels. discrete. truncated\\_ model. Truncated Negative Binomial Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.truncated_model.TruncatedNegativeBinomialResults.html)\n        - [statsmodels. discrete. conditional\\_ models. Conditional Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.conditional_models.ConditionalResults.html)\n        - [statsmodels. miscmodels. ordinal\\_ model. Ordered Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.miscmodels.ordinal_model.OrderedResults.html)\n        - [statsmodels. discrete. discrete\\_ model. Discrete Model](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.DiscreteModel.html)\n        - [statsmodels. discrete. discrete\\_ model. Discrete Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.DiscreteResults.html)\n        - [statsmodels. discrete. discrete\\_ model. Binary Model](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.BinaryModel.html)\n        - [statsmodels. discrete. discrete\\_ model. Binary Results](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.BinaryResults.html)\n        - [statsmodels. discrete. discrete\\_ model. Count Model](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.CountModel.html)\n        - [statsmodels. discrete. discrete\\_ model. Multinomial Model](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.discrete_model.MultinomialModel.html)\n        - [statsmodels. discrete. count\\_ model. Generic Zero Inflated](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.GenericZeroInflated.html)\n    - [Generalized Linear Mixed Effects Models](https:\/\/www.statsmodels.org\/dev\/mixed_glm.html)\n    - [ANOVA](https:\/\/www.statsmodels.org\/dev\/anova.html)\n    - [Other Models othermod](https:\/\/www.statsmodels.org\/dev\/other_models.html)\n  - [Time Series Analysis](https:\/\/www.statsmodels.org\/dev\/user-guide.html#time-series-analysis)\n  - [Other Models](https:\/\/www.statsmodels.org\/dev\/user-guide.html#other-models)\n  - [Statistics and Tools](https:\/\/www.statsmodels.org\/dev\/user-guide.html#statistics-and-tools)\n  - [Data Sets](https:\/\/www.statsmodels.org\/dev\/user-guide.html#data-sets)\n  - [Sandbox](https:\/\/www.statsmodels.org\/dev\/user-guide.html#sandbox)\n- [Examples](https:\/\/www.statsmodels.org\/dev\/examples\/index.html)\n- [API Reference](https:\/\/www.statsmodels.org\/dev\/api.html)\n- [About statsmodels](https:\/\/www.statsmodels.org\/dev\/about.html)\n- [Developer Page](https:\/\/www.statsmodels.org\/dev\/dev\/index.html)\n- [Release Notes](https:\/\/www.statsmodels.org\/dev\/release\/index.html)\n\nContents\n\n- [C statsmodels. discrete. count\\_ model. Zero Inflated Poisson](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson)\n  - [Parameters](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-parameters)\n  - [Attributes](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-attributes)\n\n# statsmodels.discrete.count\\_model.ZeroInflatedPoisson[¶](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels-discrete-count-model-zeroinflatedpoisson \"Link to this heading\")\n*class* statsmodels.discrete.count\\_model.ZeroInflatedPoisson(*[endog](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.endog (Python parameter)\")*, *[exog](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.exog (Python parameter)\")*, *[exog\\_infl](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.exog_infl (Python parameter)\")\\=`None`*, *[offset](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.offset (Python parameter)\")\\=`None`*, *[exposure](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.exposure (Python parameter)\")\\=`None`*, *[inflation](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.inflation (Python parameter)\")\\=`'logit'`*, *[missing](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.missing (Python parameter)\")\\=`'none'`*, *\\*\\*[kwargs](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.kwargs (Python parameter)\")*)[\\[source\\]](https:\/\/www.statsmodels.org\/dev\/_modules\/statsmodels\/discrete\/count_model.html#ZeroInflatedPoisson)[¶](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson \"Link to this definition\")\n\nPoisson Zero Inflated Model\n\nParameters:[¶](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-parameters \"Permalink to this headline\")\n\n**endog**[array\\_like](https:\/\/numpy.org\/doc\/stable\/glossary.html#term-array_like \"(in NumPy v2.4)\")\n\nA 1-d endogenous response variable. The dependent variable.\n\n**exog**[array\\_like](https:\/\/numpy.org\/doc\/stable\/glossary.html#term-array_like \"(in NumPy v2.4)\")\n\nA nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See `statsmodels.tools.add_constant`.\n\n**exog\\_infl**[array\\_like](https:\/\/numpy.org\/doc\/stable\/glossary.html#term-array_like \"(in NumPy v2.4)\") or [`None`](https:\/\/docs.python.org\/3\/library\/constants.html#None \"(in Python v3.14)\")\n\nExplanatory variables for the binary inflation model, i.e. for mixing probability model. If None, then a constant is used.\n\n**offset**[array\\_like](https:\/\/numpy.org\/doc\/stable\/glossary.html#term-array_like \"(in NumPy v2.4)\")\n\nOffset is added to the linear prediction with coefficient equal to 1.\n\n**exposure**[array\\_like](https:\/\/numpy.org\/doc\/stable\/glossary.html#term-array_like \"(in NumPy v2.4)\")\n\nLog(exposure) is added to the linear prediction with coefficient equal to 1.\n\n**inflation**{‘logit’, ‘probit’}\n\nThe model for the zero inflation, either Logit (default) or Probit\n\n**missing**[`str`](https:\/\/docs.python.org\/3\/library\/stdtypes.html#str \"(in Python v3.14)\")\n\nAvailable options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.\n\nAttributes:[¶](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-attributes \"Permalink to this headline\")\n\n**endog**[`ndarray`](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.ndarray.html#numpy.ndarray \"(in NumPy v2.4)\")\n\nA reference to the endogenous response variable\n\n**exog**[`ndarray`](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.ndarray.html#numpy.ndarray \"(in NumPy v2.4)\")\n\nA reference to the exogenous design.\n\n**exog\\_infl**[`ndarray`](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.ndarray.html#numpy.ndarray \"(in NumPy v2.4)\")\n\nA reference to the zero-inflated exogenous design.\n\nMethods\n\n|   |   |\n|---|---|\n| [`cdf`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf \"statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf (Python method) — The cumulative distribution function of the model.\")(X) | The cumulative distribution function of the model. |\n| [`cov_params_func_l1`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1 \"statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1 (Python method) — Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.\")(likelihood\\_model, xopt, ...) | Computes cov\\_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. |\n| [`fit`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.fit \"statsmodels.discrete.count_model.ZeroInflatedPoisson.fit (Python method) — Fit the model using maximum likelihood.\")(\\[start\\_params, method, maxiter, ...\\]) | Fit the model using maximum likelihood. |\n| [`fit_regularized`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized \"statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized (Python method) — Fit the model using a regularized maximum likelihood.\")(\\[start\\_params, method, ...\\]) | Fit the model using a regularized maximum likelihood. |\n| [`from_formula`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula \"statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula (Python method) — Create a Model from a formula and dataframe.\")(formula, data\\[, subset, drop\\_cols\\]) | Create a Model from a formula and dataframe. |\n| [`get_distribution`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution \"statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution (Python method) — Get frozen instance of distribution based on predicted parameters.\")(params\\[, exog, exog\\_infl, ...\\]) | Get frozen instance of distribution based on predicted parameters. |\n| [`hessian`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian \"statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian (Python method) — Generic Zero Inflated model Hessian matrix of the loglikelihood\")(params) | Generic Zero Inflated model Hessian matrix of the loglikelihood |\n| [`information`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.information.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.information \"statsmodels.discrete.count_model.ZeroInflatedPoisson.information (Python method) — Fisher information matrix of model.\")(params) | Fisher information matrix of model. |\n| [`initialize`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize \"statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize (Python method) — Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.\")() | Initialize is called by statsmodels.model.LikelihoodModel.\\_\\_init\\_\\_ and should contain any preprocessing that needs to be done for a model. |\n| [`loglike`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike \"statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike (Python method) — Loglikelihood of Generic Zero Inflated model.\")(params) | Loglikelihood of Generic Zero Inflated model. |\n| [`loglikeobs`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs \"statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs (Python method) — Loglikelihood for observations of Generic Zero Inflated model.\")(params) | Loglikelihood for observations of Generic Zero Inflated model. |\n| [`pdf`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf \"statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf (Python method) — The probability density (mass) function of the model.\")(X) | The probability density (mass) function of the model. |\n| [`predict`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.predict.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.predict \"statsmodels.discrete.count_model.ZeroInflatedPoisson.predict (Python method) — Predict expected response or other statistic given exogenous variables.\")(params\\[, exog, exog\\_infl, exposure, ...\\]) | Predict expected response or other statistic given exogenous variables. |\n| [`score`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.score.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.score \"statsmodels.discrete.count_model.ZeroInflatedPoisson.score (Python method) — Score vector of model.\")(params) | Score vector of model. |\n| [`score_obs`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs \"statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs (Python method) — Generic Zero Inflated model score (gradient) vector of the log-likelihood\")(params) | Generic Zero Inflated model score (gradient) vector of the log-likelihood |\n\nProperties\n\n|   |   |\n|---|---|\n| [`endog_names`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names \"statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names (Python property) — Names of endogenous variables.\") | Names of endogenous variables. |\n| [`exog_names`](https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names \"statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names (Python property) — Names of exogenous variables.\") | Names of exogenous variables. |\n\nApr 16, 2026\n\n© Copyright 2009-2025, Josef Perktold, Skipper Seabold, Jonathan Taylor, statsmodels-developers.\n\nCreated using [Sphinx](https:\/\/www.sphinx-doc.org\/) 7.3.7. and [Sphinx-Immaterial](https:\/\/github.com\/jbms\/sphinx-immaterial\/)","attrs_readable_markdown":null,"meta_canonical":null}

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Boilerpipe Text	cdf (X) The cumulative distribution function of the model. cov_params_func_l1 (likelihood_model, xopt, ...) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. fit ([start_params, method, maxiter, ...]) Fit the model using maximum likelihood. fit_regularized ([start_params, method, ...]) Fit the model using a regularized maximum likelihood. from_formula (formula, data[, subset, drop_cols]) Create a Model from a formula and dataframe. get_distribution (params[, exog, exog_infl, ...]) Get frozen instance of distribution based on predicted parameters. hessian (params) Generic Zero Inflated model Hessian matrix of the loglikelihood information (params) Fisher information matrix of model. initialize () Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model. loglike (params) Loglikelihood of Generic Zero Inflated model. loglikeobs (params) Loglikelihood for observations of Generic Zero Inflated model. pdf (X) The probability density (mass) function of the model. predict (params[, exog, exog_infl, exposure, ...]) Predict expected response or other statistic given exogenous variables. score (params) Score vector of model. score_obs (params) Generic Zero Inflated model score (gradient) vector of the log-likelihood
Markdown	[Skip to content](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson) [![logo](https://www.statsmodels.org/dev/_static/statsmodels-logo-v2-bw.svg)](https://www.statsmodels.org/dev/index.html "statsmodels 0.15.0 (+989)") statsmodels 0.15.0 (+989) statsmodels.discrete.count\_model.ZeroInflatedPoisson Initializing search [statsmodels](https://github.com/statsmodels/statsmodels/ "Go to repository") [![logo](https://www.statsmodels.org/dev/_static/statsmodels-logo-v2-bw.svg)](https://www.statsmodels.org/dev/index.html "statsmodels 0.15.0 (+989)") statsmodels 0.15.0 (+989) [statsmodels](https://github.com/statsmodels/statsmodels/ "Go to repository") - [Installing statsmodels](https://www.statsmodels.org/dev/install.html) - [Getting started](https://www.statsmodels.org/dev/gettingstarted.html) - [User Guide](https://www.statsmodels.org/dev/user-guide.html) User Guide - [Background](https://www.statsmodels.org/dev/user-guide.html#background) - [Regression and Linear Models](https://www.statsmodels.org/dev/user-guide.html#regression-and-linear-models) Regression and Linear Models - [Linear Regression](https://www.statsmodels.org/dev/regression.html) - [Generalized Linear Models](https://www.statsmodels.org/dev/glm.html) - [Generalized Estimating Equations](https://www.statsmodels.org/dev/gee.html) - [Generalized Additive Models (GAM)](https://www.statsmodels.org/dev/gam.html) - [Robust Linear Models](https://www.statsmodels.org/dev/rlm.html) - [Linear Mixed Effects Models](https://www.statsmodels.org/dev/mixed_linear.html) - [Regression with Discrete Dependent Variable](https://www.statsmodels.org/dev/discretemod.html) Regression with Discrete Dependent Variable - [Module Reference](https://www.statsmodels.org/dev/discretemod.html#module-statsmodels.discrete.discrete_model) Module Reference - [statsmodels. discrete. discrete\_ model. Logit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Logit.html) - [statsmodels. discrete. discrete\_ model. Probit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Probit.html) - [statsmodels. discrete. discrete\_ model. MNLogit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.MNLogit.html) - [statsmodels. discrete. discrete\_ model. Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Poisson.html) - [statsmodels. discrete. discrete\_ model. Negative Binomial](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.NegativeBinomial.html) - [statsmodels. discrete. discrete\_ model. Negative Binomial P](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.NegativeBinomialP.html) - [statsmodels. discrete. discrete\_ model. Generalized Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.GeneralizedPoisson.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html) statsmodels. discrete. count\_ model. Zero Inflated Poisson - [C statsmodels. discrete. count\_ model. Zero Inflated Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson) statsmodels. discrete. count\_ model. Zero Inflated Poisson - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. cdf](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. cov\_ params\_ func\_ l1](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. fit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. fit\_ regularized](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. from\_ formula](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. get\_ distribution](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. hessian](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. information](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.information.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. initialize](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. loglike](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. loglikeobs](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. pdf](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. predict](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.predict.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. score](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.score.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. score\_ obs](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. endog\_ names](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. exog\_ names](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names.html) - statsmodels. discrete. count\_ model. Zero Inflated Poisson [statsmodels. discrete. count\_ model. Zero Inflated Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html) Contents - [C statsmodels. discrete. count\_ model. Zero Inflated Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson) - [Parameters](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-parameters) - [Attributes](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-attributes) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. cdf](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. cov\_ params\_ func\_ l1](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. fit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. fit\_ regularized](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. from\_ formula](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. get\_ distribution](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. hessian](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. information](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.information.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. initialize](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. loglike](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. loglikeobs](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. pdf](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. predict](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.predict.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. score](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.score.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. score\_ obs](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. endog\_ names](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson. exog\_ names](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names.html) - [statsmodels. discrete. count\_ model. Zero Inflated Negative Binomial P](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialP.html) - [statsmodels. discrete. count\_ model. Zero Inflated Generalized Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoisson.html) - [statsmodels. discrete. truncated\_ model. Hurdle Count Model](https://www.statsmodels.org/dev/generated/statsmodels.discrete.truncated_model.HurdleCountModel.html) - [statsmodels. discrete. truncated\_ model. Truncated LFNegative Binomial P](https://www.statsmodels.org/dev/generated/statsmodels.discrete.truncated_model.TruncatedLFNegativeBinomialP.html) - [statsmodels. discrete. truncated\_ model. Truncated LFPoisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.truncated_model.TruncatedLFPoisson.html) - [statsmodels. discrete. conditional\_ models. Conditional Logit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.conditional_models.ConditionalLogit.html) - [statsmodels. discrete. conditional\_ models. Conditional MNLogit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.conditional_models.ConditionalMNLogit.html) - [statsmodels. discrete. conditional\_ models. Conditional Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.conditional_models.ConditionalPoisson.html) - [statsmodels. miscmodels. ordinal\_ model. Ordered Model](https://www.statsmodels.org/dev/generated/statsmodels.miscmodels.ordinal_model.OrderedModel.html) - [statsmodels. discrete. discrete\_ model. Logit Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.LogitResults.html) - [statsmodels. discrete. discrete\_ model. Probit Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.ProbitResults.html) - [statsmodels. discrete. discrete\_ model. Count Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.CountResults.html) - [statsmodels. discrete. discrete\_ model. Multinomial Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.MultinomialResults.html) - [statsmodels. discrete. discrete\_ model. Negative Binomial Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.NegativeBinomialResults.html) - [statsmodels. discrete. discrete\_ model. Generalized Poisson Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.GeneralizedPoissonResults.html) - [statsmodels. discrete. count\_ model. Zero Inflated Poisson Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoissonResults.html) - [statsmodels. discrete. count\_ model. Zero Inflated Negative Binomial Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults.html) - [statsmodels. discrete. count\_ model. Zero Inflated Generalized Poisson Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults.html) - [statsmodels. discrete. truncated\_ model. Hurdle Count Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.truncated_model.HurdleCountResults.html) - [statsmodels. discrete. truncated\_ model. Truncated LFPoisson Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.truncated_model.TruncatedLFPoissonResults.html) - [statsmodels. discrete. truncated\_ model. Truncated Negative Binomial Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.truncated_model.TruncatedNegativeBinomialResults.html) - [statsmodels. discrete. conditional\_ models. Conditional Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.conditional_models.ConditionalResults.html) - [statsmodels. miscmodels. ordinal\_ model. Ordered Results](https://www.statsmodels.org/dev/generated/statsmodels.miscmodels.ordinal_model.OrderedResults.html) - [statsmodels. discrete. discrete\_ model. Discrete Model](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.DiscreteModel.html) - [statsmodels. discrete. discrete\_ model. Discrete Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.DiscreteResults.html) - [statsmodels. discrete. discrete\_ model. Binary Model](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.BinaryModel.html) - [statsmodels. discrete. discrete\_ model. Binary Results](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.BinaryResults.html) - [statsmodels. discrete. discrete\_ model. Count Model](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.CountModel.html) - [statsmodels. discrete. discrete\_ model. Multinomial Model](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.MultinomialModel.html) - [statsmodels. discrete. count\_ model. Generic Zero Inflated](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.GenericZeroInflated.html) - [Generalized Linear Mixed Effects Models](https://www.statsmodels.org/dev/mixed_glm.html) - [ANOVA](https://www.statsmodels.org/dev/anova.html) - [Other Models othermod](https://www.statsmodels.org/dev/other_models.html) - [Time Series Analysis](https://www.statsmodels.org/dev/user-guide.html#time-series-analysis) - [Other Models](https://www.statsmodels.org/dev/user-guide.html#other-models) - [Statistics and Tools](https://www.statsmodels.org/dev/user-guide.html#statistics-and-tools) - [Data Sets](https://www.statsmodels.org/dev/user-guide.html#data-sets) - [Sandbox](https://www.statsmodels.org/dev/user-guide.html#sandbox) - [Examples](https://www.statsmodels.org/dev/examples/index.html) - [API Reference](https://www.statsmodels.org/dev/api.html) - [About statsmodels](https://www.statsmodels.org/dev/about.html) - [Developer Page](https://www.statsmodels.org/dev/dev/index.html) - [Release Notes](https://www.statsmodels.org/dev/release/index.html) Contents - [C statsmodels. discrete. count\_ model. Zero Inflated Poisson](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson) - [Parameters](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-parameters) - [Attributes](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-attributes) # statsmodels.discrete.count\_model.ZeroInflatedPoisson[¶](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels-discrete-count-model-zeroinflatedpoisson "Link to this heading") class statsmodels.discrete.count\_model.ZeroInflatedPoisson([endog](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.endog (Python parameter)"), [exog](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.exog (Python parameter)"), [exog\_infl](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.exog_infl (Python parameter)")\=`None`, [offset](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.offset (Python parameter)")\=`None`, [exposure](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.exposure (Python parameter)")\=`None`, [inflation](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.inflation (Python parameter)")\=`'logit'`, [missing](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.missing (Python parameter)")\=`'none'`, \\[kwargs](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "statsmodels.discrete.count_model.ZeroInflatedPoisson.__init__.kwargs (Python parameter)"))[\[source\]](https://www.statsmodels.org/dev/_modules/statsmodels/discrete/count_model.html#ZeroInflatedPoisson)[¶](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson "Link to this definition") Poisson Zero Inflated Model Parameters:[¶](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-parameters "Permalink to this headline") endog[array\_like](https://numpy.org/doc/stable/glossary.html#term-array_like "(in NumPy v2.4)") A 1-d endogenous response variable. The dependent variable. exog[array\_like](https://numpy.org/doc/stable/glossary.html#term-array_like "(in NumPy v2.4)") A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See `statsmodels.tools.add_constant`. exog\_infl[array\_like](https://numpy.org/doc/stable/glossary.html#term-array_like "(in NumPy v2.4)") or [`None`](https://docs.python.org/3/library/constants.html#None "(in Python v3.14)") Explanatory variables for the binary inflation model, i.e. for mixing probability model. If None, then a constant is used. offset[array\_like](https://numpy.org/doc/stable/glossary.html#term-array_like "(in NumPy v2.4)") Offset is added to the linear prediction with coefficient equal to 1. exposure[array\_like](https://numpy.org/doc/stable/glossary.html#term-array_like "(in NumPy v2.4)") Log(exposure) is added to the linear prediction with coefficient equal to 1. inflation{‘logit’, ‘probit’} The model for the zero inflation, either Logit (default) or Probit missing[`str`](https://docs.python.org/3/library/stdtypes.html#str "(in Python v3.14)") Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’. Attributes:[¶](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.html#statsmodels.discrete.count_model.ZeroInflatedPoisson-attributes "Permalink to this headline") endog[`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.4)") A reference to the endogenous response variable exog[`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.4)") A reference to the exogenous design. exog\_infl[`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.4)") A reference to the zero-inflated exogenous design. Methods \| \| \| \|---\|---\| \| [`cdf`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf "statsmodels.discrete.count_model.ZeroInflatedPoisson.cdf (Python method) — The cumulative distribution function of the model.")(X) \| The cumulative distribution function of the model. \| \| [`cov_params_func_l1`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1 "statsmodels.discrete.count_model.ZeroInflatedPoisson.cov_params_func_l1 (Python method) — Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.")(likelihood\_model, xopt, ...) \| Computes cov\_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. \| \| [`fit`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.fit "statsmodels.discrete.count_model.ZeroInflatedPoisson.fit (Python method) — Fit the model using maximum likelihood.")(\[start\_params, method, maxiter, ...\]) \| Fit the model using maximum likelihood. \| \| [`fit_regularized`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized "statsmodels.discrete.count_model.ZeroInflatedPoisson.fit_regularized (Python method) — Fit the model using a regularized maximum likelihood.")(\[start\_params, method, ...\]) \| Fit the model using a regularized maximum likelihood. \| \| [`from_formula`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula "statsmodels.discrete.count_model.ZeroInflatedPoisson.from_formula (Python method) — Create a Model from a formula and dataframe.")(formula, data\[, subset, drop\_cols\]) \| Create a Model from a formula and dataframe. \| \| [`get_distribution`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution "statsmodels.discrete.count_model.ZeroInflatedPoisson.get_distribution (Python method) — Get frozen instance of distribution based on predicted parameters.")(params\[, exog, exog\_infl, ...\]) \| Get frozen instance of distribution based on predicted parameters. \| \| [`hessian`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian "statsmodels.discrete.count_model.ZeroInflatedPoisson.hessian (Python method) — Generic Zero Inflated model Hessian matrix of the loglikelihood")(params) \| Generic Zero Inflated model Hessian matrix of the loglikelihood \| \| [`information`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.information.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.information "statsmodels.discrete.count_model.ZeroInflatedPoisson.information (Python method) — Fisher information matrix of model.")(params) \| Fisher information matrix of model. \| \| [`initialize`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize "statsmodels.discrete.count_model.ZeroInflatedPoisson.initialize (Python method) — Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.")() \| Initialize is called by statsmodels.model.LikelihoodModel.\_\_init\_\_ and should contain any preprocessing that needs to be done for a model. \| \| [`loglike`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike "statsmodels.discrete.count_model.ZeroInflatedPoisson.loglike (Python method) — Loglikelihood of Generic Zero Inflated model.")(params) \| Loglikelihood of Generic Zero Inflated model. \| \| [`loglikeobs`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs "statsmodels.discrete.count_model.ZeroInflatedPoisson.loglikeobs (Python method) — Loglikelihood for observations of Generic Zero Inflated model.")(params) \| Loglikelihood for observations of Generic Zero Inflated model. \| \| [`pdf`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf "statsmodels.discrete.count_model.ZeroInflatedPoisson.pdf (Python method) — The probability density (mass) function of the model.")(X) \| The probability density (mass) function of the model. \| \| [`predict`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.predict.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.predict "statsmodels.discrete.count_model.ZeroInflatedPoisson.predict (Python method) — Predict expected response or other statistic given exogenous variables.")(params\[, exog, exog\_infl, exposure, ...\]) \| Predict expected response or other statistic given exogenous variables. \| \| [`score`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.score.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.score "statsmodels.discrete.count_model.ZeroInflatedPoisson.score (Python method) — Score vector of model.")(params) \| Score vector of model. \| \| [`score_obs`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs "statsmodels.discrete.count_model.ZeroInflatedPoisson.score_obs (Python method) — Generic Zero Inflated model score (gradient) vector of the log-likelihood")(params) \| Generic Zero Inflated model score (gradient) vector of the log-likelihood \| Properties \| \| \| \|---\|---\| \| [`endog_names`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names "statsmodels.discrete.count_model.ZeroInflatedPoisson.endog_names (Python property) — Names of endogenous variables.") \| Names of endogenous variables. \| \| [`exog_names`](https://www.statsmodels.org/dev/generated/statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names.html#statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names "statsmodels.discrete.count_model.ZeroInflatedPoisson.exog_names (Python property) — Names of exogenous variables.") \| Names of exogenous variables. \| Apr 16, 2026 © Copyright 2009-2025, Josef Perktold, Skipper Seabold, Jonathan Taylor, statsmodels-developers. 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