A parallel coordinates plot visualizes multivariate data by representing each variable as a vertical axis and each observation as a line connecting values across all axes. This technique is powerful for identifying patterns, clusters, and outliers in high-dimensional datasets where traditional 2D plots fall short. It enables simultaneous comparison of multiple variables for each data point.
""" anyplot.ai
parallel-basic: Basic Parallel Coordinates Plot
Library: altair 6.2.2 | Python 3.13.14
Quality: 90/100 | Updated: 2026-07-24
"""
import importlib
import os
import sys
from PIL import Image
# This file is named 'altair.py'. Remove the script directory (and '') from sys.path
# before loading the altair library to prevent this file from being imported as altair.
_script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if p not in ("", _script_dir)]
alt = importlib.import_module("altair")
np = importlib.import_module("numpy")
pd = importlib.import_module("pandas")
# 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"
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data - Iris-like dataset with 4 dimensions and 3 species
np.random.seed(42)
n_per_species = 50
species_names = ["Setosa", "Versicolor", "Virginica"]
dimensions = [
"Sepal Length (cm)",
"Sepal Width (cm)",
"Petal Length (cm)",
"Petal Width (cm)",
"Petal Area (cm2)",
"Sepal Aspect Ratio",
]
data = []
for i, sp in enumerate(species_names):
sepal_length = np.random.normal([5.0, 5.9, 6.6][i], 0.35, n_per_species)
sepal_width = np.random.normal([3.4, 2.8, 3.0][i], 0.38, n_per_species)
petal_length = np.random.normal([1.5, 4.3, 5.5][i], 0.17 + i * 0.25, n_per_species)
petal_width = np.random.normal([0.2, 1.3, 2.0][i], 0.1 + i * 0.15, n_per_species)
petal_area = petal_length * petal_width
sepal_aspect_ratio = sepal_length / sepal_width
for j in range(n_per_species):
data.append(
{
"Species": sp,
"Sepal Length (cm)": round(sepal_length[j], 2),
"Sepal Width (cm)": round(sepal_width[j], 2),
"Petal Length (cm)": round(petal_length[j], 2),
"Petal Width (cm)": round(petal_width[j], 2),
"Petal Area (cm2)": round(petal_area[j], 2),
"Sepal Aspect Ratio": round(sepal_aspect_ratio[j], 2),
"id": i * n_per_species + j,
}
)
df = pd.DataFrame(data)
# Normalize values to 0-1 range for fair comparison across axes
for dim in dimensions:
min_val = df[dim].min()
max_val = df[dim].max()
df[f"{dim}_norm"] = (df[dim] - min_val) / (max_val - min_val)
# Long format retaining original values for tooltips
df_long = df.melt(
id_vars=["id", "Species"] + dimensions,
value_vars=[f"{d}_norm" for d in dimensions],
var_name="Dimension",
value_name="Normalized Value",
)
df_long["Dimension"] = df_long["Dimension"].str.replace("_norm", "")
# Interactive selection: click a species in the legend to highlight/dim
species_select = alt.selection_point(fields=["Species"], bind="legend", empty=True)
# Plot
spec = (
alt.Chart(df_long)
.mark_line(strokeWidth=2.0)
.encode(
x=alt.X(
"Dimension:N",
sort=dimensions,
axis=alt.Axis(labelAngle=-20, labelFontSize=13, titleFontSize=17, title=None, labelPadding=10),
),
y=alt.Y(
"Normalized Value:Q",
scale=alt.Scale(domain=[0, 1]),
axis=alt.Axis(labelFontSize=14, titleFontSize=17, title="Normalized Value", tickCount=5),
),
detail="id:N",
color=alt.Color(
"Species:N",
scale=alt.Scale(domain=species_names, range=IMPRINT),
legend=alt.Legend(
title="Species",
titleFontSize=17,
labelFontSize=15,
symbolSize=170,
symbolStrokeWidth=3,
orient="right",
padding=12,
labelLimit=200,
),
),
opacity=alt.condition(species_select, alt.value(0.40), alt.value(0.06)),
tooltip=[
alt.Tooltip("Species:N"),
alt.Tooltip("Sepal Length (cm):Q", format=".2f"),
alt.Tooltip("Sepal Width (cm):Q", format=".2f"),
alt.Tooltip("Petal Length (cm):Q", format=".2f"),
alt.Tooltip("Petal Width (cm):Q", format=".2f"),
alt.Tooltip("Petal Area (cm2):Q", format=".2f"),
alt.Tooltip("Sepal Aspect Ratio:Q", format=".2f"),
],
)
.properties(
width=580,
height=300,
background=PAGE_BG,
title=alt.Title(
"parallel-basic · python · altair · anyplot.ai",
subtitle="Setosa separates cleanly on petal traits, while Versicolor and Virginica overlap on sepal traits",
fontSize=24,
subtitleFontSize=14,
subtitleColor=INK_SOFT,
anchor="middle",
),
)
.add_params(species_select)
)
chart = (
spec.configure_view(fill=PAGE_BG, strokeWidth=0)
.configure_axis(
domainColor=INK_SOFT, tickColor=INK_SOFT, gridColor=INK, gridOpacity=0.10, labelColor=INK_SOFT, titleColor=INK
)
.configure_title(color=INK)
.configure_legend(
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
strokeWidth=0.5,
cornerRadius=4,
labelColor=INK_SOFT,
titleColor=INK,
)
)
# Save
chart.save(f"plot-{THEME}.png", scale_factor=4.0)
# PAD-only to canonical target (do NOT crop — cropping clips title/axis labels)
TW, TH = 3200, 1800
_img = Image.open(f"plot-{THEME}.png").convert("RGB")
_w, _h = _img.size
if _w > TW or _h > TH:
raise SystemExit(
f"altair vl-convert produced {_w}x{_h}, exceeds target {TW}x{TH}. "
f"Shrink chart .properties(width=, height=) values and re-render."
)
if _w < TW or _h < TH:
_canvas = Image.new("RGB", (TW, TH), PAGE_BG)
_canvas.paste(_img, ((TW - _w) // 2, (TH - _h) // 2))
_canvas.save(f"plot-{THEME}.png")
chart.save(f"plot-{THEME}.html")
Part of Basic Parallel Coordinates Plot on anyplot.ai.