diff --git a/docs/api/plotting.md b/docs/api/plotting.md
index 4383853b1b3..301a3f62457 100644
--- a/docs/api/plotting.md
+++ b/docs/api/plotting.md
@@ -74,17 +74,26 @@ chart # You can now access chart.value to get the selected data
 
 Altair has a concept of [data](https://altair-viz.github.io/user_guide/data_transformers.html) transformers, which can be used to improve performance.
 
-Such examples are:
-
-- pandas Dataframe has to be sanitized and serialized to JSON.
-- The rows of a Dataframe might need to be sampled or limited to a maximum number.
-- The Dataframe might be written to a `.csv` or `.json` file for performance reasons.
-
-By default, Altair uses the `default` data transformer, which is the slowest in marimo. It is limited to 5000 rows (although we increase this to `20_000` rows as marimo can handle this). This includes the data inside the HTML that is being sent over the network, which can also be limited by marimo's maximum message size.
-
-It is recommended to use the `marimo_csv` data transformer, which is the most performant and can handle the largest datasets: it converts the data to a CSV file which is smaller and can be sent over the network. This can handle up to +400,000 rows with no issues.
-
-When using `mo.ui.altair_chart`, we automatically set the data transformer to `marimo_csv` for you. If you are using Altair directly, you can set the data transformer using the following code:
+Some examples are:
+
+- pandas Dataframe has to be sanitized and serialized to JSON;
+- the rows of a Dataframe might need to be sampled or limited to a maximum number;
+- the Dataframe might be written to a `.csv` or `.json` file for performance reasons.
+
+By default, Altair uses the `default` data transformer, which is the slowest in
+marimo. It is limited to 5000 rows (although we increase this to `20_000` rows
+as marimo can handle this). This includes the data inside the HTML that is
+being sent over the network, which can also be limited by marimo's maximum
+message size.
+
+It is recommended to use the `marimo_csv` data transformer, which is the most
+performant and can handle the largest datasets: it converts the data to a CSV
+file which is smaller and can be sent over the network. This can handle up to
++400,000 rows with no issues.
+
+When using `mo.ui.altair_chart`, we automatically set the data transformer to
+`marimo_csv` for you. If you are using Altair directly, you can set the data
+transformer using the following code:
 
 ```python
 import altair as alt
diff --git a/docs/guides/working_with_data/plotting.md b/docs/guides/working_with_data/plotting.md
index 0de3ae7f3d8..d2735e0c98b 100644
--- a/docs/guides/working_with_data/plotting.md
+++ b/docs/guides/working_with_data/plotting.md
@@ -41,7 +41,7 @@ points inside the selection. When nothing is selected, `fig.value` is falsy and
 
 #### Example
 
-<!-- TODO add a WASM version when shipped. -->
+/// tab | code
 
 ```python
 import matplotlib.pyplot as plt
@@ -62,6 +62,37 @@ mask = ax.value.get_mask(x, y)
 selected_x, selected_y = x[mask], y[mask]
 ```
 
+///
+
+/// tab | live example
+
+/// marimo-embed
+    size: large
+
+```python
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    x = np.random.randn(500)
+    y = np.random.randn(500)
+    plt.scatter(x, y)
+    ax = mo.ui.matplotlib(plt.gca())
+    ax
+    return
+
+@app.cell
+def __():
+    mask = ax.value.get_mask(x, y)
+    np.column_stack([x[mask], y[mask]])
+    return
+```
+
+///
+
+///
+
 #### Debouncing
 
 By default, the selection streams to Python as you drag. For expensive
@@ -284,7 +315,7 @@ conda install -c conda-forge "vegafusion-python-embed>=1.4.0" "vegafusion>=1.4.0
 @app.cell(hide_code=True)
 async def __():
     import micropip
-    await micropip.install("plotly[express]")
+    await micropip.install(["plotly[express]", "pandas"])
     import plotly.express as px
     return px,