# Standardize Explainer on Bambi in PyMC Examples

**URL:** <https://discourse.pymc.io/t/standardize-explainer-on-bambi-in-pymc-examples/9326>\
**Category:** v5\
**Tags:** doc\
**Created:** [May 3, 2022, 1:45am UTC](https://discourse.pymc.io/t/standardize-explainer-on-bambi-in-pymc-examples/9326 "2022-05-03T01:45:07Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![hectormz](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/hectormz/32/1799_2.png) [@hectormz](https://discourse.pymc.io/u/hectormz)\
**Post date:** [May 3, 2022, 1:45am UTC](https://discourse.pymc.io/t/standardize-explainer-on-bambi-in-pymc-examples/9326/1 "2022-05-03T01:45:07Z")

</div>

I’ve been reviewing some of the GLM documents for `pymc` v4 and saw that `.glm()` has been deprecated and `bambi` seems to be the preferred route.

Some of the examples that were originally done _without_ `bambi` read a little confusing to me (or an outsider), that an example from `pymc` is about `bambi`.

I was wondering if there is some standard info that can be added to the intro/preamble of each example that explains what `bambi` is, that it is based on `pymc`, and that it is the preferred method.

I think `bambi` should even be in some of the titles of the examples. Ex:

> (Generalized) Linear and Hierarchical Linear Models in PyMC3 **and Bambi**

Even though `bambi` is built on top of `pymc`, I imagine people coming to the examples may be confused that they are technically learning to model with `bambi` and not `pymc` directly.

This example does a pretty good job about explaining `bambi` but its use could be explained in an intro that comes before the imports:

[(Generalized) Linear and Hierarchical Linear Models in PyMC3](https://docs.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM.html)

Here is an example that could be fixed:

[GLM: Logistic Regression](https://docs.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-logistic.html)

For the record, I like this example that shows the manual way, and the simpler way with `bambi`:

[GLM: Poisson Regression](https://docs.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-poisson-regression.html)