BAyesian Model-Building Interface (BAMBI) in Python#

Bambi is a high-level Bayesian model-building interface written in Python. It works with two probabilistic programming frameworks, PyMC3 or PyStan, and is designed to make it extremely easy to fit Bayesian mixed-effects models common in biology, social sciences and other disciplines.

New Features#

Bambi version 0.1.1 will be the final version supporting Python 2, but look forward to the forthcoming Bambi version 0.1.2!


Bambi is tested on Python 2.7 and 3.6 and depends on NumPy, Pandas, PyMC3, PyStan, and Patsy (see requirements.txt for version information).


The latest release of Bambi can be installed using pip:

pip install bambi

Alternatively, if you want the bleeding edge version of the package, you can install from GitHub:

pip install git+


A simple fixed effects model is shown below as example.

from bambi import Model
import pandas as pd

# Read in a tab-delimited file containing our data
data = pd.read_table('my_data.txt', sep='\t')

# Initialize the model
model = Model(data)

# Fixed effects only model
results ='DV ~ IV1 + IV2', samples=1000, chains=4)

# Drop the first 100 burn-in samples from each chain and plot

# Key summary and diagnostic info on the model parameters

For a more in-depth introduction to Bambi see our Quickstart or our set of example notebooks.


We welcome contributions from interested individuals or groups! For information about contributing to Bambi, check out our instructions, policies, and guidelines here.


See the GitHub contributor page.