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: letsplot 4.11.0 | Python 3.13.14
Quality: 87/100 | Updated: 2026-07-24
"""
import os
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_line,
geom_segment,
geom_text,
ggplot,
ggsave,
ggsize,
labs,
scale_alpha_identity,
scale_color_manual,
scale_size_identity,
scale_x_continuous,
scale_y_continuous,
theme,
)
LetsPlot.setup_html()
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 palette positions 1-3 — first series always #009E73
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data - Iris dataset with 4 dimensions
# Using 30 samples (10 per species) for clarity
data = {
"sepal_length": [
5.1,
4.9,
4.7,
4.6,
5.0,
5.4,
4.6,
5.0,
4.4,
4.9,
7.0,
6.4,
6.9,
5.5,
6.5,
5.7,
6.3,
4.9,
6.6,
5.2,
6.3,
5.8,
7.1,
6.3,
6.5,
7.6,
4.9,
7.3,
6.7,
7.2,
],
"sepal_width": [
3.5,
3.0,
3.2,
3.1,
3.6,
3.9,
3.4,
3.4,
2.9,
3.1,
3.2,
3.2,
3.1,
2.3,
2.8,
2.8,
3.3,
2.4,
2.9,
2.7,
3.3,
2.7,
3.0,
2.9,
3.0,
3.0,
2.5,
2.9,
2.5,
3.6,
],
"petal_length": [
1.4,
1.4,
1.3,
1.5,
1.4,
1.7,
1.4,
1.5,
1.4,
1.5,
4.7,
4.5,
4.9,
4.0,
4.6,
4.5,
4.7,
3.3,
4.6,
3.9,
6.0,
5.1,
5.9,
5.6,
5.8,
6.6,
4.5,
6.3,
5.8,
6.1,
],
"petal_width": [
0.2,
0.2,
0.2,
0.2,
0.2,
0.4,
0.3,
0.2,
0.2,
0.1,
1.4,
1.5,
1.5,
1.3,
1.5,
1.3,
1.6,
1.0,
1.3,
1.4,
2.5,
1.9,
2.1,
1.8,
2.2,
2.1,
1.7,
1.8,
1.8,
2.5,
],
"species": ["Setosa"] * 10 + ["Versicolor"] * 10 + ["Virginica"] * 10,
}
df = pd.DataFrame(data)
# Dimensions to plot (shorter labels to avoid overlap)
dimensions = ["sepal_length", "sepal_width", "petal_length", "petal_width"]
dim_labels = ["Sepal\nLength (cm)", "Sepal\nWidth (cm)", "Petal\nLength (cm)", "Petal\nWidth (cm)"]
# Normalize each dimension to 0-1 range for fair comparison
df_normalized = df.copy()
for dim in dimensions:
min_val = df[dim].min()
max_val = df[dim].max()
df_normalized[dim] = (df[dim] - min_val) / (max_val - min_val)
# Convert to long format for parallel coordinates
line_data = []
for idx, row in df_normalized.iterrows():
obs_id = idx
species = row["species"]
for i, dim in enumerate(dimensions):
line_data.append({"x": i, "y": row[dim], "observation": obs_id, "species": species})
line_df = pd.DataFrame(line_data)
# Fix the Imprint color order explicitly (Setosa always gets brand green,
# regardless of row/draw order) — see default-style-guide.md "First series
# is ALWAYS #009E73"
species_order = ["Setosa", "Versicolor", "Virginica"]
line_df["species"] = pd.Categorical(line_df["species"], categories=species_order, ordered=True)
# Setosa shows the sharpest separation on petal dimensions — rendered heavier
# and more opaque than the other species, and drawn last (on top), so it
# reads as the visual focal point.
focus_species = "Setosa"
line_df["line_size"] = (line_df["species"] == focus_species).map({True: 1.3, False: 0.65})
line_df["line_alpha"] = (line_df["species"] == focus_species).map({True: 0.9, False: 0.5})
line_df = pd.concat([line_df[line_df["species"] != focus_species], line_df[line_df["species"] == focus_species]])
# Create axis lines data (vertical lines at each x position)
axis_data = []
for i in range(len(dimensions)):
axis_data.append({"x": i, "y": 0, "xend": i, "yend": 1})
axis_df = pd.DataFrame(axis_data)
# Horizontal rules tying the axis tops and bottoms together into one frame
frame_df = pd.DataFrame(
{"x": [-0.3, -0.3], "y": [0, 1], "xend": [len(dimensions) - 0.7, len(dimensions) - 0.7], "yend": [0, 1]}
)
# Create label data for dimension names at the bottom
label_data = []
for i, label in enumerate(dim_labels):
label_data.append({"x": i, "y": -0.15, "label": label})
label_df = pd.DataFrame(label_data)
# Create tick labels for each axis (showing original scale) - only min and max
tick_data = []
for i, dim in enumerate(dimensions):
min_val = df[dim].min()
max_val = df[dim].max()
tick_data.append({"x": i - 0.08, "y": 0, "label": f"{min_val:.1f}"})
tick_data.append({"x": i - 0.08, "y": 1, "label": f"{max_val:.1f}"})
tick_df = pd.DataFrame(tick_data)
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
# Only horizontal gridlines — vertical gridlines would create a
# phantom-column look that competes with the 4 manually-drawn axis bars.
panel_grid_major_x=element_blank(),
panel_grid_major_y=element_line(color=INK_SOFT, size=0.2),
panel_grid_minor=element_blank(),
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
plot_title=element_text(color=INK, size=17),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT, size=9),
legend_title=element_text(color=INK, size=10),
)
# Plot
plot = (
ggplot()
# Vertical axis lines (theme-adaptive color)
+ geom_segment(aes(x="x", y="y", xend="xend", yend="yend"), data=axis_df, color=INK_SOFT, size=1)
# Subtle top/bottom rule tying all axes into one frame
+ geom_segment(aes(x="x", y="y", xend="xend", yend="yend"), data=frame_df, color=INK_SOFT, size=0.5, alpha=0.3)
# Data lines connecting observations across dimensions — Setosa rendered
# heavier/more opaque (line_size, line_alpha) as the storytelling focal point
+ geom_line(
aes(x="x", y="y", group="observation", color="species", size="line_size", alpha="line_alpha"), data=line_df
)
# Imprint palette — first series is brand green; size/alpha are literal
# values already computed above, not separate legend-worthy aesthetics
+ scale_color_manual(values=IMPRINT)
+ scale_size_identity()
+ scale_alpha_identity()
# Dimension labels at the bottom (theme-adaptive color, size matched to base ggsize(800,450))
+ geom_text(aes(x="x", y="y", label="label"), data=label_df, size=10, color=INK)
# Tick value labels on the left side of axes (theme-adaptive color)
+ geom_text(aes(x="x", y="y", label="label"), data=tick_df, size=8, color=INK_SOFT, hjust=1)
# Styling
+ scale_x_continuous(limits=(-0.4, len(dimensions) - 0.6))
+ scale_y_continuous(limits=(-0.32, 1.1))
+ labs(title="parallel-basic · python · letsplot · anyplot.ai", color="Species")
+ ggsize(800, 450)
+ anyplot_theme
)
# Save with theme-named output files
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Basic Parallel Coordinates Plot on anyplot.ai.