diff --git a/doc/python/plotly-express.md b/doc/python/plotly-express.md
index 4a3327c848d..4e4c30e8f31 100644
--- a/doc/python/plotly-express.md
+++ b/doc/python/plotly-express.md
@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.2'
-      jupytext_version: 1.4.2
+      format_version: '1.3'
+      jupytext_version: 1.14.7
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.7
+    version: 3.10.4
   plotly:
     description: Plotly Express is a terse, consistent, high-level API for creating
       figures.
@@ -42,8 +42,7 @@ Plotly Express provides [more than 30 functions for creating different types of
 
 Here is a talk from the [SciPy 2021 conference](https://www.scipy2021.scipy.org/) that gives a good introduction to Plotly Express and [Dash](https://dash.plotly.com/):
 
-```python hide_code=true
-%%html
+```html hide_code=true
 <div align="center">
 <iframe width="560" height="315"
 src="https://www.youtube.com/embed/FpCgG85g2Hw"
@@ -72,7 +71,7 @@ The Plotly Express API in general offers the following features:
 
 * **A single entry point into `plotly`**: just `import plotly.express as px` and get access to [all the plotting functions](https://plotly.com/python-api-reference/plotly.express.html), plus [built-in demo datasets under `px.data`](https://plotly.com/python-api-reference/generated/plotly.data.html#module-plotly.data) and [built-in color scales and sequences under `px.color`](https://plotly.com/python-api-reference/generated/plotly.colors.html#module-plotly.colors). Every PX function returns a `plotly.graph_objects.Figure` object, so you can edit it using all the same methods like [`update_layout` and `add_trace`](https://plotly.com/python/creating-and-updating-figures/#updating-figures).
 * **Sensible, Overridable Defaults**: PX functions will infer sensible defaults wherever possible, and will always let you override them.
-* **Flexible Input Formats**: PX functions [accept input in a variety of formats](/python/px-arguments/), from `list`s and `dict`s to [long-form or wide-form Pandas `DataFrame`s](/python/wide-form/) to [`numpy` arrays and `xarrays`](/python/imshow/) to [GeoPandas `GeoDataFrames`](/python/maps/).
+* **Flexible Input Formats**: PX functions [accept input in a variety of formats](/python/px-arguments/), from `list`s and `dict`s to [long-form or wide-form `DataFrame`s](/python/wide-form/) to [`numpy` arrays and `xarrays`](/python/imshow/) to [GeoPandas `GeoDataFrames`](/python/maps/).
 * **Automatic Trace and Layout configuration**: PX functions will create one [trace](/python/figure-structure) per animation frame for each unique combination of data values mapped to discrete color, symbol, line-dash, facet-row and/or facet-column. Traces' [`legendgroup` and `showlegend` attributes](https://plotly.com/python/legend/) are set such that only one legend item appears per unique combination of discrete color, symbol and/or line-dash. Traces are automatically linked to a correctly-configured [subplot of the appropriate type](/python/figure-structure).
 * **Automatic Figure Labelling**: PX functions [label axes, legends and colorbars](https://plotly.com/python/figure-labels/) based in the input `DataFrame` or `xarray`, and provide [extra control with the `labels` argument](/python/styling-plotly-express/).
 * **Automatic Hover Labels**: PX functions populate the hover-label using the labels mentioned above, and provide [extra control with the `hover_name` and `hover_data` arguments](/python/hover-text-and-formatting/).
diff --git a/doc/python/px-arguments.md b/doc/python/px-arguments.md
index 377b74cacce..223d6dac9dc 100644
--- a/doc/python/px-arguments.md
+++ b/doc/python/px-arguments.md
@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.2'
-      jupytext_version: 1.4.2
+      format_version: '1.3'
+      jupytext_version: 1.14.7
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.7
+    version: 3.10.4
   plotly:
     description: Input data arguments accepted by Plotly Express functions
     display_as: file_settings
@@ -35,9 +35,11 @@ jupyter:
 
 ### Plotly Express works with Column-oriented, Matrix or Geographic Data
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces [easy-to-style figures](/python/styling-plotly-express/).
 
-Plotly Express provides functions to visualize a variety of types of data. Most functions such as `px.bar` or `px.scatter` expect to operate on column-oriented data of the type you might store in a Pandas `DataFrame` (in either "long" or "wide" format, see below). [`px.imshow` operates on matrix-like data](/python/imshow/) you might store in a `numpy` or `xarray` array and functions like [`px.choropleth` and `px.choropleth_mapbox` can operate on geographic data](/python/maps/) of the kind you might store in a GeoPandas `GeoDataFrame`. This page details how to provide column-oriented data to most Plotly Express functions.
+Plotly Express provides functions to visualize a variety of types of data. Most functions such as `px.bar` or `px.scatter` expect to operate on column-oriented data of the type you might store in a `DataFrame` (in either "long" or "wide" format, see below). These functions use Pandas internally to process the data, but also accept other types of DataFrames as arguments. See the **Input Data as Non-Pandas DataFrames** section below for more details.
+
+[`px.imshow` operates on matrix-like data](/python/imshow/) you might store in a `numpy` or `xarray` array and functions like [`px.choropleth` and `px.choropleth_mapbox` can operate on geographic data](/python/maps/) of the kind you might store in a GeoPandas `GeoDataFrame`. This page details how to provide column-oriented data to most Plotly Express functions.
 
 
 
@@ -166,6 +168,36 @@ fig = px.bar(df, x='year', y=gdp, color='continent', labels={'y':'gdp'},
 fig.show()
 ```
 
+### Input Data as Non-Pandas `DataFrame`s
+
+**New in 5.16**
+
+In the examples above, we've used Pandas DataFrames. You can also provide another type of DataFrame to the `data_frame` argument if that DataFrame has a `to_pandas` method or supports the [Python dataframe interchange protocol](https://data-apis.org/dataframe-protocol/latest/index.html), for example, a [Polars](https://www.pola.rs/) DataFrame.
+
+Plotly Express uses Pandas internally to process the data. When you provide a Non-Pandas DataFrame to the `data_frame` argument of a Plotly Express function, the entire DataFrame is converted to a Pandas DataFrame.
+
+If you are using a type of DataFrame that doesn't have a `to_pandas` method, but supports the Python dataframe interchange protocol, you'll need to have Pandas version 2.0.3 or later installed. 
+
+In this example, we use a Polars DataFrame. If you are using Polars, you'll need to install `pyarrow`, which is used by its [`to_pandas` method](
+https://pola-rs.github.io/polars/py-polars/html/reference/dataframe/api/polars.DataFrame.to_pandas.html)
+
+```python
+import polars as pl
+import plotly.express as px
+
+wide_df = pl.DataFrame(
+    {
+        "nation": ["South Korea", "China", "Canada"],
+        "gold": [24, 10, 9],
+        "silver": [13, 15, 12],
+        "bronze": [9, 12, 12],
+    }
+)
+
+fig = px.bar(wide_df, x="nation", y=["gold", "silver", "bronze"], title="Wide-Form Input")
+fig.show()
+```
+
 ### Input Data as array-like columns: NumPy arrays, lists...
 
 `px` arguments can also be array-like objects such as lists, NumPy arrays, in both long-form or wide-form (for certain functions).
@@ -243,4 +275,4 @@ fig = px.bar(df, x='year', y=gdp, color='continent', labels={'y':'log gdp'},
              hover_data=['country'],
              title='Evolution of world GDP')
 fig.show()
-```
\ No newline at end of file
+```
diff --git a/doc/python/wide-form.md b/doc/python/wide-form.md
index b1a35c01064..88d9c08865a 100644
--- a/doc/python/wide-form.md
+++ b/doc/python/wide-form.md
@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.2'
-      jupytext_version: 1.4.2
+      format_version: '1.3'
+      jupytext_version: 1.14.7
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.7
+    version: 3.10.4
   plotly:
     description: Plotly Express' 2D-Cartesian functions accept data in long-, wide-,
       and mixed-form.
@@ -36,7 +36,9 @@ jupyter:
 
 ### Plotly Express works with Column-oriented, Matrix or Geographic Data
 
-Plotly Express provides functions to visualize a variety of types of data. Most functions such as `px.bar` or `px.scatter` expect to operate on column-oriented data of the type you might store in a Pandas `DataFrame` (in either "long" or "wide" format, see below). [`px.imshow` operates on matrix-like data](/python/imshow/) you might store in a `numpy` or `xarray` array and functions like [`px.choropleth` and `px.choropleth_mapbox` can operate on geographic data](/python/maps/) of the kind you might store in a GeoPandas `GeoDataFrame`. This page details how to provide a specific form of column-oriented data to 2D-Cartesian Plotly Express functions, but you can also check out our [detailed column-input-format documentation](/python/px-arguments/).
+Plotly Express provides functions to visualize a variety of types of data. Most functions such as `px.bar` or `px.scatter` expect to operate on column-oriented data of the type you might store in a `DataFrame` (in either "long" or "wide" format, see below). These functions use Pandas internally to process the data, but also accept other types of DataFrames as arguments. See the [Plotly Express arguments page](/python/px-arguments/) for more details.
+
+[`px.imshow` operates on matrix-like data](/python/imshow/) you might store in a `numpy` or `xarray` array and functions like [`px.choropleth` and `px.choropleth_mapbox` can operate on geographic data](/python/maps/) of the kind you might store in a GeoPandas `GeoDataFrame`. This page details how to provide a specific form of column-oriented data to 2D-Cartesian Plotly Express functions, but you can also check out our [detailed column-input-format documentation](/python/px-arguments/).
 
 ### Plotly Express works with Long-, Wide-, and Mixed-Form Data
 
@@ -312,4 +314,4 @@ fig.show()
 
 fig = px.box(wide_df, orientation="h")
 fig.show()
-```
\ No newline at end of file
+```
diff --git a/doc/requirements.txt b/doc/requirements.txt
index 038ce54d8ed..b888ed8816a 100644
--- a/doc/requirements.txt
+++ b/doc/requirements.txt
@@ -37,3 +37,4 @@ orjson
 dash-bio
 jinja2<3.1
 parmed<=3.4.4; python_version<"3.8"
+polars