A scatter plot displayed in polar coordinates where data points are positioned using angle (theta) and radius (r) rather than Cartesian x and y coordinates. This visualization is particularly effective for cyclical or directional data where the angular component carries meaningful information.
""" anyplot.ai
polar-scatter: Polar Scatter Plot
Library: altair 6.1.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-09
"""
import os
import altair as alt
import numpy as np
import pandas as pd
# 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 (positions 1, 2, 3 for three categories)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data - Synthetic wind measurements with prevailing directions
np.random.seed(42)
n_points = 120
# Create realistic wind data with prevailing directions (NW and SE)
angles_nw = np.random.normal(315, 25, n_points // 3) # NW winds
angles_se = np.random.normal(135, 30, n_points // 3) # SE winds
angles_other = np.random.uniform(0, 360, n_points - 2 * (n_points // 3)) # Other directions
angles_deg = np.concatenate([angles_nw, angles_se, angles_other]) % 360
# Wind speeds (m/s) - higher speeds for prevailing directions
speeds = np.zeros(n_points)
speeds[: n_points // 3] = np.random.gamma(4, 2.5, n_points // 3) # NW - stronger
speeds[n_points // 3 : 2 * n_points // 3] = np.random.gamma(3, 2, n_points // 3) # SE - moderate
speeds[2 * n_points // 3 :] = np.random.gamma(2, 1.5, n_points - 2 * (n_points // 3)) # Other - weaker
speeds = np.clip(speeds, 1, 20)
# Time of day categories
time_of_day = np.array(
["Morning"] * (n_points // 3) + ["Afternoon"] * (n_points // 3) + ["Evening"] * (n_points - 2 * (n_points // 3))
)
# Convert polar to Cartesian for Altair (doesn't have native polar support)
angles_rad = np.deg2rad(angles_deg)
x = speeds * np.cos(angles_rad)
y = speeds * np.sin(angles_rad)
df = pd.DataFrame({"x": x, "y": y, "speed": speeds, "direction": angles_deg, "time_of_day": time_of_day})
# Create polar gridlines (circles)
max_radius = 20
radii = [5, 10, 15, 20]
circle_points = []
for r in radii:
theta = np.linspace(0, 2 * np.pi, 100)
for i, t in enumerate(theta):
circle_points.append({"x": r * np.cos(t), "y": r * np.sin(t), "radius": r, "order": i})
circles_df = pd.DataFrame(circle_points)
# Create angular gridlines (spokes)
spoke_angles = [0, 45, 90, 135, 180, 225, 270, 315]
spoke_points = []
for angle in spoke_angles:
rad = np.deg2rad(angle)
spoke_points.append({"x": 0, "y": 0, "angle": angle, "group": angle, "order": 0})
spoke_points.append(
{"x": max_radius * np.cos(rad), "y": max_radius * np.sin(rad), "angle": angle, "group": angle, "order": 1}
)
spokes_df = pd.DataFrame(spoke_points)
# Create angle labels
label_offset = max_radius * 1.12
angle_labels = []
direction_names = {
0: "E (0°)",
45: "NE (45°)",
90: "N (90°)",
135: "NW (135°)",
180: "W (180°)",
225: "SW (225°)",
270: "S (270°)",
315: "SE (315°)",
}
for angle, name in direction_names.items():
rad = np.deg2rad(angle)
angle_labels.append({"x": label_offset * np.cos(rad), "y": label_offset * np.sin(rad), "label": name})
labels_df = pd.DataFrame(angle_labels)
# Create radius labels
radius_labels = [{"x": r + 0.5, "y": 0.5, "label": f"{r} m/s"} for r in radii]
radius_labels_df = pd.DataFrame(radius_labels)
# Circular gridlines
grid_circles = (
alt.Chart(circles_df)
.mark_line(strokeWidth=1, opacity=0.10)
.encode(
x=alt.X("x:Q", axis=None),
y=alt.Y("y:Q", axis=None),
detail="radius:N",
order="order:Q",
color=alt.value(INK_SOFT),
)
)
# Radial spokes
grid_spokes = (
alt.Chart(spokes_df)
.mark_line(strokeWidth=1, opacity=0.10)
.encode(
x=alt.X("x:Q", axis=None),
y=alt.Y("y:Q", axis=None),
detail="group:N",
order="order:Q",
color=alt.value(INK_SOFT),
)
)
# Angle labels
angle_text = (
alt.Chart(labels_df)
.mark_text(fontSize=18, fontWeight="bold")
.encode(x=alt.X("x:Q", axis=None), y=alt.Y("y:Q", axis=None), text="label:N", color=alt.value(INK_SOFT))
)
# Radius labels
radius_text = (
alt.Chart(radius_labels_df)
.mark_text(fontSize=14, align="left")
.encode(x=alt.X("x:Q", axis=None), y=alt.Y("y:Q", axis=None), text="label:N", color=alt.value(INK_SOFT))
)
# Data points with Okabe-Ito colors
points = (
alt.Chart(df)
.mark_point(size=220, filled=True, opacity=0.8)
.encode(
x=alt.X("x:Q", axis=None, scale=alt.Scale(domain=[-25, 25])),
y=alt.Y("y:Q", axis=None, scale=alt.Scale(domain=[-25, 25])),
color=alt.Color(
"time_of_day:N",
scale=alt.Scale(domain=["Morning", "Afternoon", "Evening"], range=IMPRINT),
title="Time of Day",
),
tooltip=[
alt.Tooltip("direction:Q", title="Direction (°)", format=".1f"),
alt.Tooltip("speed:Q", title="Wind Speed (m/s)", format=".1f"),
alt.Tooltip("time_of_day:N", title="Time of Day"),
],
)
)
# Combine all layers with theme-adaptive styling
chart = (
alt.layer(grid_circles, grid_spokes, angle_text, radius_text, points)
.properties(
width=1600,
height=1600,
background=PAGE_BG,
title=alt.Title("polar-scatter · altair · anyplot.ai", fontSize=28, anchor="middle", color=INK),
)
.configure_view(strokeWidth=0, fill=PAGE_BG)
.configure_legend(
titleFontSize=22,
labelFontSize=18,
symbolSize=220,
orient="right",
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
titleColor=INK,
labelColor=INK_SOFT,
)
.interactive()
)
# Save as PNG and HTML with theme-suffixed filenames
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Part of Polar Scatter Plot on anyplot.ai.