diff --git a/altair-python/README.md b/altair-python/README.md
new file mode 100644
index 0000000000..55f1953171
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+++ b/altair-python/README.md
@@ -0,0 +1,3 @@
+# Altair: Declarative Charts With Python
+
+This folder provides the code examples for the Real Python tutorial [Altair: Declarative Charts With Python](https://realpython.com/altair-python/).
diff --git a/altair-python/altair-python.ipynb b/altair-python/altair-python.ipynb
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index 0000000000..476655994e
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+++ b/altair-python/altair-python.ipynb
@@ -0,0 +1,732 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7e0a276e-132b-4984-8c6a-9560eca313ac",
+ "metadata": {},
+ "source": [
+ "## Step Counts as a Bar Chart"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9047ab05-f289-4fca-af14-9c1f75bc953e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.Chart(...)"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import altair as alt\n",
+ "import pandas as pd\n",
+ "\n",
+ "steps = pd.DataFrame(\n",
+ " {\n",
+ " \"Day\": [\"1-Mon\", \"2-Tue\", \"3-Wed\", \"4-Thu\", \"5-Fri\", \"6-Sat\", \"7-Sun\"],\n",
+ " \"Steps\": [6200, 8400, 7100, 9800, 5500, 9870, 3769],\n",
+ " }\n",
+ ")\n",
+ "\n",
+ "weekly_steps = (\n",
+ " alt.Chart(steps)\n",
+ " .mark_bar()\n",
+ " .encode(\n",
+ " x=\"Day\",\n",
+ " y=\"Steps\",\n",
+ " )\n",
+ ")\n",
+ "weekly_steps"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2fd3412b-83ef-40d8-94bc-16422c97c59b",
+ "metadata": {},
+ "source": [
+ "## The `movies` Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "8446e30e-7fd0-406e-89ff-b5fec360830f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from altair.datasets import data\n",
+ "\n",
+ "movies = data.movies()\n",
+ "movies = movies.dropna(\n",
+ " subset=[\n",
+ " \"Production Budget\",\n",
+ " \"Worldwide Gross\",\n",
+ " \"IMDB Rating\",\n",
+ " \"Major Genre\",\n",
+ " \"MPAA Rating\",\n",
+ " ]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39c7c21e-9536-48bc-8bc9-c3da708c2eec",
+ "metadata": {},
+ "source": [
+ "## Basic Scatter Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4e25ea99-ec0f-4797-bc6c-46ab3cb36b22",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.Chart(...)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scatter = (\n",
+ " alt.Chart(movies)\n",
+ " .mark_point()\n",
+ " .encode(\n",
+ " x=\"Production Budget:Q\",\n",
+ " y=\"Worldwide Gross:Q\",\n",
+ " )\n",
+ ")\n",
+ "scatter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62d51fc2-c1e2-4398-a0bb-e4ab3a656832",
+ "metadata": {},
+ "source": [
+ "## Scatter Plot With Color and Size Encoding Channels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "6a6f25c8-7514-42ba-ba40-b2fd3c263e35",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.Chart(...)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scatter = (\n",
+ " alt.Chart(movies)\n",
+ " .mark_point()\n",
+ " .encode(\n",
+ " x=\"Production Budget:Q\",\n",
+ " y=\"Worldwide Gross:Q\",\n",
+ " color=\"Major Genre:N\",\n",
+ " size=\"IMDB Rating:Q\",\n",
+ " tooltip=[\"Title:N\", \"IMDB Rating:Q\"],\n",
+ " )\n",
+ ")\n",
+ "scatter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9ff6c75f-6ef6-4c9e-a058-f7fdaa98864f",
+ "metadata": {},
+ "source": [
+ "## Faceted Scatter Plot Split by MPAA Rating"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4d895cbb-463d-495f-aacf-cba27d2878dd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.Chart(...)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scatter = (\n",
+ " alt.Chart(movies)\n",
+ " .mark_point()\n",
+ " .encode(\n",
+ " x=\"Production Budget:Q\",\n",
+ " y=\"Worldwide Gross:Q\",\n",
+ " color=\"Major Genre:N\",\n",
+ " size=\"IMDB Rating:Q\",\n",
+ " tooltip=[\"Title:N\", \"IMDB Rating:Q\"],\n",
+ " column=\"MPAA Rating:O\",\n",
+ " )\n",
+ ")\n",
+ "scatter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d7de8c7-f8f6-4673-9bc2-321f216c486a",
+ "metadata": {},
+ "source": [
+ "## Basic Scatter Plot With Brush"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "260eb204-8fd3-4a68-877d-4c5471004da9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.Chart(...)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "brush = alt.selection_interval()\n",
+ "\n",
+ "scatter = (\n",
+ " alt.Chart(movies)\n",
+ " .mark_point()\n",
+ " .encode(\n",
+ " x=\"Production Budget:Q\",\n",
+ " y=\"Worldwide Gross:Q\",\n",
+ " color=(\n",
+ " alt.when(brush)\n",
+ " .then(\"Major Genre:N\")\n",
+ " .otherwise(alt.value(\"lightgray\"))\n",
+ " ),\n",
+ " )\n",
+ " .add_params(brush)\n",
+ ")\n",
+ "\n",
+ "scatter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c98813a3-48c5-4f55-b0c8-78cb0e53573f",
+ "metadata": {},
+ "source": [
+ "## Scatter Plot Connexted With Bars Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "eb3aef15-7e39-47ea-936a-3c02c80462ad",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.VConcatChart(...)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bars = (\n",
+ " alt.Chart(movies)\n",
+ " .mark_bar()\n",
+ " .encode(\n",
+ " x=\"mean(IMDB Rating):Q\",\n",
+ " y=\"Major Genre:N\",\n",
+ " )\n",
+ " .transform_filter(brush)\n",
+ ")\n",
+ "\n",
+ "scatter & bars"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6984104a-ff47-475c-a1b3-e84d5f81a6dd",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/altair-python/scatter_basic.py b/altair-python/scatter_basic.py
new file mode 100644
index 0000000000..0f798cbed4
--- /dev/null
+++ b/altair-python/scatter_basic.py
@@ -0,0 +1,20 @@
+import altair as alt
+from altair.datasets import data
+
+movies = data.movies()
+movies = movies.dropna(
+ subset=[
+ "Production Budget",
+ "Worldwide Gross",
+ ]
+)
+
+scatter = (
+ alt.Chart(movies)
+ .mark_point()
+ .encode(
+ x="Production Budget:Q",
+ y="Worldwide Gross:Q",
+ )
+)
+scatter
diff --git a/altair-python/scatter_connected.py b/altair-python/scatter_connected.py
new file mode 100644
index 0000000000..a43cb069c6
--- /dev/null
+++ b/altair-python/scatter_connected.py
@@ -0,0 +1,43 @@
+import altair as alt
+from altair.datasets import data
+
+movies = data.movies()
+movies = movies.dropna(
+ subset=[
+ "Production Budget",
+ "Worldwide Gross",
+ "IMDB Rating",
+ "Major Genre",
+ ]
+)
+
+brush = alt.selection_interval()
+
+scatter = (
+ alt.Chart(movies)
+ .mark_point()
+ .encode(
+ x="Production Budget:Q",
+ y="Worldwide Gross:Q",
+ color=(
+ alt.when(brush)
+ .then("Major Genre:N")
+ .otherwise(alt.value("lightgray")),
+ ),
+ )
+ .add_params(brush)
+)
+
+scatter
+
+bars = (
+ alt.Chart(movies)
+ .mark_bar()
+ .encode(
+ x="mean(IMDB Rating):Q",
+ y="Major Genre:N",
+ )
+ .transform_filter(brush)
+)
+
+scatter & bars
diff --git a/altair-python/scatter_encoding_channels.py b/altair-python/scatter_encoding_channels.py
new file mode 100644
index 0000000000..f815e4278e
--- /dev/null
+++ b/altair-python/scatter_encoding_channels.py
@@ -0,0 +1,25 @@
+import altair as alt
+from altair.datasets import data
+
+movies = data.movies()
+movies = movies.dropna(
+ subset=[
+ "Production Budget",
+ "Worldwide Gross",
+ "IMDB Rating",
+ "Major Genre",
+ ]
+)
+
+scatter = (
+ alt.Chart(movies)
+ .mark_point()
+ .encode(
+ x="Production Budget:Q",
+ y="Worldwide Gross:Q",
+ color="Major Genre:N",
+ size="IMDB Rating:Q",
+ tooltip=["Title:N", "IMDB Rating:Q"],
+ )
+)
+scatter
diff --git a/altair-python/scatter_faceted.py b/altair-python/scatter_faceted.py
new file mode 100644
index 0000000000..dc4621b47e
--- /dev/null
+++ b/altair-python/scatter_faceted.py
@@ -0,0 +1,27 @@
+import altair as alt
+from altair.datasets import data
+
+movies = data.movies()
+movies = movies.dropna(
+ subset=[
+ "Production Budget",
+ "Worldwide Gross",
+ "IMDB Rating",
+ "Major Genre",
+ "MPAA Rating",
+ ]
+)
+
+scatter = (
+ alt.Chart(movies)
+ .mark_point()
+ .encode(
+ x="Production Budget:Q",
+ y="Worldwide Gross:Q",
+ color="Major Genre:N",
+ size="IMDB Rating:Q",
+ tooltip=["Title:N", "IMDB Rating:Q"],
+ column="MPAA Rating:O",
+ )
+)
+scatter
diff --git a/altair-python/steps.py b/altair-python/steps.py
new file mode 100644
index 0000000000..102f2f49e3
--- /dev/null
+++ b/altair-python/steps.py
@@ -0,0 +1,19 @@
+import altair as alt
+import pandas as pd
+
+steps = pd.DataFrame(
+ {
+ "Day": ["1-Mon", "2-Tue", "3-Wed", "4-Thu", "5-Fri", "6-Sat", "7-Sun"],
+ "Steps": [6200, 8400, 7100, 9800, 5500, 9870, 3769],
+ }
+)
+
+weekly_steps = (
+ alt.Chart(steps)
+ .mark_bar()
+ .encode(
+ x="Day",
+ y="Steps",
+ )
+)
+weekly_steps