Showcase polars support (#90)

* add polars example

* specify in the list of features

README.md

@@ -21,7 +21,8 @@ pip install conjugate-models
 - [Built in Plotting](https://wd60622.github.io/conjugate/examples/plotting) with `plot_pdf` and `plot_pmf` methods
 - [Vectorized Operations](https://wd60622.github.io/conjugate/examples/vectorized-inputs) for parameters and data
 - [Indexing Parameters](https://wd60622.github.io/conjugate/examples/indexing) for subsetting and slicing
-- [Generalized Numerical Inputs](https://wd60622.github.io/conjugate/examples/generalized-inputs) for inputs other than builtins and numpy arrays
+- [Generalized Numerical Inputs](https://wd60622.github.io/conjugate/examples/generalized-inputs) for any inputs that act like numbers
+    - Out of box compatibility with `polars`, `pandas`, `numpy`, and more.
 - [Unsupported Distributions](https://wd60622.github.io/conjugate/examples/pymc-sampling) for sampling from unsupported distributions
 
 ## Supported Models 

docs/examples/generalized-inputs.md

@@ -3,8 +3,30 @@ comments: true
 ---
 # Generalized Numerical Inputs
 
-Though the plotting is meant for numpy and python numbers, the conjugate models work with anything that works like numbers. 
+Conjugate models work with anything that works like numbers. 
 
+## Polars
+
+For instance, Bayesian models with the Polars package: 
+
+```python
+import polars as pl
+
+# Data
+df = pl.DataFrame({
+    "total": [10, 20, 50],
+    "successes": [5, 10, 25]
+})
+
+# Conjugate prior
+prior = Beta(alpha=1, beta=1)
+posterior = binomial_beta(n=df["total"], x=df["successes"], beta_prior=prior)
+
+ax = posterior.plot_pdf(label=df["total"])
+ax.legend(title="sample size")
+```
+
+![Polars Example](./../images/polars.png)
 
 ## Models with SQL
 
@@ -39,7 +61,6 @@ print("Posterior beta:", posterior.beta)
 # Posterior beta: "previous_failures"-1+"total"-"successes"
 ```
 
-
 ## PyMC
 
 Using PyMC distributions for sampling with additional uncertainty

docs/index.md

@@ -25,7 +25,8 @@ pip install conjugate-models
 - [Built in Plotting](examples/plotting.md) with `plot_pdf` and `plot_pmf` methods
 - [Vectorized Operations](examples/vectorized-inputs.md) for parameters and data
 - [Indexing Parameters](examples/indexing.md) for subsetting and slicing
-- [Generalized Numerical Inputs](examples/generalized-inputs.md) for inputs other than builtins and numpy arrays
+- [Generalized Numerical Inputs](examples/generalized-inputs.md) for any inputs that act like numbers
+    - Out of box compatibility with `polars`, `pandas`, `numpy`, and more.
 - [Unsupported Distributions](examples/pymc-sampling.md) for sampling from unsupported distributions
 
 ## Supported Models