feat(altair): implement scatter-color-groups

plots/altair/point/scatter-color-groups/default.py

@@ -0,0 +1,54 @@
+"""
+scatter-color-groups: Scatter Plot with Color Groups
+Library: altair
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - create iris-like dataset with three species groups
+np.random.seed(42)
+
+# Generate data for three groups with different cluster centers
+n_per_group = 50
+
+# Setosa: smaller sepal length, larger sepal width
+setosa_x = np.random.normal(5.0, 0.4, n_per_group)
+setosa_y = np.random.normal(3.4, 0.4, n_per_group)
+
+# Versicolor: medium values
+versicolor_x = np.random.normal(6.0, 0.5, n_per_group)
+versicolor_y = np.random.normal(2.8, 0.3, n_per_group)
+
+# Virginica: larger sepal length, medium sepal width
+virginica_x = np.random.normal(6.6, 0.6, n_per_group)
+virginica_y = np.random.normal(3.0, 0.35, n_per_group)
+
+data = pd.DataFrame(
+    {
+        "sepal_length": np.concatenate([setosa_x, versicolor_x, virginica_x]),
+        "sepal_width": np.concatenate([setosa_y, versicolor_y, virginica_y]),
+        "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
+    }
+)
+
+# Define custom color palette (colorblind-safe)
+color_scale = alt.Scale(domain=["setosa", "versicolor", "virginica"], range=["#306998", "#FFD43B", "#059669"])
+
+# Create scatter plot with color groups
+chart = (
+    alt.Chart(data)
+    .mark_point(size=100, opacity=0.7)
+    .encode(
+        x=alt.X("sepal_length:Q", title="Sepal Length (cm)"),
+        y=alt.Y("sepal_width:Q", title="Sepal Width (cm)"),
+        color=alt.Color("species:N", title="Species", scale=color_scale),
+        tooltip=["species:N", "sepal_length:Q", "sepal_width:Q"],
+    )
+    .properties(width=1600, height=900, title="Scatter Plot with Color Groups")
+)
+
+# Save as PNG (1600 × 900 × 3 = 4800 × 2700 px)
+chart.save("plot.png", scale_factor=3.0)