A multi-series radar chart overlays multiple data polygons on shared axes radiating from a center point, enabling direct comparison across several entities or categories. Each series is rendered as a distinct colored polygon, making it easy to identify relative strengths and weaknesses at a glance. This visualization excels at comparative analysis where multiple subjects are evaluated across the same set of metrics.
""" anyplot.ai
radar-multi: Multi-Series Radar Chart
Library: altair 6.1.0 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-07
"""
import importlib.util
import os
import sys
import numpy as np
import pandas as pd
# Explicitly import altair from site-packages to avoid shadowing
spec = importlib.util.find_spec("altair")
if spec and spec.origin and "site-packages" in spec.origin:
alt = importlib.util.module_from_spec(spec)
sys.modules["altair"] = alt
spec.loader.exec_module(alt)
else:
# Fallback: remove the directory containing this script from path
script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path[:] = [p for p in sys.path if os.path.abspath(p) != script_dir]
import altair as alt
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Okabe-Ito palette (first series ALWAYS #009E73)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data: Product comparison across key attributes
categories = ["Price", "Quality", "Durability", "Support", "Features", "Design"]
n_categories = len(categories)
data = {
"Product A": [85, 70, 90, 65, 80, 75],
"Product B": [60, 85, 75, 90, 70, 80],
"Product C": [75, 65, 80, 70, 95, 85],
}
# Calculate angles for each axis (in radians) - start from top
angles = [np.pi / 2 - i * 2 * np.pi / n_categories for i in range(n_categories)]
# Build records for each series
records = []
for series_name, values in data.items():
for i, (cat, val, angle) in enumerate(zip(categories, values, angles, strict=True)):
# Convert polar to cartesian for plotting
x = val * np.cos(angle)
y = val * np.sin(angle)
records.append(
{"series": series_name, "category": cat, "value": val, "angle": angle, "x": x, "y": y, "order": i}
)
# Close the polygon by adding the first point again
first_val = values[0]
first_angle = angles[0]
records.append(
{
"series": series_name,
"category": categories[0],
"value": first_val,
"angle": first_angle,
"x": first_val * np.cos(first_angle),
"y": first_val * np.sin(first_angle),
"order": n_categories,
}
)
df = pd.DataFrame(records)
# Create gridlines (hexagonal matching the axes)
grid_records = []
for r in [20, 40, 60, 80, 100]:
for i, angle in enumerate(angles):
grid_records.append({"radius": r, "x": r * np.cos(angle), "y": r * np.sin(angle), "order": i})
# Close the hexagon
grid_records.append({"radius": r, "x": r * np.cos(angles[0]), "y": r * np.sin(angles[0]), "order": n_categories})
grid_df = pd.DataFrame(grid_records)
# Create axis lines (spokes from center to edge)
spoke_records = []
for i, (cat, angle) in enumerate(zip(categories, angles, strict=True)):
spoke_records.append({"category": cat, "x": 0, "y": 0, "order": 0, "spoke_id": i})
spoke_records.append(
{"category": cat, "x": 105 * np.cos(angle), "y": 105 * np.sin(angle), "order": 1, "spoke_id": i}
)
spoke_df = pd.DataFrame(spoke_records)
# Create axis labels (positioned beyond the outer gridline)
label_records = []
for cat, angle in zip(categories, angles, strict=True):
label_x = 125 * np.cos(angle)
label_y = 125 * np.sin(angle)
label_records.append({"category": cat, "x": label_x, "y": label_y})
label_df = pd.DataFrame(label_records)
# Create grid value labels on all spokes
value_label_records = []
for r in [20, 40, 60, 80, 100]:
for angle in angles:
x = r * np.cos(angle) + 8
y = r * np.sin(angle) + 2
value_label_records.append({"value": str(r), "x": x, "y": y})
value_label_df = pd.DataFrame(value_label_records)
# Series list and colors
series_list = ["Product A", "Product B", "Product C"]
color_scale = alt.Scale(domain=series_list, range=IMPRINT)
# Chart dimensions for square output (base size with 3x scale factor)
chart_width = 1600
chart_height = 1600
# Domain for axes
axis_domain = [-160, 160]
# Base encoding for x and y
x_enc = alt.X("x:Q", scale=alt.Scale(domain=axis_domain), axis=None)
y_enc = alt.Y("y:Q", scale=alt.Scale(domain=axis_domain), axis=None)
# Grid hexagons
grid_lines = (
alt.Chart(grid_df)
.mark_line(strokeWidth=1.5, opacity=0.15)
.encode(x=x_enc, y=y_enc, detail="radius:N", order="order:Q", stroke=alt.value(INK_SOFT))
)
# Spokes (axis lines)
spokes = (
alt.Chart(spoke_df)
.mark_line(strokeWidth=1.5, opacity=0.25)
.encode(x=x_enc, y=y_enc, detail="spoke_id:N", order="order:Q", stroke=alt.value(INK_SOFT))
)
# Axis labels
labels = (
alt.Chart(label_df)
.mark_text(fontSize=22, fontWeight="bold")
.encode(x="x:Q", y="y:Q", text="category:N", color=alt.value(INK))
)
# Grid value labels
value_labels = (
alt.Chart(value_label_df)
.mark_text(fontSize=14, align="left", baseline="middle")
.encode(x="x:Q", y="y:Q", text="value:N", color=alt.value(INK_SOFT))
)
# Create filled polygons for each series
fill_layers = []
for series_name, fill_color in zip(series_list, IMPRINT, strict=True):
series_df = df[df["series"] == series_name].copy()
# Use mark_area for proper polygon fill
fill_layer = (
alt.Chart(series_df)
.mark_area(fillOpacity=0.25, opacity=0.25)
.encode(x=x_enc, y=y_enc, color=alt.value(fill_color), order="order:Q")
)
fill_layers.append(fill_layer)
# Polygon outlines
polygon_outline = (
alt.Chart(df)
.mark_line(strokeWidth=3, opacity=0.9)
.encode(
x=x_enc,
y=y_enc,
color=alt.Color(
"series:N",
scale=color_scale,
legend=alt.Legend(
title="Series",
titleFontSize=20,
labelFontSize=18,
orient="right",
offset=10,
symbolSize=300,
symbolStrokeWidth=3,
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
labelColor=INK_SOFT,
titleColor=INK,
),
),
detail="series:N",
order="order:Q",
)
)
# Data points (exclude the closing point)
points_df = df[df["order"] < n_categories].copy()
points = (
alt.Chart(points_df)
.mark_circle(size=200, opacity=0.9)
.encode(
x=x_enc,
y=y_enc,
color=alt.Color("series:N", scale=color_scale, legend=None),
tooltip=["series:N", "category:N", "value:Q"],
)
)
# Combine all layers
all_layers = [grid_lines, spokes] + fill_layers + [polygon_outline, points, labels, value_labels]
chart = (
alt.layer(*all_layers)
.properties(
width=chart_width,
height=chart_height,
background=PAGE_BG,
title=alt.Title("radar-multi · altair · pyplots.ai", fontSize=28, anchor="middle", offset=20),
)
.configure_view(strokeWidth=0, fill=PAGE_BG)
.configure_legend(strokeColor=INK_SOFT, padding=15, labelColor=INK_SOFT, titleColor=INK)
)
# Save as PNG and HTML with theme suffix
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Part of Multi-Series Radar Chart on anyplot.ai.